Raymond P. Burkholder - Things I Do

TSPP: A Unified Benchmarking Tool for Time-series Forecasting

While machine learning has witnessed significant advancements, the emphasis has largely been on data acquisition and model creation. However, achieving a comprehensive assessment of machine learning solutions in real-world settings necessitates standardization throughout the entire pipeline. This need is particularly acute in time series forecasting, where diverse settings impede meaningful comparisons between various methods. To bridge this gap, we propose a unified benchmarking framework that exposes the crucial modelling and machine learning decisions involved in developing time series forecasting models. This framework fosters seamless integration of models and datasets, aiding both practitioners and researchers in their development efforts. We benchmark recently proposed models within this framework, demonstrating that carefully implemented deep learning models with minimal effort can rival gradient-boosting decision trees requiring extensive feature engineering and expert knowledge.

HPX -- An open source C++ Standard Library for Parallelism and Concurrency

To achieve scalability with today's heterogeneous HPC resources, we need a dramatic shift in our thinking; MPI+X is not enough. Asynchronous Many Task (AMT) runtime systems break down the global barriers imposed by the Bulk Synchronous Programming model. HPX is an open-source, C++ Standards compliant AMT runtime system that is developed by a diverse international community of collaborators called The Ste||ar Group. HPX provides features which allow application developers to naturally use key design patterns, such as overlapping communication and computation, decentralizing of control flow, oversubscribing execution resources and sending work to data instead of data to work. The Ste||ar Group comprises physicists, engineers, and computer scientists; men and women from many different institutions and affiliations, and over a dozen different countries. We are committed to advancing the development of scalable parallel applications by providing a platform for collaborating and exchanging ideas. In this paper, we give a detailed description of the features HPX provides and how they help achieve scalability and programmability, a list of applications of HPX including two large NSF funded collaborations (STORM, for storm surge forecasting; and STAR (OctoTiger) an astro-physics project which runs at 96.8% parallel efficiency on 643,280 cores), and we end with a description of how HPX and the Ste||ar Group fit into the open source community.

TimeGraphs: Graph-based Temporal Reasoning

Many real-world systems exhibit temporal, dynamic behaviors, which are captured as time series of complex agent interactions. To perform temporal reasoning, current methods primarily encode temporal dynamics through simple sequence-based models. However, in general these models fail to efficiently capture the full spectrum of rich dynamics in the input, since the dynamics is not uniformly distributed. In particular, relevant information might be harder to extract and computing power is wasted for processing all individual timesteps, even if they contain no significant changes or no new information. Here we propose TimeGraphs, a novel approach that characterizes dynamic interactions as a hierarchical temporal graph, diverging from traditional sequential representations. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. Adopting a self-supervised method, TimeGraphs constructs a multi-level event hierarchy from a temporal input, which is then used to efficiently reason about the unevenly distributed dynamics. This construction process is scalable and incremental to accommodate streaming data. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset. The results demonstrate both robustness and efficiency of TimeGraphs on a range of temporal reasoning tasks. Our approach obtains state-of-the-art performance and leads to a performance increase of up to 12.2% on event prediction and recognition tasks over current approaches. Our experiments further demonstrate a wide array of capabilities including zero-shot generalization, robustness in case of data sparsity, and adaptability to streaming data flow.

Analysis of frequent trading effects of various machine learning models, EconPapers

In recent years, high-frequency trading has emerged as a crucial strategy in stock trading. This study aims to develop an advanced high-frequency trading algorithm and compare the performance of three different mathematical models: the combination of the cross-entropy loss function and the quasi-Newton algorithm, the FCNN model, and the vector machine. The proposed algorithm employs neural network predictions to generate trading signals and execute buy and sell operations based on specific conditions. By harnessing the power of neural networks, the algorithm enhances the accuracy and reliability of the trading strategy. To assess the effectiveness of the algorithm, the study evaluates the performance of the three mathematical models. The combination of the cross-entropy loss function and the quasi-Newton algorithm is a widely utilized logistic regression approach. The FCNN model, on the other hand, is a deep learning algorithm that can extract and classify features from stock data. Meanwhile, the vector machine is a supervised learning algorithm recognized for achieving improved classification results by mapping data into high-dimensional spaces. By comparing the performance of these three models, the study aims to determine the most effective approach for high-frequency trading. This research makes a valuable contribution by introducing a novel methodology for high-frequency trading, thereby providing investors with a more accurate and reliable stock trading strategy.

Measure of Dependence for Financial Time-Series, EconPapers

Assessing the predictive power of both data and models holds paramount significance in time-series machine learning applications. Yet, preparing time series data accurately and employing an appropriate measure for predictive power seems to be a non-trivial task. This work involves reviewing and establishing the groundwork for a comprehensive analysis of shaping time-series data and evaluating various measures of dependence. Lastly, we present a method, framework, and a concrete example for selecting and evaluating a suitable measure of dependence.

