On hole approximation algorithms in wireless sensor networks

Routing holes in sensor network are regions without operating nodes. They may occur due to several reasons, including cases caused by natural obstacles or disaster suffering areas. Determining the location and shape of holes can help monitor these disaster events (such as volcano, tsunami, etc.) or make smart, early routing decisions for circumventing a hole. However, given the energy limit of sensor nets, the determination and dissemination of the information about the exact shape of a large hole could be unreasonable. Therefore, there are some techniques to approximate a hole by a simpler shape. In this paper, the authors analyze and compare two existing approximation approaches that are considered as the most suitable for the sensor network, namely the grid-based and the convex-hull-based approaches. And a new algorithm of the grid-based approach is also introduced. The performances of all the mentioned algorithms are under analysis and evaluation in both theoretical and experimental perspectives. The findings show that grid-based approach has advantages in saving network energy and providing a finer image of the hole while the convex hull approach is better for making a shorter hole-bypassing the route but not much

Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks

AbstractA quest for geographic routing schemes of wireless sensor networks when sensor nodes are deployed in areas with obstacles has resulted in numerous ingenious proposals and techniques. However, there is a lack of solutions for complicated cases wherein the source or the sink nodes are located close to a specific hole, especially in cavern-like regions of large complex-shaped holes. In this paper, we propose a geographic routing scheme to deal with the existence of complicated-shape holes in an effective manner. Our proposed routing scheme achieves routes around holes with the (1+$$\epsilon$$ ϵ )-stretch. Experimental results show that our routing scheme yields the highest load balancing and the most extended network lifetime compared to other well-known routing algorithms as well

The Impact of TikTok UGC Videos on Online Purchase Intention: Mediating Role of Cognitive States

Background: User-Generated Content (UGC) videos have received considerable attention in recent years thanks to their great potential for buyers and sellers. However, the effect of this content on consumer behavior remains unclear, especially in the context of developing countries (e.g., Vietnam). By applying the Stimulus-Organism-Response model (S-O-R model), this paper examines the effect of UGC videos on online purchase intention. Moreover, the mediating role of cognitive responses, consisting of perceived credibility, perceived diagnosticity, and mental imagery, is also examined to offer valuable insights to businesses, enabling them to leverage and effectively promote the trends of UGC videos. Method: A convenience sampling method was employed to collect the data. A total of 318 valid respondents participated in this survey. The data was analyzed with the Partial Least Squares Structural Equation Modeling method (PLS-SEM). Results: The findings show that UGC videos have a direct impact on online purchase intention. This paper also verified that cognitive states mediate the relationship between stimuli and subsequent behavioral intentions. Conclusion: Our research findings enrich the literature on consumer’s online purchase intentions by applying the S-O-R framework by highlighting the role of cognitive responses, and improving generalizability by contributing additional consumer behavior findings in developing Asian nations. Moreover, this paper offers businesses insights into the formation of customers\u27 purchase intentions while watching UGC videos. Based on that, this paper raises practical recommendations regarding promoting the UGC video trend and creating UGC videos effectively to improve the cognitive states perceived by customers, including credibility, diagnostic value, and mental imagery

A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

Medication mistaking is one of the risks that can result in unpredictable consequences for patients. To mitigate this risk, we develop an automatic system that correctly identifies pill-prescription from mobile images. Specifically, we define a so-called pill-prescription matching task, which attempts to match the images of the pills taken with the pills' names in the prescription. We then propose PIMA, a novel approach using Graph Neural Network (GNN) and contrastive learning to address the targeted problem. In particular, GNN is used to learn the spatial correlation between the text boxes in the prescription and thereby highlight the text boxes carrying the pill names. In addition, contrastive learning is employed to facilitate the modeling of cross-modal similarity between textual representations of pill names and visual representations of pill images. We conducted extensive experiments and demonstrated that PIMA outperforms baseline models on a real-world dataset of pill and prescription images that we constructed. Specifically, PIMA improves the accuracy from 19.09% to 46.95% compared to other baselines. We believe our work can open up new opportunities to build new clinical applications and improve medication safety and patient care.Comment: Accepted for publication and presentation at the 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2022

