Abstract--Federated learning has emerged as an attractive approach to protect data privacy by eliminating the need for sharing clients' data while reducing communication costs compared with centralized machine learning algorithms. However, recent studies have shown that federated learning alone does not guarantee privacy, as private data may still be inferred from the uploaded parameters to the central server. In order to successfully avoid data leakage, adopting differential privacy (DP) in the local optimization process or in the local update aggregation process has emerged as two feasible ways for achieving sample-level or user-level privacy guarantees respectively, in federated learning models. However, compared to their non-private equivalents, these approaches suffer from a poor utility . T o improve the privacy-utility trade-off, we present a modification to these vanilla differentially private algorithms based on a Haar wavelet transformation step and a novel noise injection scheme that significantly lowers the asymptotic bound of the noise variance. We also present a holistic convergence analysis of our proposed algorithm, showing that our method yields better convergence performance than the vanilla DP algorithms. Numerical experiments on real-world datasets demonstrate that our method outperforms existing approaches in model utility while maintaining the same privacy guarantees. Machine learning (ML) has become an essential tool to analyze this data and extract valuable insights for various applications, including facial recognition, data analytics, weather prediction, and speech recognition, among others [1], [2], [3], [4], [5]. However, in real-world settings, data -- particularly personal data -- is often created and stored on end-user devices. The majority of traditional ML algorithms require the centralization of these training data, which involves collecting and processing data at a potent cloud-based server [6], [7]. This process carries significant risks to data integrity and privacy, particularly when it comes to personal data. Kanishka Ranaweera is with School of Engineering and Built Environment, Deakin University, Waurn Ponds, VIC 3216, Australia, and also with the Data61, CSIRO, Eveleigh, NSW 2015, Australia. Dinh C. Nguyen is with the Department of Electrical and Computer Engineering, The University of Alabama in Huntsville Alabama, USA. Pubudu N. Pathirana is with School of Engineering and Built Environment, Deakin University, Waurn Ponds, VIC 3216, Australia.

What Products are Right for Your Program? In addition, the event presents a unique opportunity for attendees to see drones from multiple vendors all in one place and experience the entire drone ecosystem. The event provides access to renowned public safety experts – as well as Adorama's own Director of Technical Specialists James Bushey. "We're excited to come back to Madison, Alabama for our second annual drone demo day in partnership with the Madison PD. We've expanded the event to two days, bringing together public safety UAS thought leaders as well as representatives from manufacturers like DJI, Autel, BRINC, Parrot, Yuneec, senseFly, Pix4D and more, showing public safety agencies all that UAS has to offer," says Adorama's CJ Smith.

Common AI uses include intelligent assistants such as Siri, Alexa, or Google Assistant, but one Huntsville contractor is looking into its uses on the battlefield.

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. AI and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.

Modern Technology Solutions Inc. has opened a laboratory in Huntsville, Ala., for research and development of artificial intelligence-based technology platforms for the military sector. MTSI said Friday it looks to accomplish a holistic approach to AI application through the new lab along with the company's engineering and data analytics processes. Willie Maddox, manager of AI Lab, said the company aims to apply deep reinforcement learning to address challenges related to multiagent dynamic route planning. Alexandria, Va.-based MTSI offers engineering and technology services to government customers in the missile defense, cybersecurity, intelligence, unmanned and autonomous systems, aviation, space and homeland security areas.

Senior Technical Fellow PeopleTec, Inc. Huntsville, AL, USA ABSTRACT For predictive maintenance, we examine one of the largest public datasets for machine failures derived along with their corresponding precursors as error rates, historical part replacements and sensor inputs. To simplify the timeaccuracy comparisonbetween 27 different algorithms, we treat the imbalance between normal and failing states with nominal under-sampling. We identify 3 promising regression and discriminant algorithms with both higher accuracy (96%) and twenty-fold faster execution times than previous work. Because predictive maintenance success hinges on input features prior to prediction, we provide a methodology to rank-order feature importance and show that for this dataset, error counts prove more predictive than scheduled maintenance might imply solely based on more traditional factors such as machine age or last replacement times. INTRODUCTION Successful predictive maintenance is challenging not only because failures can prove multifactorial but also because maintenance forecasters often lack good training data.