C++ Design Patterns for Low-latency Applications Including High-frequency Trading

This work aims to bridge the existing knowledge gap in the optimisation of latency-critical code, specifically focusing on high-frequency trading (HFT) systems. The research culminates in three main contributions: the creation of a Low-Latency Programming Repository, the optimisation of a market-neutral statistical arbitrage pairs trading strategy, and the implementation of the Disruptor pattern in C++. The repository serves as a practical guide and is enriched with rigorous statistical benchmarking, while the trading strategy optimisation led to substantial improvements in speed and profitability. The Disruptor pattern showcased significant performance enhancement over traditional queuing methods. Evaluation metrics include speed, cache utilisation, and statistical significance, among others. Techniques like Cache Warming and Constexpr showed the most significant gains in latency reduction. Future directions involve expanding the repository, testing the optimised trading algorithm in a live trading environment, and integrating the Disruptor pattern with the trading algorithm for comprehensive system benchmarking. The work is oriented towards academics and industry practitioners seeking to improve performance in latency-sensitive applications.

Integrating Tick-level Data and Periodical Signal for High-frequency Market Making

We focus on the problem of market making in high-frequency trading. Market making is a critical function in financial markets that involves providing liquidity by buying and selling assets. However, the increasing complexity of financial markets and the high volume of data generated by tick-level trading makes it challenging to develop effective market making strategies. To address this challenge, we propose a deep reinforcement learning approach that fuses tick-level data with periodic prediction signals to develop a more accurate and robust market making strategy. Our results of market making strategies based on different deep reinforcement learning algorithms under the simulation scenarios and real data experiments in the cryptocurrency markets show that the proposed framework outperforms existing methods in terms of profitability and risk management.

Trade Co-occurrence, Trade Flow Decomposition, and Conditional Order Imbalance in Equity Markets

The time proximity of high-frequency trades can contain a salient signal. In this paper, we propose a method to classify every trade, based on its proximity with other trades in the market within a short period of time, into five types. By means of a suitably defined normalized order imbalance associated to each type of trade, which we denote as conditional order imbalance (COI), we investigate the price impact of the decomposed trade flows. Our empirical findings indicate strong positive correlations between contemporaneous returns and COIs. In terms of predictability, we document that associations with future returns are positive for COIs of trades which are isolated from trades of stocks other than themselves, and negative otherwise. Furthermore, trading strategies which we develop using COIs achieve conspicuous returns and Sharpe ratios, in an extensive experimental setup on a universe of 457 stocks using daily data for a period of three years.

Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis - accepted at AIOPS workshop @ICDM 2023

The realm of AIOps is transforming IT landscapes with the power of AI and ML. Despite the challenge of limited labeled data, supervised models show promise, emphasizing the importance of leveraging labels for training, especially in deep learning contexts. This study enhances the field by introducing a taxonomy for log anomalies and exploring automated data labeling to mitigate labeling challenges. It goes further by investigating the potential of diverse anomaly detection techniques and their alignment with specific anomaly types. However, the exploration doesn't stop at anomaly detection. The study envisions a future where root cause analysis follows anomaly detection, unraveling the underlying triggers of anomalies. This uncharted territory holds immense potential for revolutionizing IT systems management. In essence, this paper enriches our understanding of anomaly detection, and automated labeling, and sets the stage for transformative root cause analysis. Together, these advances promise more resilient IT systems, elevating operational efficiency and user satisfaction in an ever-evolving technological landscape.

A Roadmap towards Intelligent Operations for Reliable Cloud Computing Systems

The increasing complexity and usage of cloud systems have made it challenging for service providers to ensure reliability. This paper highlights two main challenges, namely internal and external factors, that affect the reliability of cloud microservices. Afterward, we discuss the data-driven approach that can resolve these challenges from four key aspects: ticket management, log management, multimodal analysis, and the microservice resilience testing approach. The experiments conducted show that the proposed data-driven AIOps solution significantly enhances system reliability from multiple angles.

A Systematic Mapping Study in AIOps

IT systems of today are becoming larger and more complex, rendering their human supervision more difficult. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to AI and Big Data. However, past AIOps contributions are scattered, unorganized and missing a common terminology convention, which renders their discovery and comparison impractical. In this work, we conduct an in-depth mapping study to collect and organize the numerous scattered contributions to AIOps in a unique reference index. We create an AIOps taxonomy to build a foundation for future contributions and allow an efficient comparison of AIOps papers treating similar problems. We investigate temporal trends and classify AIOps contributions based on the choice of algorithms, data sources and the target components. Our results show a recent and growing interest towards AIOps, specifically to those contributions treating failure-related tasks (62%), such as anomaly detection and root cause analysis.

AIOps with Data, Analytics, and Intelligent Automation: A Foundational Capability for Modern IT Operations - sponsored by BMC but has useful general background information

ls -srSk ~/Downloads/

• s - size in blocks
• r - reverse order (largest at bottom)
• k - 1024 byte block size
• S - sort by size

ls -d ~/Downloads/*

• Shows path to all files in the directory

unstruct.ai - An AI-Enabled, Open-Source Alternative to PagerDuty. With UnStruct.AI, you're not just getting another cybersecurity tool – you're getting an all-in-one powerhouse. Instead of juggling multiple tools and racking up costs for each, get everything under one roof. Whether it's for paging, incident response, analysis, status updates, SLO/uptime monitoring, or a sprinkle of tech magic.

