13,243 research outputs found
A formal framework for security testing of automotive over-the-air update systems
Modern vehicles are comparable to desktop computers due to the increase in connectivity. This fact also extends to potential cyber-attacks. A solution for preventing and mitigating cyber attacks is Over-The-Air (OTA) updates. This solution has also been used for both desktops and mobile phones. The current de facto OTA security system for vehicles is Uptane, which is developed to solve the unique issues vehicles face. The Uptane system needs to have a secure method of updating; otherwise, attackers will exploit it. To this end, we have developed a comprehensive and model-based security testing approach by translating Uptane and our attack model into formal models in Communicating Sequential Processes (CSP). These are combined and verified to generate an exhaustive list of test cases to see to which attacks Uptane may be susceptible. Security testing is then conducted based on these generated test cases, on a test-bed running an implementation of Uptane. The security testing result enables us to validate the security design of Uptane and some vulnerabilities to which it is subject
A Multi-level Analysis on Implementation of Low-Cost IVF in Sub-Saharan Africa: A Case Study of Uganda.
Introduction: Globally, infertility is a major reproductive disease that affects an estimated 186 million people worldwide. In Sub-Saharan Africa, the burden of infertility is considerably high, affecting one in every four couples of reproductive age. Furthermore, infertility in this context has severe psychosocial, emotional, economic and health consequences. Absence of affordable fertility services in Sub-Saharan Africa has been justified by overpopulation and limited resources, resulting in inequitable access to infertility treatment compared to developed countries. Therefore, low-cost IVF (LCIVF) initiatives have been developed to simplify IVF-related treatment, reduce costs, and improve access to treatment for individuals in low-resource contexts. However, there is a gap between the development of LCIVF initiatives and their implementation in Sub-Saharan Africa. Uganda is the first country in East and Central Africa to undergo implementation of LCIVF initiatives within its public health system at Mulago Women’s Hospital.
Methods: This was an exploratory, qualitative, single, case study conducted at Mulago Women’s Hospital in Kampala, Uganda. The objective of this study was to explore how LCIVF initiatives have been implemented within the public health system of Uganda at the macro-, meso- and micro-level. Primary qualitative data was collected using semi-structured interviews, hospital observations informal conversations, and document review. Using purposive and snowball sampling, a total of twenty-three key informants were interviewed including government officials, clinicians (doctors, nurses, technicians), hospital management, implementers, patient advocacy representatives, private sector practitioners, international organizational representatives, educational institution, and professional medical associations. Sources of secondary data included government and non-government reports, hospital records, organizational briefs, and press outputs. Using a multi-level data analysis approach, this study undertook a hybrid inductive/deductive thematic analysis, with the deductive analysis guided by the Consolidated Framework for Implementation Research (CFIR).
Findings: Factors facilitating implementation included international recognition of infertility as a reproductive disease, strong political advocacy and oversight, patient needs & advocacy, government funding, inter-organizational collaboration, tension to change, competition in the private sector, intervention adaptability & trialability, relative priority, motivation &advocacy of fertility providers and specialist training. While barriers included scarcity of embryologists, intervention complexity, insufficient knowledge, evidence strength & quality of intervention, inadequate leadership engagement & hospital autonomy, poor public knowledge, limited engagement with traditional, cultural, and religious leaders, lack of salary incentives and concerns of revenue loss associated with low-cost options.
Research contributions: This study contributes to knowledge of factors salient to implementation of LCIVF initiatives in a Sub-Saharan context. Effective implementation of these initiatives requires (1) sustained political support and favourable policy & legislation, (2) public sensitization and engagement of traditional, cultural, and religious leaders (3) strengthening local innovation and capacity building of fertility health workers, in particular embryologists (4) sustained implementor leadership engagement and inter-organizational collaboration and (5) proven clinical evidence and utilization of LCIVF initiatives in innovator countries. It also adds to the literature on the applicability of the CFIR framework in explaining factors that influence successful implementation in developing countries and offer opportunities for comparisons across studies
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks
Towards Fast and Scalable Private Inference
Privacy and security have rapidly emerged as first order design constraints.
