Pangolin is the most popular tool for SARS-CoV-2 lineage assignment. During COVID-19, healthcare professionals and policymakers required accurate and timely lineage assignment of SARS-CoV-2 genomes for pandemic response. Therefore, tools such as Pangolin use a machine learning model, pangoLEARN, for fast and accurate lineage assignment. Unfortunately, machine learning models are susceptible to adversarial attacks, in which minute changes to the inputs cause substantial changes in the model prediction. We present an attack that uses the pangoLEARN architecture to find perturbations that change the lineage assignment, often with only 2–3 base pair changes. The attacks we carried out show that pangolin is vulnerable to adversarial attack, with success rates between 0.98 and 1 for sequences from non-VoC lineages when pangoLEARN is used for lineage assignment. The attacks we carried out are almost never successful against VoC lineages because pangolin uses Usher and Scorpio – the non-machine-learning alternative methods for VoC lineage assignment. A malicious agent could use the proposed attack to fake or mask outbreaks or circulating lineages. Developers of software in the field of microbial genomics should be aware of the vulnerabilities of machine learning based models and mitigate such risks.
User authentication based on username and password is the most common means to enforce access control. This form of access restriction is prone to hacking since stolen usernames and passwords can be exploited to impersonate legitimate users in order to commit malicious activity. Biometric authentication incorporates additional user characteristics such as the manner by which the keyboard is used in order to identify users. We introduce a novel approach for user authentication based on the keystroke dynamics of the password entry. A classifier is tailored to each user and the novelty lies in the manner by which the training set is constructed. Specifically, only the keystroke dynamics of a small subset of users, which we refer to as representatives, is used along with the password entry keystroke dynamics of the examined user. The contribution of this approach is twofold: it reduces the possibility of overfitting, while allowing scalability to a high volume of users. We propose two strategies to construct the subset for each user. The first selects the users whose keystroke profiles govern the profiles of all the users, while the second strategy chooses the users whose profiles are the most similar to the profile of the user for whom the classifier is constructed. Results are promising reaching in some cases 90% area under the curve. In many cases, a higher number of representatives deteriorate the accuracy which may imply overfitting. An extensive evaluation was performed using a dataset containing over 780 users.
This paper examines the attribute decomposition approach with simple Bayesian combination for dealing with classification problems that contain high number of attributes and moderate numbers of records. According to the attribute decomposition approach, the set of input attributes is automatically decomposed into several subsets. A classification model is built for each subset, then all the models are combined using simple Bayesian combination. This paper presents theoretical and practical foundation for the attribute decomposition approach. A greedy procedure, called D-IFN, is developed to decompose the input attributes set into subsets and build a classification model for each subset separately. The results achieved in the empirical compart. son testing with well-known classification methods (like C4.5) indicate the superiority of the decomposition approach.
No abstract is provided for this article.
No abstract is provided for this article.
Discovering the existence of universal adversarial perturbations had large theoretical and practical impacts on the field of adversarial learning. In the text domain, most universal studies focused on adversarial prefixes which are added to all texts. However, unlike the vision domain, adding the same perturbation to different inputs results in noticeably unnatural inputs. Therefore, we introduce a new universal adversarial setup – a universal adversarial policy, which has many advantages of other universal attacks but also results in valid texts – thus making it relevant in practice. We achieve this by learning a single search policy over a predefined set of semantics preserving text alterations, on many texts. This formulation is universal in that the policy is successful in finding adversarial examples on new texts efficiently. Our approach uses text perturbations which were extensively shown to produce natural attacks in the non-universal setup (specific synonym replacements). We suggest a strong baseline approach for this formulation which uses reinforcement learning. Its ability to generalise (from as few as 500 training texts) shows that universal adversarial patterns exist in the text domain as well.
No abstract is provided for this article.
Dynamic temporal graphs evolve over time, adding and removing nodes and edges between time snapshots. The tasks performed on such graphs are diverse and include detecting temporal trends, finding graph-to-graph similarities, and graph visualization and clustering. For all these tasks, it is necessary to embed the entire graph in a low-dimensional space by using graph-level representations instead of the more common node-level representations. This embedding requires handling the appearance of new nodes over time as well as capturing temporal patterns of the entire graph. Most existing methods perform temporal node embeddings and focus on different methods of aggregating them for a graph-based representation. In this work, we propose an end-to-end architecture that captures both the node embeddings and their influence in a structural context during a specific time period of the graph. We present GraphERT (Graph Embedding Representation using Transformers), a novel approach to temporal graph-level embeddings. Our method pioneers the use of Transformers to seamlessly integrate graph structure learning with temporal analysis. By employing a masked language model on sequences of graph random walks, together with a novel temporal classification task, our model not only comprehends the intricate graph dynamics but also unravels the temporal significance of each node and path. This novel training paradigm empowers GraphERT to capture the essence of both the structural and temporal aspects of graphs, surpassing state-of-the-art approaches across multiple tasks on real-world datasets.
No abstract is provided for this article.
No abstract is provided for this article.
Gradient Boosted Decision Trees (GBDT) is a widely used machine learning algorithm, which obtains state-of-the-art results on many machine learning tasks. In this paper we introduce a method for obtaining better results, by augmenting the features in the dataset between the iterations of GBDT. We explore a number of augmentation methods: training an Artificial Neural Network (ANN) and extracting features from it's last hidden layer (supervised), and rotating the feature-space using unsupervised methods such as PCA or Random Projection (RP). These variations on GBDT were tested on 20 classification tasks, on which all of them outperformed GBDT and previous related work.
