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Background: The Diabetes Health Plan (DHP), a value-based insurance plan that reduces cost sharing, was previously shown to modestly increase employer-level medication adherence. However, it is unclear whether the DHP has a larger impact on individual-level medication adherence among lower- or higher-income beneficiaries. Methods: An employer-level propensity score match was done to identify suitable control employers, followed by individual-level propensity score weighting. These weights were applied to difference-in-difference (DID) models examining 1) the effect of the DHP and 2) the effect of income on changes in adherence to metformin, statins, and ACE/ARBs. The weights were then applied to a differences-in-differences-in-differences (DDD) model to estimate the differential impact of DHP status on changes in adherence by income group. This is a retrospective, quasi-experimental study.Results: There were no significant differences in changes to adherence for any medications between beneficiaries enrolled in the DHP versus standard plans. However, changes in adherence were higher for all medications among those in the highest income strata (>$75,000) versus those in the lowest income strata (Conclusion: We did not find significant associations between the DHP and changes in individual-level medication adherence, even for low-income beneficiaries. New strategies to improve consumer engagement may be needed in order to translate value-based insurance designs into changes in patient behavior.

The rapid development in manufacturing industries due to the introduction of IIoT devices has led to the emergence of Industry 4.0 which results in an industry with intelligence, increased efficiency and reduction in the cost of manufacturing. However, the introduction of IIoT devices opens up the door for a variety of cyber threats in smart industries. The detection of cyber threats against such extensive, complex, and heterogeneous smart manufacturing industries is very challenging due to the lack of sufficient attack traces. Therefore, in this work, a Federated Learning enabled Deep Intrusion Detection framework is proposed to detect cyber threats in smart manufacturing industries. The proposed FLDID framework allows multiple smart manufacturing industries to build a collaborative model to detect threats and overcome the limited attack example problem with individual industries. Moreover, to ensure the privacy of model gradients, Paillier-based encryption is used in communication between edge devices (representative of smart industries) and the server. The deep learning-based hybrid model, which consists of a Convolutional Neural Network, Long Short Term Memory, and Multi-Layer Perceptron is used in the intrusion detection model. An exhaustive set of experiments on the publically available dataset proves the effectiveness of the proposed framework for detecting cyber threats in smart industries over the state-of-the-art approaches.

High quality, readable health information is vital to mitigate the impact of the COVID-19 pandemic. The aim of this study was to assess the quality and readability of online COVID-19 information using 6 validated tools. This is a cross-sectional study. “COVID-19” was searched across the three most popular English language search engines. Quality was evaluated using the DISCERN score, Journal of the American Medical Association benchmark criteria and Health On the Net Foundation Code of Conduct. Readability was assessed using the Flesch Reading Ease Score, Flesch-Kincaid Grade Level and Gunning-Fog Index. 41 websites were suitable for analysis. 9.8% fulfilled all JAMA criteria. Only one website was HONCode certified. Mean DISCERN score was 47.8/80 (“fair”). This was highest in websites published by a professional society/medical journal/healthcare provider. Readability varied from an 8th to 12th grade level. The overall quality of online COVID-19 information was “fair”. Much of this information was above the recommended 5th to 6th grade level, impeding access for many.

Zero Trust security model permits to secure cloud native applications while encrypting all network communication, authenticating, and authorizing every request. The service mesh can enable Zero Trust using a side-car proxy without changes to the application code. To the best of our knowledge, no previous work has provided a performance analysis of Zero Trust in a multi-cloud environment. This paper proposes a multi-cloud framework and a testing workflow to analyze performance of the data plane under load and the impact on the control plane, when Zero Trust is enabled. The results of preliminary tests show that Istio has reduced latency variability in responding to sequential HTTP requests. Results also reveal that the overall CPU and memory usage can increase based on service mesh configuration and the cloud environment., This paper was submitted for the IEEE CLOUD 2021 Conference and is currently being reviewed

Distributed Software-Defined Networks (SDNs) aim to maintain a consistent network state across members of the distributed control plane. This paper introduces a novel variation to the packet-in flood designed to target distributed SDNs that synchronise the network state in a strongly consistent manner. The Event Flooding Attack (EFA) takes advantage of the characteristics of a strong consistency model to enable an attacker to distribute the adverse effect of a DoS attack across a cluster, as well as engineer inconsistency between the true network state and the control plane’s view of this state. The impact of the attack is evaluated through experiments using an OpenDaylight cluster. It has been demonstrated on the testbed used in this work that an attacker can increase CPU consumption on all cluster nodes and cause inconsistency for a period of ≈ 55 s when 500 events are flooded at a frequency of 1/ms, while the same can be achieved for a period of ≈ 770 s when 2000 events are flooded at the same frequency. The impact of the attack is further demonstrated through it’s collaboration with, and simplification of, an existing host impersonation attack.