Users now demand more protection over who can see their data (confidentiality)
as well as how it is used (control). Here, existing cryptographic techniques
for security fall short: they secure data when stored or communicated but must
decrypt it for computation. Fortunately, a new paradigm of computing exists,
which we refer to as privacy-preserving computation (PPC). Emerging PPC
technologies can be leveraged for secure outsourced computation or to enable
two parties to compute without revealing either users' secret data. Despite
their phenomenal potential to revolutionize user protection in the digital age,
the realization has been limited due to exorbitant computational,
communication, and storage overheads.
This paper reviews recent efforts on addressing various PPC overheads using
private inference (PI) in neural network as a motivating application. First,
the problem and various technologies, including homomorphic encryption (HE),
secret sharing (SS), garbled circuits (GCs), and oblivious transfer (OT), are
introduced. Next, a characterization of their overheads when used to implement
PI is covered. The characterization motivates the need for both GCs and HE
accelerators. Then two solutions are presented: HAAC for accelerating GCs and
RPU for accelerating HE. To conclude, results and effects are shown with a
discussion on what future work is needed to overcome the remaining overheads of
PI.Comment: Appear in the 20th ACM International Conference on Computing
Frontier
Effects of the COVID-19 pandemic on the mental health of clinically extremely vulnerable children and children living with clinically extremely vulnerable people in Wales: a data linkage study
Objectives: To determine whether clinically extremely vulnerable (CEV) children or children living with a CEV person in Wales were at greater risk of presenting with anxiety or depression in primary or secondary care during the COVID-19 pandemic compared with children in the general population and to compare patterns of anxiety and depression during the pandemic (23 March 2020–31 January 2021, referred to as 2020/2021) and before the pandemic (23 March 2019–31 January 2020, referred to as 2019/2020), between CEV children and the general population. Design: Population-based cross-sectional cohort study using anonymised, linked, routinely collected health and administrative data held in the Secure Anonymised Information Linkage Databank. CEV individuals were identified using the COVID-19 shielded patient list. Setting: Primary and secondary healthcare settings covering 80% of the population of Wales. Participants: Children aged 2–17 in Wales: CEV (3769); living with a CEV person (20 033); or neither (415 009). Primary outcome measure: First record of anxiety or depression in primary or secondary healthcare in 2019/2020 and 2020/2021, identified using Read and International Classification of Diseases V.10 codes. Results: A Cox regression model adjusted for demographics and history of anxiety or depression revealed that only CEV children were at greater risk of presenting with anxiety or depression during the pandemic compared with the general population (HR=2.27, 95% CI=1.94 to 2.66, p<0.001). Compared with the general population, the risk among CEV children was higher in 2020/2021 (risk ratio 3.04) compared with 2019/2020 (risk ratio 1.90). In 2020/2021, the period prevalence of anxiety or depression increased slightly among CEV children, but declined among the general population. Conclusions: Differences in the period prevalence of recorded anxiety or depression in healthcare between CEV children and the general population were largely driven by a reduction in presentations to healthcare services by children in the general population during the pandemic
An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains
This research aimed to develop an empirical understanding of the relationships between integration,
dynamic capabilities and performance in the supply chain domain, based on which, two conceptual
frameworks were constructed to advance the field. The core motivation for the research was that, at
the stage of writing the thesis, the combined relationship between the three concepts had not yet
been examined, although their interrelationships have been studied individually.
To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative
study, which was undertaken via multiple case studies to investigate lines of enquiry that would
address the research questions formulated. This is consistent with the author’s philosophical
adoption of the ontology of relativism and the epistemology of constructionism, which was considered
appropriate to address the research questions. Empirical data and evidence were collected, and
various triangulation techniques were employed to ensure their credibility. Some key features of
grounded theory coding techniques were drawn upon for data coding and analysis, generating two
levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in
improving performance, the performance also informed the former. This reflects a cyclical and
iterative approach rather than one purely based on linearity. Adopting a holistic approach towards
the relationship was key in producing complementary strategies that can deliver sustainable supply
chain performance.
