Dynamic Characterization of Web Application Interfaces

Web applications are increasingly prominent in society, serving a wide variety of user needs. Engineers seeking to enhance, test, and maintain these applications and third-party programmers wishing to utilize these applications need to understand their interfaces. In this paper, therefore, we present methodologies for characterizing the interfaces of web applications through a form of dynamic analysis, in which directed requests are sent to the application, and responses are analyzed to draw inferences about its interface. We also provide mechanisms to increase the scalability of the approach. Finally, we evaluate the approach’s performance on six non-trivial web applications

Image Processing Algorithms for Detection of Anomalies in Orthopedic Surgery Implants

Orthopedic implant procedures for hip implants are performed on 300,000 patients annually in the United States, with 22.3 million procedures worldwide. While most such operations are successfully performed to relieve pain and restore joint function for the duration of the patient\u27s life, advances in medicine have enabled patients to outlive the life of their implant, increasing the likelihood of implant failure. There is significant advantage to the patient, the surgeon, and the medical community in early detection of implant failures.The research work presented in this thesis demonstrates a non-invasive digital image processing technique for the automated detection of specific arthroplasty failures before requiring revision surgery. This thesis studies hip implant loosening as the primary cause of failure. A combination of digital image segmentation, representation and numerical description is employed and validated on 2-D X-ray images of hip implant phantoms to detect 3-D rotations of the implant, with the support of radial basis function neural networks to accomplish this task. A successful clinical implementation of the methods developed in this thesis can eliminate the need for revision surgery and prolong the life of the orthopedic implant

On the impossibility of effectively using likely-invariants for software attestation purposes

Invariants monitoring is a software attestation technique that aims at proving the integrity of a running application by checking likely-invariants, which are statistically significant predicates inferred on variables’ values. Being very promising, according to the software protection literature, we developed a technique to remotely monitor invariants. This paper presents the analysis we have performed to assess the effectiveness of our technique and the effectiveness of likely-invariants for software attestation purposes. Moreover, it illustrates the identified limitations and our studies to improve the detection abilities of this technique. Our results suggest that, despite further studies and future results may increase the efficacy and reduce the side effects, software attestation based on likely-invariants is not yet ready for the real world. Software developers should be warned of these limitations, if they could be tempted by adopting this technique, and companies developing software protections should not invest in development without also investing in further research

Snowmass Theory Frontier Report

This report summarizes the recent progress and promising future directions in theoretical high-energy physics (HEP) identified within the Theory Frontier of the 2021 Snowmass Process.Comment: Contribution to the US Community Study on the Future of Particle Physics (Snowmass 2021), v2: fixed typo

Designing Trustworthy Autonomous Systems

The design of autonomous systems is challenging and ensuring their trustworthiness can have different meanings, such as i) ensuring consistency and completeness of the requirements by a correct elicitation and formalization process; ii) ensuring that requirements are correctly mapped to system implementations so that any system behaviors never violate its requirements; iii) maximizing the reuse of available components and subsystems in order to cope with the design complexity; and iv) ensuring correct coordination of the system with its environment.Several techniques have been proposed over the years to cope with specific problems. However, a holistic design framework that, leveraging on existing tools and methodologies, practically helps the analysis and design of autonomous systems is still missing. This thesis explores the problem of building trustworthy autonomous systems from different angles. We have analyzed how current approaches of formal verification can provide assurances: 1) to the requirement corpora itself by formalizing requirements with assume/guarantee contracts to detect incompleteness and conflicts; 2) to the reward function used to then train the system so that the requirements do not get misinterpreted; 3) to the execution of the system by run-time monitoring and enforcing certain invariants; 4) to the coordination of the system with other external entities in a system of system scenario and 5) to system behaviors by automatically synthesize a policy which is correct

Report of the 2021 U.S. Community Study on the Future of Particle Physics (Snowmass 2021) Summary Chapter

The 2021-22 High-Energy Physics Community Planning Exercise (a.k.a. ``Snowmass 2021'') was organized by the Division of Particles and Fields of the American Physical Society. Snowmass 2021 was a scientific study that provided an opportunity for the entire U.S. particle physics community, along with its international partners, to identify the most important scientific questions in High Energy Physics for the following decade, with an eye to the decade after that, and the experiments, facilities, infrastructure, and R&D needed to pursue them. This Snowmass summary report synthesizes the lessons learned and the main conclusions of the Community Planning Exercise as a whole and presents a community-informed synopsis of U.S. particle physics at the beginning of 2023. This document, along with the Snowmass reports from the various subfields, will provide input to the 2023 Particle Physics Project Prioritization Panel (P5) subpanel of the U.S. High-Energy Physics Advisory Panel (HEPAP), and will help to guide and inform the activity of the U.S. particle physics community during the next decade and beyond.Comment: 75 pages, 3 figures, 2 tables. This is the first chapter and summary of the full report of the Snowmass 2021 Workshop. This version fixes an important omission from Table 2, adds two references that were not available at the time of the original version, fixes a minor few typos, and adds a small amount of material to section 1.1.

An operator induction tool supporting knowledge engineering in planning

Within the field 'artificial intelligence' are many disciplines, one of which is planning. Planning seeks to find a suitable sequence of actions to carry out a task specified as a set of initial states for the objects involved in the actions and a required goal state. To do this the system has to have enough knowledge about the 'world' in the form of a planning domain model The process of constructing a planning domain model requires knowledge engineering. The structuring of the knowledge is important and hand-coding a domain model is a tedious and error-prone process. Static knowledge in the domain requires little update but the same cannot be said for the dynamic knowledge.EThOS - Electronic Theses Online ServiceGBUnited Kingdo