A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Multimodal deep learning improves flaw detection in software programs.
Improved software flaw detection using NAS on multimodal DL models.
Recent anomaly detection benchmarks are flawed, potentially misleading progress.
This paper critiques flawed MVTS anomaly detection evaluation methods and proposes a simple baseline.
Detects unusual inputs to neural networks to prevent flawed predictions.
Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead. In this paper, we introduce PROPEDEUTICA, a framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) te…
The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily basis. This calls for machine learning methods for vulnerability detection. Deep learning is attractive for this purpose because it a…
Flawed groups are shown to include all finitely generated groups isomorphic to free products of nilpotent groups.
Detects outliers in VAE latent space by identifying vacant holes.
This research identifies flaws in drift detection methods and creates adversarial data streams to exploit them.
Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ …
We discuss promising recent contributions on quantifying feature relevance using Shapley values, where we observed some confusion on which probability distribution is the right one for dropped features. We argue that the confusion is based on not carefully distinguishing between observational and interventional conditi…
A machine learning environment for detecting autonomous vehicle corner cases.
Corporate bond factor research is flawed due to measurement errors and ex-post filtering.
Machine learning models for COVID-19 detection and prognosis from chest images are flawed and unreliable.
Improving software quality through effective organizational learning.
Research proposes an ensemble learning model for efficient software defect prediction.
The paper highlights issues with fixed point claims in digital images.
The paper highlights issues in fixed point claims in digital topology.
The recent increase in the scale and complexity of software systems has introduced new challenges to the time series monitoring and anomaly detection process. A major drawback of existing anomaly detection methods is that they lack contextual information to help stakeholders identify the cause of anomalies. This proble…
The F-measure or F-score is one of the most commonly used single number measures in Information Retrieval, Natural Language Processing and Machine Learning, but it is based on a mistake, and the flawed assumptions render it unsuitable for use in most contexts! Fortunately, there are better alternatives.
CK simplifies ML model deployment and reproducibility with open APIs and DevOps.
Study flaws in generative model evaluation metrics, especially for diffusion models.
New method improves variable importance in random forests.
Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth. Recently, a large number of studies have reported near-perfect results to distinguish between recordings of patients that will deliver term or p…
Systematic review of ML models for detecting social media deception.
In financial field, a robust software system is of vital importance to ensure the smooth operation of financial transactions. However, many financial corporations still depend on operators to identify and eliminate the system failures when financial software systems break down. This traditional operation method is time…
With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like machine learning detection. Although powerful for conviction of malicious artifacts, these methods do …
Descriptive comments play a crucial role in the software engineering process. They decrease development time, enable better bug detection, and facilitate the reuse of previously written code. However, comments are commonly the last of a software developer's priorities and are thus either insufficient or missing entirel…
MISIM improves code similarity systems with neural learning.
We present in this paper experiments on Table Recognition in hand-written registry books. We first explain how the problem of row and column detection is modeled, and then compare two Machine Learning approaches (Conditional Random Field and Graph Convolutional Network) for detecting these table elements. Evaluation wa…
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine l…
Many software analysis methods have come to rely on machine learning approaches. Code segmentation - the process of decomposing source code into meaningful blocks - can augment these methods by featurizing code, reducing noise, and limiting the problem space. Traditionally, code segmentation has been done using syntact…
Ethereum smart contracts have recently drawn a considerable amount of attention from the media, the financial industry and academia. With the increase in popularity, malicious users found new opportunities to profit by deceiving newcomers. Consequently, attackers started luring other attackers into contracts that seem …
OS-ELM outperforms other classifiers in software defect prediction.
Develops methods for integrating multivariate normals and computing classification measures.
In this paper, we propose a new feature extraction technique for program execution logs. First, we automatically extract complex patterns from a program's behavior graph. Then, we embed these patterns into a continuous space by training an autoencoder. We evaluate the proposed features on a real-world malicious softwar…
This paper is being withdrawn by the author due a serious flaw.
A new method detects changes in multivariate data using random forests.
Trojans can be inserted into deep neural networks without altering training data.
New flaw found in SAP defense, reducing its effectiveness to 0.1%.
Malware constitutes a major global risk affecting millions of users each year. Standard algorithms in detection systems perform insufficiently when dealing with malware passed through obfuscation tools. We illustrate this studying in detail an open source metamorphic software, making use of a hybrid framework to obtain…
Thousands of security vulnerabilities are discovered in production software each year, either reported publicly to the Common Vulnerabilities and Exposures database or discovered internally in proprietary code. Vulnerabilities often manifest themselves in subtle ways that are not obvious to code reviewers or the develo…
This paper analyzes CNNs for malware detection in cloud IaaS.
A new method detects anomalies in multivariate streams without unit dependence.
Study examines flaws in probing LLMs' knowledge and introduces a new method.
The paper proves deep learning can be robust with certain loss functions.
System predicts ice formation to improve road safety.