The research makes theoretical, methodological and practical contributions to the field of supply
chain management. The theoretical contribution includes the development of two emerging
conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it
allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed
insight into their correlations. The latter gives a holistic view of their relationships and how they are
connected, reflecting a middle-range theory that bridges theory and practice. The methodological
contribution lies in presenting models that address gaps associated with the inconsistent use of
terminologies in philosophical assumptions, and lack of rigor in deploying case study research
methods. In terms of its practical contribution, this research offers insights that practitioners could
adopt to enhance their performance. They can do so without necessarily having to forgo certain
desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities
Novel tools for identification of oncogenic driver mutations
Genetic alterations contribute to the development and pathogenesis of several human cancers. These mutations accumulate in a cancer tissue over the course of time due to the instability of the cancer genome. Large-scale sequencing efforts have enabled identification of an abundance of these somatic mutations, and the amount of data is constantly increasing due to the improved accessibility of next-generation sequencing technologies. From this multitude of cancer-associated somatic mutations, a large majority are predicted to be inconsequential “passenger” mutations, (i.e., mutations which do not confer a selective growth advantage to the cancer cells); and only a handful have been validated as “driver” mutations (i.e., mutations playing a critical role in the development or maintenance of cancer). These driver mutations also function as predictive markers for survival, therapeutic efficacy, and often make the cancer cells susceptible to therapeutic intervention.
Identification of driver mutations is an integral part of biomarker discovery in cancer research, and my thesis aimed to address this by developing a screening platform and a database. The in vitro Screen for Activating Mutations (iSCREAM) is a high-throughput screening workflow which was established with Epidermal Growth Factor Receptor (EGFR) as a model. The screen was validated by detection of known activating mutations like EGFR L858R. A previously known EGFR variant of unknown significance (VUS), EGFR A702V, was discovered in the screen and was functionally characterized to be an activating mutation. The iSCREAM screening methodology was further used to systematically study ERBB4, another gene in the EGFR family of receptor tyrosine kinases. We detected ERBB4 VUS R687K, and E715K in the screen and identify them as activating mutations. The ERBB4 mutations were characterized for their effect on ERBB4 phosphorylation, their sensitivity to various tyrosine kinase inhibitors, and their tumorigenicity was evaluated with in vivo allografts.
The Database Of Recurrent Mutations (DORM), was prepared by analyzing a public registry of somatic mutations and preparing a catalog of the mutations identified from genome-wide studies to recapitulate the “real-world” frequency of all the recurrent (n > 1) somatic mutations. DORM allows limiting the scope of search to 38 tissue types and supports advanced queries using regular expressions. The easy-to-use database and its backend are written to be very responsive and fast in comparison to contemporary public cancer databases.
Taken together, the findings and resources presented in this thesis establish grounds for further studies with other tyrosine kinases and potentially enable diversification into new niches.Uusia työkaluja syöpää aiheuttavien mutaatioiden tunnistamiseksi
Geneettiset muutokset vaikuttavat useiden ihmisen syöpien syntyyn ja kehittymiseen. Syöpäkudokseen geenimutaatioita kertyy yhä enemmän ajan kuluessa syövän genomisen instabiliteetin vuoksi. Laajamittaisten sekvensointihankkeiden avulla on pystytty tunnistamaan paljon erilaisia somaattisia eli hankinnallisia mutaatioita ja sekvensointitulosten määrä kasvaa jatkuvasti uuden sukupolven sekvensointitekniikoiden (engl. next generation sequencing, NGS) paremman saatavuuden ansiosta. Näistä lukuisista syöpään liittyvistä somaattisista mutaatioista suurin osa on potilaan ennusteen kannalta merkityksettömiä "matkustajamutaatioita" (engl. passenger mutation) eli mutaatioita, jotka eivät anna valikoivaa kasvuetua syöpäsoluille. Vain muutamia somaattisia mutaatioita on validoitu "ajajamutaatioiksi" (engl. driver mutation) eli mutaatioiksi, joilla on kriittinen rooli syövän kehittymisessä tai ylläpitämisessä. Nämä ajajamutaatiot toimivat usein eloonjäämisen sekä hoidon tehon ennusteellisina markkereina ja usein myös herkistävät syöpäsoluja hoidoille.
