On The Assessment of Communities of Web Services

The notion of community of web services has been recently proposed and investigated to gather functionally similar web services in the same virtual space. This allows increasing the visibility of web services and their collaboration, which makes their discovery and composition easier. Using the community infrastructure, users are supposed to direct their requests to the community's manager (called master), that is in charge of selecting the appropriate web service. Because many communities providing the same functionality are available, selecting the best community to deal with, from the users and providers perspectives, is a key factor that still needs to be investigated. Another particularly challenging issue yet to be addressed is the selection by the master of the appropriate web service to be hosted in the community. Reputation has been proposed as a means to help users, providers, and masters evaluate and rank different candidates. However, reputation is mainly based on users feedback, which is not always accurate. Moreover, other performance parameters should be considered in the selection game. In this thesis, we propose a new assessment process that focuses on various performance aspects of the community rather than just its reputation. This assessment considers the performance parameters from the users, providers, and masters perspectives. In this approach, the communities performance rate is mainly based on the web services hosted by those communities. Such an assessment approach helps the master of the community differentiate between web services so that only the appropriate ones can be invited or accepted to join based on the communities requirements. It also helps the users and providers select the best available communities. The proposed method works on three steps. The first step focuses on defining and iv computing the evaluation metrics used in the assessment process while considering the requirements of all the stakeholders, namely users, providers, and communities. Thus, each community or web service is described by a vector of metrics. The second step includes the clustering of the evaluated communities and web services using the resulted vectors from the first step. During the third step, the resulting clusters are ranked using a function called goodness function. Web services and communities belonging to the best cluster are then selected. The effectiveness of the proposed assessment approach is tested by simulation and comparison to two other approaches in the literature

On The Security of Wide Area Measurement System and Phasor Data Collection

Smart grid is a typical cyber-physical system that presents the dependence of power system operations on cyber infrastructure for control, monitoring, and protection purposes. The rapid deployment of phasor measurements in smart grid transmission system has opened opportunities to utilize new applications and enhance the grid operations. Thus, the smart grid has become more dependent on communication and information technologies such as Wide Area Measurement Systems (WAMS). WAMS are used to collect real-time measurements from different sensors such as Phasor Measurement Units (PMUs) installed across widely dispersed areas. Such system will improve real-time monitoring and control; however, recent studies have pointed out that the use of WAMS introduces significant vulnerabilities to cyber-attacks that can be leveraged by attackers. Therefore, preventing or reducing the damage of cyber attacks onWAMS is critical to the security of the smart grid. In this thesis, we focus our attention on the relation between WAMS security and the IP routing protocol, which is an essential aspect to the collection of sensors measurements. Synchrophasor measurements from different PMUs are transferred through a data network and collected at one or multiple data concentrators. The timely collection of phasors from PMU dispersed across the grid allows to maintain system observability and take corrective actions when needed. This collection is made possible through Phasor Data Concentrators (PDCs) that time-align and aggregate phasor measurements, and forward the resulting stream to be used by monitoring and control applications. WAMS applications relying on these measurements have strict and stringent delay requirements, e.g., end-to-end delay as well as delay variation between measurements from different PMUs. Measurements arriving past a predetermined time period at a data concentrator will be dropped, causing incompleteness of data and affecting WAMS applications and hence the system’s operations. It has been shown that non-functional properties, such as data delay and packet drops, have a negative impact on the system functionality. We show that simply forwarding measurements from PMUs through shortest routes to phasor data collectors may result in data being dropped at their destinations. We believe therefore that there is a strong interplay between the routing paths (delays along the paths) for gathering the measurements and the value of timeout period. This is particularly troubling when a malicious attacker deliberately causes delays on some communication links along the shortest routes. Therefore, we present a mathematical model for constructing forwarding trees for PMUs’ measurements which satisfy the end to end delay as well as the delay variation requirements of WAMS applications at data concentrators. We show that a simple shortest path routing will result in larger fraction of data drop and that our method will find a suitable solution. Then, we study the relation between cyber-attack propagation and IP multicast routing. To this extent, we formulate the problem as the construction of a multicast tree that minimizes the propagation of cyber-attacks while satisfying real-time and capacity requirements. The proposed attack propagation multicast tree is evaluated using different IEEE test systems. Finally, cyber-attacks resulting in the disconnection of PDC(s) from WAMS initiate a loss of its phasor stream and incompleteness in the observability of the power system. Recovery strategies based on the re-routing of lost phasors to other connected and available PDCs need to be designed while considering the functional requirements of WAMS. We formulate a recovery strategy from loss of compromised or failed PDC(s) in the WAMS network based on the rerouting of disconnected PMUs to functional PDCs. The proposed approach is mathematically formulated as a linear program and tested on standard IEEE test systems. These problems will be extensively studied throughout this thesis

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

Artificial intelligence (AI) continues to transform data analysis in many domains. Progress in each domain is driven by a growing body of annotated data, increased computational resources, and technological innovations. In medicine, the sensitivity of the data, the complexity of the tasks, the potentially high stakes, and a requirement of accountability give rise to a particular set of challenges. In this review, we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making. (1) Explainable AI aims to produce a human-interpretable justification for each output. Such models increase confidence if the results appear plausible and match the clinicians expectations. However, the absence of a plausible explanation does not imply an inaccurate model. Especially in highly non-linear, complex models that are tuned to maximize accuracy, such interpretable representations only reflect a small portion of the justification. (2) Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains. For example, a classification task based on images acquired on different acquisition hardware. (3) Federated learning enables learning large-scale models without exposing sensitive personal health information. Unlike centralized AI learning, where the centralized learning machine has access to the entire training data, the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates, not personal health data. This narrative review covers the basic concepts, highlights relevant corner-stone and state-of-the-art research in the field, and discusses perspectives.Comment: This paper is accepted in IEEE CAA Journal of Automatica Sinica, Nov. 10 202