Scalable factorization model to discover implicit and explicit similarities across domains

University of Technology Sydney. Faculty of Engineering and Information Technology.E-commerce businesses increasingly depend on recommendation systems to introduce personalized services and products to their target customers. Achieving accurate recommendations requires a sufficient understanding of user preferences and item characteristics. Given the current innovations on the Web, coupled datasets are abundantly available across domains. An analysis of these datasets can provide a broader knowledge to understand the underlying relationship between users and items. This thorough understanding results in more collaborative filtering power and leads to a higher recommendation accuracy. However, how to effectively use this knowledge for recommendation is still a challenging problem. In this research, we propose to exploit both explicit and implicit similarities extracted from latent factors across domains with matrix tri-factorization. On the coupled dimensions, common parts of the coupled factors across domains are shared among them. At the same time, their domain-specific parts are preserved. We show that such a configuration of both common and domain-specific parts benefits cross-domain recommendations significantly. Moreover, on the non-coupled dimensions, the middle factor of the tri-factorization is proposed to use to match the closely related clusters across datasets and align the matched ones to transfer cross-domain implicit similarities, further improving the recommendation. Furthermore, when dealing with data coupled from different sources, the scalability of the analytical method is another significant concern. We design a distributed factorization model that can scale up as the observed data across domains increases. Our data parallelism, based on Apache Spark, enables the model to have the smallest communication cost. Also, the model is equipped with an optimized solver that converges faster. We demonstrate that these key features stabilize our model’s performance when the data grows. Validated on real-world datasets, our developed model outperforms the existing algorithms regarding recommendation accuracy and scalability. These empirical results illustrate the potential of our research in exploiting both explicit and implicit similarities across domains for improving recommendation performance

A long range, energy efficient internet of things based drought monitoring system

The climate change and global warning have been appeared as an emerging issue in recent decades. In which, the drought problem has been influenced on economics and life condition in Vietnam. In order to solve this problem, in this paper, we have designed and deployed a long range and energy efficient drought monitoring based on IoT (Internet of Things) for real time applications. After being tested in the real condition, the proposed system has proved its high dependability and effectiveness. The system is promising to become a potential candidate to solve the drought problem in Vietnam

DEVELOPMENT OF ELECTROACTIVE POLYMETHYLTHIOPHENE BASED DOPAMINE SENSOR

Joint Research on Environmental Science and Technology for the Eart

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

AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?

Can we better anticipate an actor's future actions (e.g. mix eggs) by knowing what commonly happens after his/her current action (e.g. crack eggs)? What if we also know the longer-term goal of the actor (e.g. making egg fried rice)? The long-term action anticipation (LTA) task aims to predict an actor's future behavior from video observations in the form of verb and noun sequences, and it is crucial for human-machine interaction. We propose to formulate the LTA task from two perspectives: a bottom-up approach that predicts the next actions autoregressively by modeling temporal dynamics; and a top-down approach that infers the goal of the actor and plans the needed procedure to accomplish the goal. We hypothesize that large language models (LLMs), which have been pretrained on procedure text data (e.g. recipes, how-tos), have the potential to help LTA from both perspectives. It can help provide the prior knowledge on the possible next actions, and infer the goal given the observed part of a procedure, respectively. To leverage the LLMs, we propose a two-stage framework, AntGPT. It first recognizes the actions already performed in the observed videos and then asks an LLM to predict the future actions via conditioned generation, or to infer the goal and plan the whole procedure by chain-of-thought prompting. Empirical results on the Ego4D LTA v1 and v2 benchmarks, EPIC-Kitchens-55, as well as EGTEA GAZE+ demonstrate the effectiveness of our proposed approach. AntGPT achieves state-of-the-art performance on all above benchmarks, and can successfully infer the goal and thus perform goal-conditioned "counterfactual" prediction via qualitative analysis. Code and model will be released at https://brown-palm.github.io/AntGP

