Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.Comment: To appear in the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, 2018. (IJCAI-ECAI 2018

Using Adherence-Contingent Rebates on Chronic Disease Treatment Costs to Promote Medication Adherence: Results from a Randomized Controlled Trial

Background: Poor adherence to medications is a global public health concern with substantial health and cost implications, especially for chronic conditions. In the USA, poor adherence is estimated to cause 125,000 deaths and cost $US100 Billion annually. The most successful adherence-promoting strategies that have been identified so far have moderate effect, are relatively costly, and raise availability, feasibility, and/or scalability issues. Objective: The main objective of SIGMA (Study on Incentives for Glaucoma Medication Adherence) was to measure the effectiveness on medication adherence of a novel incentive strategy based on behavioral economics that we refer to as adherence-contingent rebates. These rebates offered patients a near-term benefit while leveraging loss aversion and regret nd increasing the salience of adherence. Methods: IGMA is a 6-month randomized, controlled, open-label, single-center superiority trial with two parallel arms. total of 100 non-adherent glaucoma patients from the Singapore National Eye Centre were randomized into intervention (adherence-contingent rebates) and usual care (no rebates) arms in a 1:1 ratio. The primary outcome was the mean change from baseline in percentage of adherent days at Month 6. The trial registration number is NCT02271269 and a detailed study protocol has been published elsewhere. Findings: We found that participants who were offered adherence-contingent rebates were adherent to all their edications on 73.1$US5.94 as of 2 November 2017) per month during the intervention period. Conclusion: This study shows that simultaneously leveraging several insights from behavioral economics can significantly improve medication adherence rates. The relatively low cost of the rebates and significant health and cost implications of medication non-adherence suggest that this strategy has the potential to cost-effectively improve health outcomes for many conditions

Mobile game for the elderly: Bundled Bingo Game

The aim of this project is to develop a customized Bingo game based on the user interface guidelines that suit the elderly users. This project targeted to improve the cognitive ability of the elderly with the specifically designed gaming features. In this research, qualitative data will be used to analyze the confidence and acceptance level of the elderly on the mobile digital games, as well as to understand how the elderly interacts with current technologies (mobile devices) and the benefit they can gain from the mobile games. User testing, survey, and result analysis have been done with a group of elderly members from D’Happy club in Petaling Jaya, Malaysia. The results of this project contribute to the development of an elderly friendly mobile game to motivate the elderly’s engagement in mobile gaming, with the will that it could help to delay and decrease the risk of the elderly in developing the Alzheimer’s disease

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Loop Formulas for Description Logic Programs

Description Logic Programs (dl-programs) proposed by Eiter et al. constitute an elegant yet powerful formalism for the integration of answer set programming with description logics, for the Semantic Web. In this paper, we generalize the notions of completion and loop formulas of logic programs to description logic programs and show that the answer sets of a dl-program can be precisely captured by the models of its completion and loop formulas. Furthermore, we propose a new, alternative semantics for dl-programs, called the {\em canonical answer set semantics}, which is defined by the models of completion that satisfy what are called canonical loop formulas. A desirable property of canonical answer sets is that they are free of circular justifications. Some properties of canonical answer sets are also explored.Comment: 29 pages, 1 figures (in pdf), a short version appeared in ICLP'1