Facebook has got big plans for a new $750 million data center in Huntsville, Alabama. On Thursday, the social networking giant announced it was building a new 970,000 square foot facility in Huntsville, a city in the northern part of the US state. "As a growing tech hub, Huntsville seemed like a natural fit for Facebook," the company wrote on a new Facebook post dedicated to the planned data center. "It also provides reliable access to renewable energy, strong local infrastructure, a great set of community partners, and very importantly, an outstanding pool of talent." A Facebook spokesperson confirmed to Business Insider that it is investing $750 million in the project.

Community annotation of biological entities with concepts from multiple bio-ontologies has created large and growing repositories of ontology-based annotation data with embedded implicit relationships among orthogonal ontologies. Development of efficient data mining methods and metrics to mine and assess the quality of the mined relationships has not kept pace with the growth of annotation data. In this study, we present a data mining method that uses ontology-guided generalization to discover relationships across ontologies along with a new interestingness metric based on information theory. We apply our data mining algorithm and interestingness measures to datasets from the Gene Expression Database at the Mouse Genome Informatics as a preliminary proof of concept to mine relationships between developmental stages in the mouse anatomy ontology and Gene Ontology concepts (biological process, molecular function and cellular component). In addition, we present a comparison of our interestingness metric to four existing metrics. Ontology-based annotation datasets provide a valuable resource for discovery of relationships across ontologies. The use of efficient data mining methods and appropriate interestingness metrics enables the identification of high quality relationships.

High performance DAQMAG2A Rugged Display Computer: High performance DAQMAG2A Rugged Display Computer from GE's Intelligent Platforms business are designed to minimize cost, risk and time-to-market for prime contractors, systems integrators and OEMs developing sophisticated video capture, processing and transmission applications with multiple inputs and outputs, it is qualified to the DO-160G Environmental Conditions and Test Procedures for Airborne Equipment standard. The DAQMAG2A Line Replaceable Unit (LRU) has already been successfully deployed by AgustaWestland on the AW189 and AW101 helicopters and by FLIR Systems Inc.GE's Intelligent Platforms business (NYSE: GE) is headquartered in Charlottesville, VA and part of GE Energy Management. The company's work in the military/aerospace segment, headquartered in Huntsville, AL, and Towcester, England, provides one of the industry's broadest ranges of high performance, rugged, SWaP-optimized embedded computing platforms. Backed by programs that provide responsive customer support and minimize long term cost of ownership for multi-year programs, GE's solutions are designed to help customers minimize program risk and cost, and to speed time-to-market. The high TRL (Technology Readiness Level 9) of the DAQMAG2A means systems integrators can select it with confidence to concentrate on solving more important challenges such as integration into the higher level system, application development/port and so on.

The mail sorting, folding, and inserting mobile personal communications goal is to facilitate the design of exhaust equipment, has implemented an expert network that will permit any mufflers of inlet manifolds in system solution at the core of its type of wireless telephone transmission--voice, hours instead of days. Air Force Manufacturing Technology service data from which common GKIS Intelligent Systems (Houston, Directorate (MANTECH) (Wright-Patterson knowledge--such as service procedures, Tex.) has developed the It is process to prove out and select Intergraph (Huntsville, Ala.), a designed to mine environmental optimal new concepts. The company has Industries (Phenix City, Ala.), a decisions related to advanced launched Project Solomon to upgrade textile manufacturer, is using an automated strike-warfare technology. The Workers' Compensation Fund uses advanced vision technology, neural knowledge-based software. The system compares workers' to develop a fuzzy logic-based solution off-quality production.