Hacker News Perspective

According to the slides from a 2023 Linux Storage, Filesystem, Memory-Management and BPF Summit talk, guests operating through the netkit device (which was called "meta" at that time) are able to attain TCP data-transmission rates that are just as high as can be had by running directly on the host. The performance penalty for running within a guest has, in other words, been entirely removed.

Chamberlain blocks smart garage door opener from working with smart homes - Chamberlain Group recently made the decision to prevent unauthorized usage of our myQ ecosystem through third-party apps.

Whenever someone says: I can control my garage door from my phone! I point out that what they've really done is:

• ceded all control of their garage door to another entity
• they request action(s) from that entity
• hope the other entity allows that action

One popular fix for people with the MyQ problem is ratgo - You plug a board into the opener and it talks to you locally with no cloud involvement.

• Learn Nokia SR Linux - Service Router Linux
• SR tutorial with containerlab
• Microsoft has crafted a switch OS on Debian Linux

• a href="https://www.first.org/resources/papers/aa-dec2021/Protective-DNS-a-Boris-Slides.pdf" target=_blank>Protective DNS – Why It Matters, How to Deploy It On-prem, and to Take Control and Defense back
• Threat blocking
• Introducing 1.1.1.1 for Families
• Cybercrime Supply Chain 2023: Measurements and Assessments of Cyber Attack Resources and Where Criminals Acquire Them

At one point Firefox was going to send everything to their favorite DoH resolver but they got a great deal of pushback from people who pointed out that they had policies on their networks and they'd have to ban Firefox. Firefox responded with a lame hack where you can tell your cache to respond to some name and if so Firefox will use your resolver.

To install rabbitmq and enable mqtt extensions:

• apt install rabbitmq-server
• rabbitmq-plugins enable rabbitmq_mqtt

To add a user and set permissions for an mqtt client, only one client can use the connection at once

# username and password are both "mqtt-test"
rabbitmqctl add_user mqtt-test mqtt-test
rabbitmqctl set_permissions -p / mqtt-test ".*" ".*" ".*"
rabbitmqctl set_user_tags mqtt-test management   

To disable anonymous attachments:

root@rabbitmq:/# cat /etc/rabbitmq/rabbitmq.conf
mqtt.allow_anonymous = false

how to do a dynamic parser in X3?

The presentation went well, but as always with a large surface area library like Spirit it went on for too long IMO (2 hrs). This was similar to what happened when I presented Spirit 2.x almost 10 yrs ago. If I were to present on this again, I would split the topic into two talks:

• A background on recursive variant datatypes and visitor pattern and dipping into the Fusion library for adapting arbitrary application structures to Fusion tuples.
• Spirit X3 parsing w/annotations and error handling.

I try to present from real code in the IDE as much as possible so that we're looking at real code. I almost always write my own examples with the library I'm presenting and put them on github. The exception is when the library comes with an example that is everything I want to talk about .

I cribbed quite a bit from the spirit x3 fun example presented by Michael Caisse a few years back, but I did extend the AST to handle imaginary numbers.

How to use SkyScanner to find Cheap Flights

Request: Or people simple do not want to share own email, as it is not related for delivery, but required by the form. Thus they fill semi random email like string.

Response: That is what mailinator is for, or the temporary addresses offered by several browser plugins, or if you know they're going to use it to spam you, abuse@ their domain. nobody@example.com works as well, and 1-900-976-1212 for a "required" phone number.

Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud

Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers want to know when such events happen and how serious the degradation is, to perform interference-aware migrations and alleviate the problem. However, virtual machines (VM) in Infrastructure-as-a-Service public clouds are black-boxes to providers, where application-level performance information cannot be acquired. This makes performance monitoring intensely challenging as cloud providers can only rely on low-level metrics such as CPU usage and hardware counters.

We propose a novel machine learning framework, Alioth, to monitor the performance degradation of cloud applications. To feed the data-hungry models, we first elaborate interference generators and conduct comprehensive co-location experiments on a testbed to build Alioth-dataset which reflects the complexity and dynamicity in real-world scenarios. Then we construct Alioth by (1) augmenting features via recovering low-level metrics under no interference using denoising auto-encoders, (2) devising a transfer learning model based on domain adaptation neural network to make models generalize on test cases unseen in offline training, and (3) developing a SHAP explainer to automate feature selection and enhance model interpretability. Experiments show that Alioth achieves an average mean absolute error of 5.29% offline and 10.8% when testing on applications unseen in the training stage, outperforming the baseline methods. Alioth is also robust in signaling quality-of-service violation under dynamicity. Finally, we demonstrate a possible application of Alioth's interpretability, providing insights to benefit the decision-making of cloud operators. The dataset and code of Alioth have been released on GitHub.