Ajajamutaatioiden tunnistaminen on olennainen osa syövän biomarkkereiden tutkimusta. Väitöskirjatyöni tavoitteena oli kehittää ajajamutaatioiden seulonta-alusta ja tietokanta. Aktivoivien mutaatioiden in vitro -seulonta (engl. in vitro Screen for Activating Mutations, iSCREAM) on tehoseulontamenetelmä, jonka kehittämistyössä käytettiin mallina epidermaalista kasvutekijäreseptoria (EGFR) koodaavaa geeniä iSCREAM-seulonnalla tunnistettiin jo tunnettuja aktivoivia EGFR-mutaatioita, kuten L858R, mikä validoi menetelmän toimivuuden. Seulontamenetelmällä tunnistettiin ja karakterisoitiin myös uusi EGFR-geenin aktivoiva mutaatio, A702V, jonka oletettu toimintamekanismi selvitettiin. iSCREAM-seulontamenetelmää hyödynnettiin tässä työssä myös EGFR-reseptorityrosiinikinaasiperheen toisen geenin, ERBB4-geenin, systemaattiseen tutkimiseen, jonka avulla löydettiin uusina aktivoivina mutaatioina ERBB4 R687K ja E715K. Näiden ERBB4-mutaatioiden vaikutusta ERBB4:n fosforylaatioon ja lääkeherkkyyteen erilaisille tyrosiinikinaasiestäjille karakterisoitiin, ja niiden tuumorigeenisyys validoitiin in vivo -allografteissa.
Toistuvien mutaatioiden tietokanta (engl. Database Of Recurrent Mutations, DORM) luotiin analysoimalla somaattisten mutaatioiden julkista rekisteriä ja laatimalla luettelo genominlaajuisissa tutkimuksissa tunnistetuista mutaatioista, jotta kaikkien toistuvien (n > 1) somaattisten mutaatioiden "todellinen" esiintymistiheys voitaisiin laskea. DORM mahdollistaa haun rajoittamisen 38:aan kudostyyppiin ja tukee edistyneempiä kyselyjä säännöllisten lausekkeiden (engl. regular expression) avulla. Helppokäyttöinen tietokanta ja sen taustajärjestelmä kehitettiin hyvin reagoivaksi ja nopeaksi nykyisiin julkisiin syöpätietokantoihin verrattuna. Tässä työssä esitetyt havainnot ja resurssit luovat yhdessä perustan jatkotutkimuksille muilla tyrosiinikinaaseilla ja ovat mahdollisesti laajennettavissa muillekin tutkimusalueille
Spear or Shield: Leveraging Generative AI to Tackle Security Threats of Intelligent Network Services
Generative AI (GAI) models have been rapidly advancing, with a wide range of
applications including intelligent networks and mobile AI-generated content
(AIGC) services. Despite their numerous applications and potential, such models
create opportunities for novel security challenges. In this paper, we examine
the challenges and opportunities of GAI in the realm of the security of
intelligent network AIGC services such as suggesting security policies, acting
as both a ``spear'' for potential attacks and a ``shield'' as an integral part
of various defense mechanisms. First, we present a comprehensive overview of
the GAI landscape, highlighting its applications and the techniques
underpinning these advancements, especially large language and diffusion
models. Then, we investigate the dynamic interplay between GAI's spear and
shield roles, highlighting two primary categories of potential GAI-related
attacks and their respective defense strategies within wireless networks. A
case study illustrates the impact of GAI defense strategies on energy
consumption in an image request scenario under data poisoning attack. Our
results show that by employing an AI-optimized diffusion defense mechanism,
energy can be reduced by 8.7%, and retransmission count can be decreased from
32 images, without defense, to just 6 images, showcasing the effectiveness of
GAI in enhancing network security
Based on the difference of Newton’s method integrated energy system distributed collaborative optimization
With the integration of renewable energy into the grid, the traditional power system stability faced by huge challenges, and the development of integrated energy system, it is of essence to improve the coupling of multiple integrated energy systems of different types, management in the integrated energy system and reduce the pressure of communication and computing, in this paper, we construct a distributed Newton algorithm based on Newton’s method to accelerate the solving speed, which decreases the times of iterations to reduce the pressure of communication and calculation, saving the cost of operation. Besides, privacy protection is particularly important for a distributed control system, under the premise that calculation speed is guaranteed, meanwhile, privacy protection of all agents in an integrated energy system is also critical. This study uses annular directed distributed algorithm to enhance the privacy of integrated distributed energy systems in the intelligent body, so as to fully ensure the privacy safety of all agents in the system. Moreover, the forementioned difference Newton algorithm in this study avoid the behavior of Zeno, greatly accelerating the speed of iteration and finding the best energy market price,. At the same time, the privacy safety of all agentsin the distributed energy system are ensured. Finally, a distributed integrated energy system based on the algorithm proposed by this study has went through theoretical proof and simulation experiment, whose result shows the validity of the algorithm
Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review
Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)
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