Italian Film Series: "The Bandit of Tacca del Lupo" [Il brigante di Tacca del Lupo] (1952) Date: Wednesday, December 2, 2020 Time: 7:00 PM Place: Virtual (Zoom) Description: Join us for our 6th Annual Italian Film Series at UMass Dartmouth, co-sponsored by the Foreign Literature and Languages Department and the History Department. This year commemorates the 200th anniversary of the birth of Vittorio Emanuele II and the 150th anniversary of the capture of Rome! This fall, we will be taking a look at the Unification (Risorgimento) period in Italy. These films reflect on the experience of 19th century Italy and the Risorgimento (Unification). For our third and final film, we will show "The Bandit of Tacca del Lupo" [Il brigante di Tacca del Lupo] (1952) is set "[i]n 19th century southern Italy (near Melfi, Basilicata), [where] a small force of soldiers fight in the hills against the bandits who are holding their country to ransom" (IMDB). All films have English subtitles and will be screened via Zoom. To join our mailing list and/or receive the Zoom link, please contact: Prof. Rose Facchini - email@example.com Prof. Matthew Sneider - firstname.lastname@example.org
This information session is for any prospective students interested in graduate business programs at UMass Dartmouth. This event will include information on the following programs: - MBA - MS Accounting - MS Finance - MS Healthcare Management - MS Technology Management This is a virtual zoom event designed to answer questions about the various degree and certificate programs.
The Accounting and Finance Department announces the following research seminar. Speaker: Associate Professor. Jeff Chen (Texas Christian University) Title: Non-GAAP Earnings and Auditors' Going Concern Opinions Date: Friday, December 4, 2020 Time: 10:30 AM Location: via Zoom Meeting https://umassd.zoom.us/j/4387551509?pwd=SmZyUGZCak1ZSUpOUmFaclc3OFhyZz09 Meeting ID: 438 755 1509 Passcode: 20200211 Abstract: We examine whether auditors' going concern opinions reflect an assessment of non-GAAP earnings, focusing on firms with a GAAP loss but a non-GAAP profit (i.e., a non-GAAP switch) as a predictor of future loss reversals. Using management and analyst non-GAAP earnings to proxy for auditors' assessments of core performance, we find that auditors are significantly less likely to issue a going concern opinion when there is a non-GAAP switch. This finding is attributable to analyst non-GAAP switches while there is no association for non-GAAP switches indicated solely by management. Further, we find that the weight that auditors place on non-GAAP switches in their going concern assessments is consistent with the weight implied by a bankruptcy prediction model, and that non-GAAP switches are associated with lower Type II errors by the auditor. Overall, our evidence suggests that auditors look beyond the GAAP financial statements to assess core profitability when making going concern determinations. For additional information, please contact Prof. Hongkang Xu at email@example.com.
Topic: Probabilistic Reliability and Security Risk Assessment Zoom Teleconference: https://umassd.zoom.us/j/94834229550 Abstract: With advances and globalization of information technology such as big data and cloud computing, topics about potential risks with security vulnerabilities have been brought to the forefront. Considerable efforts have been made to estimate security risks with an unlimited cycle of disclosed vulnerabilities in the form of threats or attacks and management strategies to mitigate these risks. On the other hand, reliability is often considered as one of the most vital factors that affect functioning of critical computing systems. Existing works on risk analysis have mostly focused on either security or reliability, but not both. In addition, the existing approaches for quantifying risks are mostly based on simple multiplications of frequencies and quantitative consequences of hazard occurrence without considering dependencies among the hazards. In this dissertation research, an integrated framework is explored for simultaneously and systematically modeling and quantifying both reliability and security risks of modern technological systems. Under the framework, we advance the state of the art in quantitative security risk assessment by modeling sequential cyber-attacks, where multiple sequence-dependent hazardous actions are performed to launch a successful attack. Continuous-time Markov chain (CTMC) and semi-Markov process (SMP) based methods are proposed to estimate the occurrence probability of a security risk for systems undergoing the sequential cyber-attack. While the CTMC-based method is limited to the exponentially distributed state transition time, the proposed SMP-based approach is applicable to analyzing attacks with arbitrary types of transition time distributions. Both methods are illustrated using case studies where Trojan attacks in the banking application are modeled and analyzed. In this dissertation research, we make another contribution by modeling and analyzing survivability and vulnerability of a cloud RAID (Redundant Array of Independent Disks) storage system subject to disk faults and cyber-attacks. The cloud RAID survivability is concerned with the system's ability to function correctly even under the circumstance of hazardous behaviors including disk failures and malicious attacks. The cloud RAID invulnerability is concerned with the system's ability to function correctly while occupying a certain state immune to malicious attacks. A CTMC-based method is suggested to perform the time-dependent disk level survivability and invulnerability analysis and an SMP-based approach is implemented to analyze the steady-state disk survivability and invulnerability. Combinatorial methods are suggested for the cloud RAID system level analysis, which can accommodate both homogeneous (based on combinatorics) and heterogeneous (based on multi-valued decision diagrams) disks. A detailed case study on a cloud RAID 5 system is conducted to illustrate the application of the proposed methods. Impacts of parameters modeling different attack, recovery and rescue behaviors on the disk and system survivability and invulnerability are also investigated. Note: All ECE Graduate Students are ENCOURAGED to join the zoom teleconference. All interested parties are invited to join. Advisor: Dr. Liudong Xing Committee Members: Dr. Hong Liu and Dr. Honggang Wang, Department of Electrical & Computer Engineering, University of Massachusetts Dartmouth; Dr. Yan Sun, Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island. *For further information, please contact Dr. Liudong Xing via email at firstname.lastname@example.org.
Topic: A Path Towards Risk Averse Autonomous Vehicle Navigation Zoom Teleconference: https://umassd.zoom.us/my/chellis Abstract: Current approaches to developing autonomous moving agents only allow the agent to operate in an environment representative of its previous experience. For agents that are modeled using a Markov decision process (MDP), a policy for navigation behavior is generated as the result of optimizing a reward function. Traditionally, this reward function is pre-defined by the algorithm designers and immutable. If the designed reward function fails to capture all aspects of the agents operational environment, undesired behavior will occur when the agent fails to optimize the true reward function and therefore express indifference to potentially dangerous, unseen scenarios. In the context of autonomous ground vehicles (AGV), consider an AGV which has been optimized in a wooded, off road environment described by a representative reward function. If the AGV is placed in a different environment such as an urban area, the original reward function will fail to accurately describe the desired behavior due to the presence of new terrain features, leading to potentially dangerous behavior. When an agent encounters features never seen before during training, how can an agent respond to these features? In these potentially dangerous scenarios (edge cases), an agents behavior should be risk adverse to decrease the chance of total system or mission failure. This research explores the development of a risk adverse AGV by expressing uncertainty in the designed reward function by considering all possible reward functions that satisfy the training MDP. A Bayesian method is proposed to infer a reward function from a partially defined reward function based on human demonstrations and a training MDP, which enables risk averse behavior. NOTE: All ECE Graduate Students are ENCOURAGED to join the zoom teleconference. All interested parties are invited to join. Advisor: Dr. Lance Fiondella Committee Members: Dr. Liudong Xing and Dr. Hong Liu, Department of Electrical & Computer Engineering, University of Massachusetts Dartmouth *For further information, please contact Dr. Lance Fiondella via email at email@example.com.