Temporal event data are commonly encountered in software applications across a wide range of domains, from IT telemetry and system logs to business process automation. Temporal event data in software applications carry information in various forms: both structured and unstructured, and with both regular and irregular occurrence frequency, making the representation of temporal event data challenging. Further chal-lenges come from the diversity in terms of the scale and volume of events that need to be summarized by the representation. We propose a general and unified approach to handle temporal event data for the purpose of learning a predictive transformer model. Our approach subsumes many existing techniques and is relevant across diverse application domains. Further, our approach can be used with many transformer architectures from the original transformer to recent, more complex architectures. Through a simple extension of the transformer training procedure, we enable the model to provide explanations of its predicted events. Empirically, we show that the proposed approach achieves state-of-the-art performance on software-related tasks coming from a wide variety of application domains.

A Bitcoin Generator Scam (BGS) is a type of cyberattack in which scammers promise to provide individuals with free cryptocurrencies if they pay a mining fee. Although graph neural networks (GNNs) have been used for detecting other cryptocurrency frauds, the usefulness of these methods for BGS detection has not been studied. In this paper, we carry out extensive experiments to assess the use of both standard machine learning (ML) methods and GNNs to detect Bitcoin transactions associated with activities stemming from Bitcoin Generator Scams. We observe that the over-smoothing problem exists in GNNs designed for BGS detection and show that Random Walk Positional Encoding (RWPE) allows representing long-range interactions between far-away transactions in GNNs without causing over-smoothing. We show that the General, Powerful, Scalable (GPS) Graph Transformer with RWPE outperforms both GNN and ML based state-of-the-art fraud detection methods in Bitcoin Generator Scams. We also analyze the effectiveness of Breadth First Search (BFS) for graph sampling and show that it should not be used as it induces bias toward the subnetwork structure. We propose the Random First Search (RFS) sampling alternative and show that this is a more suitable solution.

Optimal transport is a framework for comparing measures whereby a cost is incurred for transporting one measure to another. Recent works have aimed to improve optimal transport plans through the introduction of various forms of structure. We introduce novel order constraints into the optimal transport formulation to allow for the incorporation of structure. We define an efficient method for obtaining explainable solutions to the new formulation that scales far better than standard approaches. The theoretical properties of the method are provided. We demonstrate experimentally that order constraints improve explainability using the e-SNLI (Stanford Natural Language Inference) dataset that includes human-annotated rationales as well as on several image color transfer examples.

We propose a framework, called neural-progressive hedging (NP), that leverages stochastic programming during the online phase of executing a reinforcement learning (RL) policy. The goal is to ensure feasibility with respect to constraints and risk-based objectives such as conditional value-at-risk (CVaR) during the execution of the policy, using probabilistic models of the state transitions to guide policy adjustments. The framework is particularly amenable to the class of sequential resource allocation problems since feasibility with respect to typical resource constraints cannot be enforced in a scalable manner. The NP framework provides an alternative that adds modest overhead during the online phase. Experimental results demonstrate the efficacy of the NP framework on two continuous real-world tasks: (i) the portfolio optimization problem with liquidity constraints for financial planning, characterized by non-stationary state distributions; and (ii) the dynamic repositioning problem in bike sharing systems, that embodies the class of supply-demand matching problems. We show that the NP framework produces policies that are better than deep RL and other baseline approaches, adapting to non-stationarity, whilst satisfying structural constraints and accommodating risk measures in the resulting policies. Additional benefits of the NP framework are ease of implementation and better explainability of the policies.

This paper presents a simple yet principled approach to boosting the robustness of the residual network (ResNet) that is motivated by a dynamical systems perspective. Namely, a deep neural network can be interpreted using a partial differential equation, which naturally inspires us to characterize ResNet based on an explicit Euler method. This consequently allows us to exploit the step factor h in the Euler method to control the robustness of ResNet in both its training and generalization. In particular, we prove that a small step factor h can benefit its training and generalization robustness during backpropagation and forward propagation, respectively. Empirical evaluation on real-world datasets corroborates our analytical findings that a small h can indeed improve both its training and generalization robustness.

