Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains a global challenge due to its complex pathology and the lack of definitive diagnostic tools. This paper introduces an innovative approach to predicting and analyzing Alzheimer's disease by constructing signed brain network models and leveraging signed graph neural network technologies. By modeling the brain network as a signed graph that incorporates both positive and negative correlations, we capture the nuanced interactions between brain regions more effectively than traditional methods. We utilize graph convolutional networks (GCNs) and their variants to process these signed brain networks, significantly improving the accuracy of Alzheimer's disease prediction. Comparative analysis reveals that the signed graph model outperforms its unsigned counterparts in diagnostic precision (with an improvement of at least 19%), emphasizing the importance of incorporating negative correlations in neural interactions. Furthermore, precisely because of the additional negative edge information that we can utilize both positive and negative attention matrices, derived from these prediction tasks, to determine important brain region biomarkers. This work is an attempt to systematically validate the role of negative information through comparisons of different signed graph variants, which holds particular promise for enhancing Alzheimer's disease diagnostic accuracy at early stages. We believe that this approach will have significant clinical applications in the future.

Fusion has been widely used in machine learning community, especially for problems dealing with multiple input sources and classifiers. The general strategy for information fusion in deep neural network is to directly concatenate the embedding features on the latent space of input sources. However, it is very hard to capture the relative importance of fused sources. It is also impossible to learn the correlation among fused multimodalities inputs, e.g., intra-class and inter-class similarities. Besides, most existing deep learning fusion approaches use universal fusion weights strategy, which cannot fully exploit the relative importance of different inputs. In order to address these problems, in this work we propose an Adaptive Weighted Deep Fusion scheme (AWDF) to capture potential relationships among various input sources. It integrates the feature level and decision level fusion in one framework. Furthermore, in order to address the limitations of existing fusing models with fixed weights, we propose a new scheme named Cross Decision Weights Method (CDWM). It can dynamically learn the weight for each input branch during the fusion process instead of utilizing pre-defined weights. To evaluate the performance of AWDF, we conduct experiments on three different real-world datasets: Wild Business Terms (WBT) Dataset, Iceberg Detection Dataset and CareerCon Dataset. Our experimental results demonstrate the superiority of AWDF over other fusion approaches.

Obesity, a serious public health concern worldwide, increases the risk of many diseases, including hypertension, stroke, and type 2 diabetes. To tackle this problem, researchers collect diverse types of data, which includes biomedical, behavioral and activity, and utilize machine learning techniques to mine hidden patterns for obesity status improvement prediction. While existing machine learning methods such as Recurrent Neural Networks (RNNs) provide exceptional results, it is challenging to discover hidden patterns of the sequential data due to the irregular observation time instances. Meanwhile, the lack of understanding of why those learning models are effective also limits further improvements on their architectures. Thus, we develop a RNN based time-aware architecture to handle irregular observation times and identify relevant feature extractions from longitudinal patient records for obesity status improvement pre-diction. Evaluations of real-world data involving activity data collected from wearables and electronic health records demonstrate that our proposed method can capture the underlying structures in users' time sequences with irregularities, and achieve an accuracy of 77% in predicting the obesity status improvement.

Until today, finding the most suitable APIs to use in an application was burdensome, requiring manual and time-consuming searches across a diverse set of websites, in particular regarding how multiple APIs could be combined and worked together (i.e. API mashups). In this paper, we propose a new method to automatically generate API mashups through real-world data collection, text mining and natural language processing (NLP ) techniques. The generated API mashups are further ranked and recommended to developers based on a quantitative indicator of whether the given API mashup is plausible. To evaluate the overall accuracy of the proposed method, we use the generated API mashups to train several machine learning and deep learning models, and then use an independent mashup dataset collected from Github projects for testing. The experimental results show that our proposed method is feasible and accurate for automatic API mashup generation and recommendation.

Image retrieval has become an important function in many emerging computer vision applications e.g. online shopping via images, medical health care systems. More and more images are being generated and stored in public clouds. However, recent photo leakage events raise concerns about privacy leaks for images stored in public clouds. In this paper, we present an efficient scalable hierarchical image retrieval system (CASHEIRS) which provides privacy-aware image retrieval feature. CASHEIRS employs transformed Convolutional Neural Network features to improve image retrieval accuracy and an encrypted hierarchical index tree to speed up the query process. Extensive evaluations using Caltech256 and INRIA Holiday datasets show that CASHEIRS is more effective than three existing schemes. We also demonstrate its practicality on a mobile device.

Affordable cloud computing technologies allow users to efficiently store, and manage their Personal Health Records (PHRs) and share with their caregivers or physicians. This in turn improves the quality of healthcare services, and lower health care cost. However, serious security and privacy concerns emerge because people upload their personal information and PHRs to the public cloud. Data encryption provides privacy protection of medical information but it is challenging to utilize encrypted data. In this paper, we present a privacy-preserving disease treatment, complication prediction scheme (PDTCPS), which allows authorized users to conduct searches for disease diagnosis, personalized treatments, and prediction of potential complications. $PDTCPS$ uses a tree-based structure to boost search efficiency, a wildcard approach to support fuzzy keyword search, and a Bloom-filter to improve search accuracy and storage efficiency. In addition, our design also allows health care providers and the public cloud to collectively generate aggregated training models for disease diagnosis, personalized treatments and complications prediction. Moreover, our design provides query unlinkability and hides both search & access patterns. Finally, our evaluation results using two UCI datasets show that our scheme is more efficient and accurate than two existing schemes.

Many organizations and individual users are out-sourcing their information which includes sensitive data into the cloud. To deal with the potential risks of privacy exposure, such data is typically encrypted before being outsourced but users would like to conduct keyword-based searches. Traditional searchable encryption techniques are overly restrictive for they only allow exact keyword search. Thus, fuzzy keyword search is needed to deal with typos in users' search strings. In this paper, we present a Cuckoo Filter Based Private Keyword Search Scheme (CFPKS) to provide privacy-aware keyword search over encrypted data. This CFPKS scheme uses a bed-tree structure-based index to boost search efficiency, a wildcard approach to support fuzzy keyword search, and a Cuckoo-filter to improve search accuracy and storage efficiency. Our scheme handles both typos and query unlinkability. Using a large ACM publication dataset, the evaluation results comparing the search efficiency and accuracy of our proposed CFPKS scheme with three existing schemes show that our scheme achieves higher search accuracy with lower search cost.