THE COVID-19 pandemic has brought the global society to a historic turning point. While the current pandemic is transforming our normal lives in an unprecedented way, the computing and information services ecosystem have provided the much-needed support to the continuity of the social interactions and the organizational functions. As COVID-19 continues to transform the global society, it is also exposing

Most modern organizations today support network infrastructure to provide ubiquitous network coverage at their premises. Such a network infrastructure consisting of a set of access points deployed at different locations in buildings can be used to support coarse-level localization of individuals, who connect to the infrastructure using their mobile devices. This paper describes a system, entitled Quest

In the last decade there has been a strong move towards mobile computing and the proliferation of the IoT (Internet of Things). A huge number of devices have been connected to the Internet and created zettabytes of data items. To extract value from such massive data volumes, processing power offered by cloud computing is often utilized. However, streaming data to the cloud exposes some limitations

We present the design and development of a data visualization service (RAMPVIS) in response to the urgent need to support epidemiological modeling workflows during the COVID-19 pandemic. Facing a set of demanding requirements and several practical challenges, our small team of volunteers had to rely on existing knowledge and components of services computing, while thinking on our feet in configuring

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients wearable or home devices)

With the rapid advancement in medical imaging techniques, Content-Based (medical) Image Retrieval (CBIR), which can assist in disease diagnosis, has gained much attention in both academia and industry. However, due to patients' sensitive information involved in medical images, privacy-preserving CBIR is a challenge worth exploiting. Though several privacy-preserving CBIR schemes have been put forth

With the increasing scale and complexity of cloud platforms and big-data analytics platforms, it is becoming more and more challenging to understand and diagnose the processing of a service request across multi-layer software stacks of such platforms. One way that helps to deal with this problem is to accurately capture the complete end-to-end execution path of service requests among all involved components

Attribute-based keyword search (ABKS) has been proposed to realize fine-grained access control and provide search service in cloud computing. However, most ABKS schemes focus on single or conjunctive keyword search, while the recent Boolean keyword search schemes only support monotonic query formula mainly involving AND, OR and threshold operators. How to support more expressive Boolean query formulas

The encrypted image retrieval technique allows users to retrieve images in an encrypted manner without decrypting images. However, most of the existing schemes still are vulnerable to security threats and inefficiency, caused by malicious users and inefficient feature extraction methods, respectively. To this end, we propose a traceable encrypted image retrieval in the multi-user setting in this paper

Currently, the world is experiencing the rapid spread of Coronavirus Disease 2019 (COVID-19). Since the epidemic continues to take a devastating impact on the society, economy, and healthcare, the real-time detection of COVID-19 is essential for fast and cost-effective diagnosis services. Fortunately, deep learning (DL), as a promising technology, enables the COVID-19 diagnosis services on chest X-ray

Given that cloud servers are usually remotely located from the devices of mobile apps, the end-users of the apps can face delays. The Fog has been introduced to augment the apps with machines located at the network edge close to the end-users. However, edge machines are usually resource constrained. Thus, the execution of online data-analytics on edge machines may not be feasible if the time complexity

With the rapid development of the Internet of Things (IoT) and communication technology, Deep Neural Network (DNN) applications like computer vision, can now be widely used in IoT devices. However, due to the insufficient memory, low computing capacity, and low battery capacity of IoT devices, it is difficult to support the high-efficiency DNN inference and meet users’ requirements for Quality of Service