This project was completed as part of a project conducted with an industrial partner DellEMC

Artificial Intelligence (AI) is an enabling technology that when integrated into healthcare applications and smart wearable devices such as Fitbits etc. can predict the occurrence of health conditions in users by capturing and analysing their health data. The integration of AI and smart wearable devices has a range of potential applications in the area of smart healthcare but there is a challenge in the black box operation of decisions made by AI models which have resulted in a lack of accountability and trust in the decisions made. Explainable AI (XAI) is a domain in which techniques are developed to explain predictions made by AI systems. In this paper, XAI is discussed as a technique that can used in the analysis and diagnosis of health data by AI-based systems and a proposed approach presented with the aim of achieving accountability. transparency, result tracing, and model improvement in the domain of healthcare.

Context The aspect-oriented paradigm is aimed at solving the code scattering and tangling problem, providing new mechanisms to support better separation of concerns. For specific scenarios where high runtime adaptability is an important requirement, dynamic Aspect-Oriented Programming (AOP) represents a useful tool. With dynamic AOP, components and aspects can be woven and unwoven at runtime, enabling applications greater responsiveness when dealing with different or changing requirements. However, this responsiveness typically incurs a cost in terms of runtime performance and memory consumption. Objective Build an efficient dynamic aspect weaver for Java that provides the best runtime performance compared to the existing approaches, minimum memory overhead consumption, and similar functionalities to the widespread runtime weavers. Method We design and implement weaveJ, a dynamic aspect weaver for Java. This dynamic weaver leverages the invokedynamic opcode introduced in Java 7, which allows dynamic relinkage of method and field access. We compare the functionalities of weaveJ with the existing dynamic weavers for Java, and evaluate their runtime performance and memory consumption. Results weaveJ shows the best runtime performance for all benchmarks and real applications executed. Method interception with invokedynamic is at least 142% faster than the techniques used by the existing runtime weavers. The average cost of dynamic weaving using invokedynamic is only 2.2% for short running programs, and 1.5% for long running applications. Moreover, the use of aspects in weaveJ does not imply additional memory consumption. Conclusion The dynamic aspect weaver implemented demonstrates that invokedynamic is a suitable mechanism to provide efficient runtime aspect weaving for Java applications. Moreover, it supports concurrent and programmatic aspect (un)weaving at any point of execution, a wide set of join points, class and object weaving, and allow aspects to have their own state. Neither the Java language nor the virtual machine needs to be modified.

Objective: Chronic depression is associated with significant impairment in work functioning, relationships, and health. Such impairment often persists following medication-induced remission of depressive symptoms. We adapted and tested Behavioral Activation therapy with a goal of return to work (BA-W) in subjects with chronic depression who had responded to medication treatment but remained unemployed. Method: Sixteen adults aged 18–65 with DSM-IV diagnosed Dysthymic Disorder or chronic Major Depression were recruited from clinical trials taking place at the New York State Psychiatric Institute between 4/2009 and 12/2012 and enrolled in 12 weeks of individual manual-driven BA-W. Functioning was measured at intake, post-treatment and at 24 week follow-up. Results: Eighty-seven percent (n = 14) of subjects completed the full 12 weeks of BA-W. Hours of work related activity (p

Encouraged by the success of network virtualization in enterprise networks, wireless sensor network (WSN) virtualization has been receiving increasing attention. WSN virtualization is seen as offering economic benefits through resource sharing. But the actual gain is not clear and its cost-benefit ratio has not been thoroughly analyzed. While the discussion about the economic benefits of network virtualization is often reduced to two key components, namely CAPEX and OPEX, the effect of radio interference in wireless multi-hop networks such as WSN has not been considered. In this paper, we analyze the influencing factors that contribute to the cost of providing a virtual WSN over a shared physical substrate. We model the CAPEX and OPEX associated with Virtualized WSNs (VWSNs) and introduce an additional element, which we call INEX, into our cost function which captures the future impact (i.e. the ”actual cost”) of interference. INEX results in a loss of income due to increased VN request rejection rates. Using this model we conduct extensive simulations that allow us to compare the cost difference between renting shared WSN resources and deploying a standalone network. The results indicate that shared WSNs result in higher cost when there is only one sink.

Accommodating multiple different applications on a single sensor node or within the same wireless sensor network (WSN) offers numerous benefits including reduced deployment cost and improved utilization of the physical substrate. This paper presents a new middleware for WSNs, called Motley. Motley, enables a shared infrastructure and multi-application support for WSNs by providing services for the dissemination, installation and scheduling of applications with diverse QoS requirements. Implementation challenges and the measures employed in Motley to overcome these as part of a shared WSN substrate are discussed. Some initial findings on the cost in terms of code loading time to support multiple applications are presented as part of this paper.

The Public Cloud has enabled the provisioning & management of an organisations IT infrastructure to be handled by a third-party, otherwise referred to Infrastructure as a Service (IaaS). This offers the potential to create cost effective & scalable IT solutions. However, little work has been done on the processes involved in cloud migration of existing infrastructure. Specifically, there has been no framework or solution implemented in a case study setting to detail the processes necessary to perform a migration of existing infrastructure to the public cloud. This paper presents the architecture of an Environment Migration Framework (EMF), the purpose of which is to automate the migration of existing IT infrastructure to the IaaS platform. To prove the viability of this framework, an ongoing case study is described where the Environment Migration Framework is being implemented in an organisation seeking to migrate their existing virtual machine environments to the public cloud. The final sections of this paper detail the results obtained so far from the ongoing project and the future work.