The Status of Educational Sciences In Vietnam: A Bibliometric Analysis From Clarivate Web Of Science Database Between 1991 And 2018

Since 2013, Vietnam has implemented a plan to reform the whole education sector. However, there is little understanding on the status of educational research in Vietnam, which may lay the foundation for such plan. Thus, this research aims to analyze the whole picture of educational research from Vietnam, as seen from the Clarivate Web of Science (WOS) database: 215 publications were recorded, ranging from 1991 to 2018. These 215 publications were further analyzed from five perspectives: 1) number of publications by year; 2) research fields and levels of education; 3) top institutions with the highest number of publications; 4) international collaboration; and 5) quality. Some of the most notable results are: 1) the educational sciences in Vietnam have been still under-developed until recently; 2) among different research topics research among educational sciences, some (e.g., Vocational Education and Training or Early Childhood Education) seemed to be overlooked whereas others (e.g., Higher Education and Teaching and Learning) seemed to receive more attention from educational scholars; 3) all the most major education – specialized universities did not appear among the top five institutions with highest number of publications; 4) Australia, Thailand, the USA, New Zealand and China were the countries with the highest number of co-publications with Vietnamese researchers; and 5) The majority of publications belonged to low-ranked journals. Implications would be withdrawn for Vietnamese policymakers, education leaders, educational researchers and teachers in order to adjust their policies and/or action plans; thus, enhancing the performance and impacts of educational research in the future

Changes in the levels of immunological markers after treatment in patients with allergic rhinitis

Introduction: Monitoring changes in the levels of immune markers is of great significance in evaluating the effectiveness of treatment in patients with allergic rhinitis. Objectives: Determine the change in the concentration of immune markers after treatment in patients with allergic rhinitis caused by cotton dust. Methods : A descriptive, single-group, comparative before and after intervention study on 52 patients with allergic rhinitis caused by cotton dust. Comparison of immunological markers results before and after 36 months of treatment. Results: Total IgE concentration after treatment decreased, the median decreased from 1227.756 U/mL to 676.805 UI/mL. Serum levels of IgG, IgG4, and IgG1 in patients after treatment increased compared to before (p< 0.001). The cytokines also changed in the direction of no longer responding toward allergy. Median IL-17 decreased from 1.752 mg/dL to 0.417 mg/dL. Conclusion: In patients with allergic rhinitis after specific sublingual desensitization treatment, IgE levels and cytokines such as IL-6 and IL-17 are significantly reduced and IgG, IgG4 and IgG1 levels are increased after treatment

On-chip ZnO nanofibers prepared by electrospinning method for NO2 gas detection

In the present study, on-chip ZnO nanofibers were fabricated by means of the electrospinning technique followed by a calcination process at 600 oC towards the gas sensor application. The morphology, composition, and crystalline structure of the as-spun and annealed ZnO nanofibers were investigated by field emission scanning electron microscopy (FESEM), energy dispersive X-ray (EDX), and X-ray diffraction (XRD), respectively. The findings show that spider-net like ZnO nanofibers with a diameter of 60 – 100 nm were successfully synthesized without any incorporation of impurities into the nanofibers. The FESEM images also reveal that each nanofiber is composed of many nanograins. The combination of experimental and calculated X-ray diffraction data indicate that ZnO nanofibers were crystallized in hexagonal wurtzite structure. For the gas sensing device application, the ZnO nanofibers-based sensors were tested with the nitrogen dioxide gas in the temperature range of 200 oC to 350 oC and concentrations from 2.5 ppm to 10 ppm. The sensing property results indicate that at the optimal working temperature of 300 oC, the ZnO nanofibers-based sensors exhibited a maximum response of 30 and 166 times on exposure of 2.5 and 10 ppm NO2 gas, respectively. The presence of nanograins within nanofibers, which results in further intensification of the resistance modulation, is responsible for such high gas response