Self-building Artificial Intelligence and machine learning to empower big data analytics in smart cities

YesThe emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the selfbuilding AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.Supported by the Data to Decisions Cooperative Research Centre (D2D CRC) as part of their analytics and decision support program and a La Trobe University Postgraduate Research Scholarship

Kinase-Impaired BTK Mutations Are Susceptible to Clinical-Stage BTK and IKZF1/3 Degrader NX-2127

INTRODUCTION: Bruton’s tyrosine kinase (BTK) is a nonreceptor kinase in the B cell receptor (BCR) signaling cascade critical for B cell survival. As such, chronic lymphocytic leukemia (CLL) and other B cell cancers are sensitive to inhibition of BTK. Covalent and noncovalent inhibitors of BTK have revolutionized the treatment of these cancers. Therefore, understanding mechanisms by which acquired mutation in BTK confer drug resistance and developing new therapies to overcome resistance are critically important. RATIONALE: We recently discovered BTK mutations that confer resistance across covalent and noncovalent BTK inhibitors. In this study, we found that a group of these mutants impair BTK kinase activity despite still enabling downstream BCR signaling. We therefore set out to understand the nonenzymatic functions of BTK and explored targeted protein degradation to overcome the oncogenic scaffold function of mutant BTK. This effort included evaluation of BTK degradation in patients with CLL treated in a phase 1 clinical trial of NX-2127, a first-in-class BTK degrader (NCT04830137). RESULTS: BTK enzymatic activity assays revealed that drug resistance mutations in BTK fall into two distinct groups: kinase proficient and kinase impaired. Immunoprecipitation mass spectrometry of kinase-impaired BTK L528W (Leu528→Trp) revealed a scaffold function of BTK with downstream signaling and survival dependent on surrogate kinases that bind to kinase-impaired BTK proteoforms. To target the nonenzymatic functions of BTK, we developed NX-2127, a heterobifunctional molecule that engages the ubiquitin-proteasome system to simultaneously bind both BTK and the cereblon E3 ubiquitin ligase complex, inducing polyubiquitination and proteasome-dependent degradation of IKZF1/3 and all recurrent drug-resistant forms of mutant BTK. The activity of NX-2127 on BTK degradation was further demonstrated in patients with CLL treated in a phase 1 clinical trial of NX-2127, where \u3e80% BTK degradation was achieved and clinical responses were also seen in 79% of evaluable patients, independent of mutant BTK genotypes. CONCLUSION: We identified that BTK inhibitor resistance mutations fall into two distinct functional categories. Kinase-impaired BTK mutants disable BTK kinase activity while promoting physical interactions with other kinases to sustain downstream BCR signaling. This scaffold function of BTK was disrupted by NX-2127, a potent BTK degrader, which showed promising responses for patients with relapsed and refractory CLL, independently of mutant BTK functional category

Spatiotemporal anomaly detection using deep learning for real-time video surveillance

Rapid developments in urbanization and autonomous industrial environments have augmented and expedited the need for intelligent real-time video surveillance. Recent developments in artificial intelligence for anomaly detection in video surveillance only address some of the challenges, largely overlooking the evolving nature of anomalous behaviors over time. Tightly coupled dependence on a known normality training dataset and sparse evaluation based on reconstruction error are further limitations. In this article, we propose the incremental spatiotemporal learner (ISTL) to address challenges and limitations of anomaly detection and localization for real-time video surveillance. ISTL is an unsupervised deep-learning approach that utilizes active learning with fuzzy aggregation, to continuously update and distinguish between new anomalies and normality that evolve over time. ISTL is demonstrated and evaluated on accuracy, robustness, computational overhead as well as contextual indicators, using three benchmark datasets. Results of these experiments validate our contribution and confirm its suitability for real-time video surveillance

Self-evolving intelligent algorithms for facilitating data interoperability in IoT environments

Internet of Things (IoT) is predicted to connect 20.4 billion devices in 2020 and surge to 75 billion by 2025. Such a connected world where machines will communicate with other machines opens up huge opportunities and a very different way of life, with smart homes, self-driving vehicles and wearable devices. It is expected that such interconnectedness will enable the capture of events as data in real time and provide actionable insights to people and organizations to maximize efficiencies, be pro-active and more effective. Interconnected devices will require interoperability, and the seamless, secure and controlled exchange of data between devices and applications has been called data interoperability. Such a dynamic and volatile environment with a wide diversity of data will require a new breed of intelligent algorithms with the ability to adapt and self-learn as well as envisage and analyse events at multiple levels of abstraction to gauge association and interrelationships. This research proposes three algorithmic requirements for intelligent algorithms in such IoT environments: unsupervised self-learning capability, ability to self-generate to the environment and incrementally learn with temporal changes. The paper first presents empirical results with real data from a fire department in Australia to highlight the need and value of IoT and data interoperability. Dynamic Self Organizing Map based unsupervised algorithms which satisfy the requirements are described and further empirical results are presented to validate the required functionality of these algorithms