The centralised control provided by Software- Defined Networking allows an increase in network security as all traffic can be vetted before leaving the attachment switch. Nevertheless, as in any complex system, there are implementation and policy compromises which lead to security vulnerabilities. This paper exploits such vulnerabilities to implement a suite of attacks, consisting of Address Resolution Protocol (ARP) cache poisoning, Man in the Middle, a firewall and access control bypassing port scan called a Phantom Host Scan, and a Distributed Denial of Service attack called a Phantom Storm which induces the participation of legitimate hosts. These attacks were successfully implemented in a Floodlight controlled network.

Background: During the past decade, several novel medication treatments and psychosocial interventions have been tested. Overall, their impact on reducing alcohol use and preventing relapse has been modest. These outcomes have spurred researchers to investigate whether the addition of manualized psychosocial therapies with demonstrated efficacy to pharmacotherpy would have a synergistic effect. Objectives: We conducted a meta-analysis to test the hypothesis that the addition of manualized psychosocial therapies would reduce the frequency of relapse. Results: This review analyzed data from six studies. Among studies that used random assignment to manually guided psychosocial interventions, the rates of relapse between the naltrexone plus a psychosocial intervention and naltrexone without a psychosocial intervention were very similar. Among patients allocated to placebo, similar rates of relapse were also found between Cognitive Behavioral Therapy (CBT) and non-CBT. In studies which used manualized psychotherapies as a platform, relapse rates were similar between naltrexone and placebo. In contrast, studies using Treatment As Usual psychotherapy as a platform demonstrated lower rates of relapse in the naltrexone group, compared to placebo group. Conclusions: We conclude that CBT does not appear to offer benefits beyond those derived from study medications. Scientific Significance: The benefit of the addition of CBT to medication in preventing relapse may be limited. (Am J Addict 2012;21:501–507)

Objective Numerous double-blind studies have assessed the efficacy of antidepressants in treating chronic depressive disorder, including dysthymic disorder, low-grade chronic depression. However, there are no double-blind, placebo-controlled studies of serotonin-norepinephrine reuptake inhibitors in chronic depressive disorder. Method Outpatients with chronic depressive disorder, but without concurrent major depressive disorder (MDD), were randomly assigned to prospective double-blind duloxetine (beginning at 30 mg/d, increased to a maximum dose of 120 mg/d) versus placebo for 10 weeks. Inclusion criteria were current DSM-IV-TR diagnosis of dysthymic disorder or depression not otherwise specified, age 18-75 years, and a Hamilton Depression Rating Scale (HDRS) score ≥ 12. Exclusion criteria included current major depression. The study was conducted between August 2006 and December 2011. HDRS, Cornell Dysthymia Rating Scale (CDRS), Clinical Global Impressions (CGI), Beck Depression Inventory (BDI), Global Assessment of Functioning (GAF), Social Adjustment Scale (SAS), and other assessments were administered at each visit. We hypothesized that duloxetine would be superior to placebo in (1) 24-item HDRS total score, (2) the percentage of subjects classified as responders and remitters, and (3) secondary measures (CDRS, BDI, CGI). Response was defined as > 50% decrease in 24-item HDRS and CGI-Improvement scale score of 1 or 2 ("very much improved" or "much improved"). Remission was defined as HDRS-17 item score ≤ 4 and 0 on item 1 of the HDRS (depressed mood). Results 65 subjects were enrolled, of whom 57 began medication. They ranged in age from 19 to 70 years (mean ± SD = 41.63 ± 11.22) and included 24 women and 33 men. Baseline 24-item HDRS score (mean ± SD) for both groups was 20.75 ± 4.92. After 10 weeks, duloxetine-treated subjects had significantly lower 24-item HDRS scores than placebo-treated subjects (time-by-drug group effect on analysis of variance: F1,55 = 9.43, P = .003). Responder and remitter analyses significantly favored duloxetine treatment. The response rate was 65.5% for duloxetine versus 25.0% for placebo (χ(2)(1) = 9.43, P = .003); and the remitter rate was 55.2% for duloxetine versus 14.3% for placebo (χ(2)(1) = 10.46, P = .002). After 10 weeks, duloxetine-treated subjects did not differ significantly better from placebo-treated subjects on the SAS (time-by-drug group effect on analysis of variance: F(1,46) = 0.35, P = .555) or on the GAF (time-by-drug group effect on analysis of variance: F(1,51) = .01, P = .922). Conclusions Results on the 24-item HDRS, CGI, and CDRS suggest that duloxetine is efficacious in acute treatment of chronic nonmajor depressive disorder. Response and remission rates also differed significantly, favoring duloxetine treatment, but BDI, GAF, and social functioning (Social Adjustment Scale) did not. Duloxetine appears to be effective in acute treatment of nonmajor chronic depression. Trial registration ClinicalTrials.gov identifier: NCT00360724.