CHANGES IN PLASMA LEVELS OF STEROID HORMONES DURING SEXUAL MATURATION OF MALE HELICOPTER CATFISH (WALLAGO ATTU) IN CAPTIVITY

In order to provide reliable indicators of the spawning season of captive helicopter catfish (Wallago attu), this study evaluated the temporal variation in gonadosomatic index (GSI), plasma levels of testosterone (T), and 11-ketotestosterone (11-KT) in male broodstock in captivity. GSI was estimated as the percentage of the relative weight of testis to total body weight. Plasma levels of sex steroids were determined by enzyme-linked immunosorbent assay (EIA). Testis samples were dehydrated and embedded in paraffin, then sectioned at 5 μm thickness. The highest level of T (402.1 ± 16.7 pg/mL) was found in June, followed by a peak in 11-KT level (76.9 ± 4.7 pg/mL) in May. Testes containing the highest concentrations of spermatozoa were observed from June to August. The GSI of males increased significantly from January to June and peaked in July (2.14%). Taken together, we conclude that the spawning season of captive helicopter catfish occurs from June to August. These results will contribute to the basic knowledge of the reproductive biology of helicopter catfish, which can be useful in artificial breeding

Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata

This work discusses the use of contrastive learning and deep learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals. While the ECG signals usually contain 12 leads (channels), many healthcare facilities and devices lack access to all these 12 leads. This raises the problem of how to use only fewer ECG leads to produce meaningful diagnoses with high performance. We introduce a simple experiment to test whether contrastive learning can be applied to this task. More specifically, we added the similarity between the embedding vectors when the 12 leads signal and the fewer leads ECG signal to the loss function to bring these representations closer together. Despite its simplicity, this has been shown to have improved the performance of diagnosing with all lead combinations, proving the potential of contrastive learning on this task.Comment: Accepted for presentation at the Midwest Machine Learning Symposium (MMLS 2023), Chicago, IL, US

FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Co-Training

We introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL approaches, which require each client to train the full-size neural network independently during each training round, the proposed FedDCT allows a cluster of several clients to collaboratively train a large deep learning model by dividing it into an ensemble of several small sub-models and train them on multiple devices in parallel while maintaining privacy. In this co-training process, clients from the same cluster can also learn from each other, further improving their ensemble performance. In the aggregation stage, the server takes a weighted average of all the ensemble models trained by all the clusters. FedDCT reduces the memory requirements and allows low-end devices to participate in FL. We empirically conduct extensive experiments on standardized datasets, including CIFAR-10, CIFAR-100, and two real-world medical datasets HAM10000 and VAIPE. Experimental results show that FedDCT outperforms a set of current SOTA FL methods with interesting convergence behaviors. Furthermore, compared to other existing approaches, FedDCT achieves higher accuracy and substantially reduces the number of communication rounds (with $4-8$ times fewer memory requirements) to achieve the desired accuracy on the testing dataset without incurring any extra training cost on the server side.Comment: Under review by the IEEE Transactions on Network and Service Managemen

Anthraquinones from Hedyotis pinifolia

Hedyotis pinifolia Wall ex G.Don (Vietnamsese name An điền lá thông), family of Rubiaceae, has not yet been chemically studied. From the aerial parts of H. pinifolia, four anthraquinones had been isolated: 1,6-dihydroxy-7-methoxy-2-methylanthraquinone (1), 1,6-dihydroxy-2-methylanthraquinone (2), 3,6-dihydroxy-2-methylanthraquinon (3) and 1,3,6-trihydroxy-2-methylanthraquinone (4). Their chemical structures were established by spectroscopic analysis.

FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.Comment: Accepted for presentation at the 51st International Conference on Parallel Processin

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{internal dynamics}, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss. In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at \url{https://github.com/thanhtrunghuynh93/estimate}.Comment: Technical report for accepted paper at WSDM 202