To detect anomalies in real-world graphs, such as social, email, and financial networks, various approaches have been developed. While they typically assume static input graphs, most real-world graphs grow over time, naturally represented as edge streams. In this context, we aim to achieve three goals: (a) instantly detecting anomalies as they occur, (b) adapting to dynamically changing states, and (c) handling the scarcity of dynamic anomaly labels. In this paper, we propose SLADE (Self-supervised Learning for Anomaly Detection in Edge Streams) for rapid detection of dynamic anomalies in edge streams, without relying on labels. SLADE detects the shifts of nodes into abnormal states by observing deviations in their interaction patterns over time. To this end, it trains a deep neural network to perform two self-supervised tasks: (a) minimizing drift in node representations and (b) generating long-term interaction patterns from short-term ones. Failure in these tasks for a node signals its deviation from the norm. Notably, the neural network and tasks are carefully designed so that all required operations can be performed in constant time (w.r.t. the graph size) in response to each new edge in the input stream. In dynamic anomaly detection across four real-world datasets, SLADE outperforms nine competing methods, even those leveraging label supervision.

Decompilation is a well-studied area with numerous high-quality tools available. These are frequently used for security tasks and to port legacy code. However, they regularly generate difficult-to-read programs and require a large amount of engineering effort to support new programming languages and ISAs. Recent interest in neural approaches has produced portable tools that generate readable code. However, to-date such techniques are usually restricted to synthetic programs without optimization, and no models have evaluated their portability. Furthermore, while the code generated may be more readable, it is usually incorrect. This paper presents SLaDe, a Small Language model Decompiler based on a sequence-to-sequence transformer trained over real-world code. We develop a novel tokenizer and exploit no-dropout training to produce high-quality code. We utilize type-inference to generate programs that are more readable and accurate than standard analytic and recent neural approaches. Unlike standard approaches, SLaDe can infer out-of-context types and unlike neural approaches, it generates correct code. We evaluate SLaDe on over 4,000 functions from AnghaBench on two ISAs and at two optimizations levels. SLaDe is up to 6 times more accurate than Ghidra, a state-of-the-art, industrial-strength decompiler and up to 4 times more accurate than the large language model ChatGPT and generates significantly more readable code than both.

An Echo Dot owner claims that Amazon's Alexa assistant has started calling him a's*******' whenever he asks the personal assistant to play him music. Micheal Slade, 29, was reportedly shocked when his Echo Dot speaker began to swear at him following his cancellation of his Amazon Prime subscription. The incident has reportedly left Amazon engineers puzzled -- with the tech firm offering Mr Slade gift cards and a year of free Prime membership in compensation. Software is available for the Echo Dot speaker that can make Alexa curse -- but it is unclear whether someone might have deliberately uploaded this to the device. An Echo Dot owner claims that Amazon's Alexa assistant has started calling him a's*******' whenever he asks the personal assistant to play him music Online business owner and Cwmbran, South Wales resident Michael Slade, 29, said that the trouble began the day after called Amazon to cancel his subscription to their Prime subscription service.

Black Friday is approaching and retailers will be keen to maximise sales with highly targeted advertising throughout the weekend-long bargain fest. That will make it an equally busy time for Location Sciences Group PLC (LON:LSAI). The AIM-listed business has built a platform that gives advertisers a whole range of data on how effective an ad has been in getting people into shops. Products cover out of home ads (posters), digital promotions on smartphones and tablets, raw data on consumer movements or reports with insights and analysis. Kitchen maker Wren, for example, uses it to link footfall at its stores with its marketing activity.