The proliferation of wireless technologies in today's everyday life is one of the key drivers of the Internet of Things (IoT). In addition to being an enabler of connectivity, the vast penetration of wireless devices today gives rise to a secondary functionality as a means of tracking and localization of the devices themselves. Indeed, in order to discover and automatically connect to known Wi-Fi networks, mobile devices have to scan and broadcast the so-called probe requests on all available channels, which can be captured and analyzed in a non-intrusive manner. Thus, one of the key applications of this feature is the ability to track and analyze human behaviors in real-time directly from the patterns observed from their Wi-Fi-enabled devices. In this paper, we develop such a system to obtain these Wi-Fi signatures in a completely passive manner and use the Wi-Fi features it captures within a set of adaptive machine learning techniques to predict in real-time the expected length of stay (LOS) of the device owners at a specific location.

Given the changing dynamics of mobility patterns and rapid growth of cities, transport agencies seek to respond more rapidly to needs of the public with the goal of offering an effective and competitive public transport system. A more data-centric approach for transport planning is part of the evolution of this process. In particular, the vast penetration of mobile phones provides an opportunity to monitor and derive insights on transport usage. Real time and historical analyses of such data can give a detailed understanding of mobility patterns of people and also suggest improvements to current transit systems. On its own, however, mobile geolocation data has a number of limitations. We thus propose a joint telco-and-farecard-based learning approach to understanding urban mobility. The approach enhances telecommunications data by leveraging it jointly with other sources of real-time data. The approach is illustrated on the First- and last-mile problem as well as route choice estimation within a densely-connected train network.

We present a novel autonomic control system for high performance stream processing systems. The system uses bandwidth controls on incoming or outgoing streams to achieve a desired resource utilization balance among a set of concurrently executing stream processing tasks. We show that CPU prioritization and allocation mechanisms in schedulers and virtual machine managers are not sufficient to control such I/O-centric applications, and present an autonomic bandwidth control system that adaptively adjusts incoming and outgoing traffic rates to achieve system management goals. The system dynamically learns the bandwidth rate necessary to meet the system management goals using stochastic nonlinear optimization, and detects changes in the stream processing applications that require bandwidth adjustment. Our prototype Linux implementation is lightweight, has low overhead, and is capable of effectively managing stream processing applications.

We pose the question of how to encourage the resource sharing in a distributed, multi-provider environment, where each node, or provider, has local work but is able to accept additional work from other nodes/providers if there is available capacity. An instance of such an environment is found in content delivery, where. numerous, competing providers can work together if enough benefit is to be gained from doing so. We model individual provider behavior as essentially selfish, and then propose pricing schemes to exploit the selfishness to achieve system wide performance gains. We employ a game theoretic framework to analyze the problem, and come up with a time-dependent, noncooperative network equilibrium model. To influence the system towards the positive end of resource sharing, we suggest the creation of a monetary unit, tokens, whose exchange encourages a more efficient use of system-wide capacity, and whose effect is regulated by the pricing scheme in place. The impact of the different node behavior, model parameters, and pricing schemes in influencing the system performance is investigated through simulation. This framework can be combined with distance and round trip time to calibrate redirection behavior of distributed server environments.

No abstract available.

Design of e-commerce services that are competitive in a quickly responding market requires the analyses of prices and price structures. We develop a general model of an e-commerce market that allows us to analyze optimal price structures, both flat and usage-based. Based on the price structure of a major web hosting provider, we consider single-tier and two-tier (burst-rate) pricing, and our result suggests that the more complex two-tier structure may not be worth the marketing effort, as the firm's equilibrium profits will not increase through the use of this structure. An essential feature of our approach is that we model explicitly the spread of price-QoS tradeoffs across the end-user population.