Our solution is application agnostic and relies only on the data collected for diagnosis. For the evaluation, we compare the proposed solution with a modified version of the PC algorithm and the state-of-the-art for root cause analysis.

Among the thriving ecosystem of cloud computing and the proliferation of Large Language Model (LLM)-based code generation tools, there is a lack of benchmarking for code generation in cloud-native applications. In response to this need, we present CloudEval-YAML, a practical benchmark for cloud configuration generation. CloudEval-YAML tackles the diversity challenge by focusing on YAML, the de facto standard of numerous cloud-native tools. We develop the CloudEval-YAML benchmark with practicality in mind: the dataset consists of hand-written problems with unit tests targeting practical scenarios. We further enhanced the dataset to meet practical needs by rephrasing questions in a concise, abbreviated, and bilingual manner. The dataset consists of 1011 problems that take more than 1200 human hours to complete. To improve practicality during evaluation, we build a scalable evaluation platform for CloudEval-YAML that achieves a 20 times speedup over a single machine. To the best of our knowledge, the CloudEval-YAML dataset is the first hand-written dataset targeting cloud-native applications. We present an in-depth evaluation of 12 LLMs, leading to a deeper understanding of the problems and LLMs, as well as effective methods to improve task performance and reduce cost.

In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (VoIP) applications. Our approach involves adapting the DNS 2020 models to the specific acoustic characteristics of VoIP communications, which includes distortion and artifacts caused by compression, transmission, and platform-specific processing. To this end, we propose a multi-task learning framework for VoIP-DNS that jointly optimizes noise suppression and VoIP-specific acoustics for speech enhancement. We evaluate our approach on a diverse VoIP scenarios and show that it outperforms both industry performance and state-of-the-art methods for speech enhancement on VoIP applications. Our results demonstrate the potential of models trained on DNS-2020 to be improved and tailored to different VoIP platforms using VoIP-DNS, whose findings have important applications in areas such as speech recognition, voice assistants, and telecommunication.

Nightfall AI, a startup providing cloud data loss prevention services, today announced that it raised $40 million in Series B financing from investors including WestBridge Capital, Venrock, Bain Capital Ventures and -- for some reason -- athletes and celebrities including Paul Rudd, Drew Brees and Josh Childress. CEO Isaac Madan says that the proceeds will be put toward doubling Nightfall's 60-person headcount, scaling the platform to more customers and markets, and expanding Nightfall's partner ecosystem. Isaac was previously a VC investor at Venrock, where he focused on early-stage investments in software as a service, security and machine learning. Rohan was one of the founding engineers at Uber Eats, where he designed and built software to grow the platform's footprint. Madan says he and Sathe were inspired to launch Nightfall by Sathe's personal experiences with data breaches arising from poor "data security hygiene."

The AV industry is all about innovation. But for the past year, our creative energies have been devoted not to shaping the future, but solving the problems of the present. But larger trends are nonetheless having incremental influence over the direction we're heading. As the powerful capabilities of things like 5G connectivity, artificial intelligence, and cloud computing direct the technology world as a whole, how is pro AV incorporating them to create a more capable and intuitive future? Related: Tech Trends That WON'T Happen in 2021 We reached out to leading AV manufacturers to find out how prominent emerging trends are playing into their product development roadmaps.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Moving data between applications and warehousing data for analysis are recurring issues for app builders, data engineers, and IT teams. But we all know our businesses can benefit in significant ways if we are smart with our data. There are plenty of options for moving data now.

Cloud providers are facing a complex problem in configuring software applications ready for deployment on their infrastructures. Hierarchical Task Network (HTN) planning can provide effective means to solve such deployment problems. We present an HTN planning domain that models deployment problems as found in realistic Cloud environments.

The year 2020 has inarguably been an unprecedented year for humanity. With a global pandemic upending people's lives, the cyber world has been no less affected. On the upside, the virus-enforced digital transition in nearly all aspects of our lives has created massive momentum and scale for the uptake of cyber technologies. However, the downside is the increased opportunities this creates for unethical hackers and cyber criminals. In this backdrop, how is the cyber security landscape going to unfold this year?

We propose a simple yet effective policy for the predictive auto-scaling of horizontally scalable applications running in cloud environments, where compute resources can only be added with a delay, and where the deployment throughput is limited. Our policy uses a probabilistic forecast of the workload to make scaling decisions dependent on the risk aversion of the application owner. We show in our experiments using real-world and synthetic data that this policy compares favorably to mathematically more sophisticated approaches as well as to simple benchmark policies.

For many organizations, Cloud Computing and the use of Cloud applications is the new norm. Employees are accessing their email and files while on the road--using both personal and business devices--because the Cloud makes data available from anywhere there is an internet connection. At the same time, organizations are falling victim to high-profile data breaches, exposing employee and customer personally identifiable information (PII) and payment card industry (PCI) data. This data is now being used by hackers in account takeover attacks, phishing schemes and more. This means that the same Cloud applications giving employees the freedom to work and access company data from anywhere now pose a problem for traditional security techniques.