West Midlands Police has admitted it overstated the evidence used to make the decision to ban Israeli fans from a match in Birmingham. Craig Guildford, its former chief constable, retired earlier this month after damning criticism of the ban on Maccabi Tel Aviv fans from the Europa League match against Aston Villa, last November. In newly released documents, the force also said we did not engage early enough with the local Jewish community, and indicated there was now a ban on AI use after its evidence included a match that did not take place. Furthermore, it said its operations would have lasted four days, involved multiple forces, and cost more than £5m, if 2,500 away fans had attended. The documents were released ahead of a public meeting on Tuesday, at which Police and Crime Commissioner for the West Midlands, Simon Foster, will discuss at his accountability and governance board, the decision to ban the Maccabi fans.

The chief constable of West Midlands Police has retired after damning criticism of a decision to ban Israeli fans from a match against Aston Villa. Craig Guildford's retirement was confirmed on Friday after both Downing Street and the home secretary said this week they had lost confidence in his leadership. He faced numerous calls to resign after apologising for providing incorrect evidence to MPs, which included the denial that AI was used in a report which led to the decision to ban Maccabi Tel Aviv fans from the game on 6 November. Announcing his retirement, Guildford, 52, did not offer an apology and blamed what he described as the political and media frenzy for his decision to step down. I have carefully considered my position and concluded that retirement is in the best interests of the organisation, myself and my family, he said.

Home Secretary Shabana Mahmood says she has lost confidence in West Midlands Police's chief constable after Israeli football fans were banned from a match against Aston Villa. Mahmood told MPs a damning review from the policing watchdog over the intelligence that led to Maccabi Tel Aviv fans being banned showed a failure of leadership. The force has apologised saying it did not deliberately distort evidence that was used by Birmingham's Safety Advisory Group for the 6 November game . Chief Constable Craig Guildford remains in post, but faces a meeting on 27 January to be questioned by Police and Crime Commissioner Simon Foster who has the authority to sack him. Mahmood told the Commons on Wednesday she intended to restore the power for home secretaries to dismiss chief constables who fail their communities.

When a police force is supposed to seek the truth and uphold the law, what happens when the evidence they present to officials and the public is, as Home Secretary Shabana Mahmood put it, exaggerated or untrue? The police inspectorate has concluded the leaders of West Midlands Police fell foul of confirmation bias. In simple terms, that means senior officers had already reached a decision and were looking for intelligence to justify it. The list of errors and inaccuracies set out in an independent review of the decision-making that led to fans of Israeli football club Maccabi Tel Aviv being banned from attending a fixture at Villa Park in November have been described by Mahmood as damning. They include: A report of a football match in an intelligence report produced using AI which never happened; a twice-repeated denial by senior police leaders to MPs that AI had not been relied on to produce the inaccurate report; the claim that local Jewish groups had been consulted on the move when they had not been; inaccurately presenting evidence from Dutch police reports from a previous fixture involving the club.

Identifying instrument activities within audio excerpts is vital in music information retrieval, with significant implications for music cataloging and discovery. Prior deep learning endeavors in musical instrument recognition have predominantly emphasized instrument classes with ample data availability. Recent studies have demonstrated the applicability of hierarchical classification in detecting instrument activities in orchestral music, even with limited fine-grained annotations at the instrument level. Based on the Hornbostel-Sachs classification, such a hierarchical classification system is evaluated using the MedleyDB dataset, renowned for its diversity and richness concerning various instruments and music genres. This work presents various strategies to integrate hierarchical structures into models and tests a new class of models for hierarchical music prediction. This study showcases more reliable coarse-level instrument detection by bridging the gap between detailed instrument identification and group-level recognition, paving the way for further advancements in this domain.

Reverberation is a key element in spatial audio perception, historically achieved with the use of analogue devices, such as plate and spring reverb, and in the last decades with digital signal processing techniques that have allowed different approaches for Virtual Analogue Modelling (VAM). The electromechanical functioning of the spring reverb makes it a nonlinear system that is difficult to fully emulate in the digital domain with white-box modelling techniques. In this study, we compare five different neural network architectures, including convolutional and recurrent models, to assess their effectiveness in replicating the characteristics of this audio effect. The evaluation is conducted on two datasets at sampling rates of 16 kHz and 48 kHz. This paper specifically focuses on neural audio architectures that offer parametric control, aiming to advance the boundaries of current black-box modelling techniques in the domain of spring reverberation.

This paper presents an examination of State Space Models (SSM) and Koopman-based deep learning methods for modelling the dynamics of both linear and non-linear stiff strings. Through experiments with datasets generated under different initial conditions and sample rates, we assess the capacity of these models to accurately model the complex behaviours observed in string dynamics. Our findings indicate that our proposed Koopman-based model performs as well as or better than other existing approaches in non-linear cases for long-sequence modelling. We inform the design of these architectures with the structure of the problems at hand. Although challenges remain in extending model predictions beyond the training horizon (i.e., extrapolation), the focus of our investigation lies in the models' ability to generalise across different initial conditions within the training time interval. This research contributes insights into the physical modelling of dynamical systems (in particular those addressing musical acoustics) by offering a comparative overview of these and previous methods and introducing innovative strategies for model improvement. Our results highlight the efficacy of these models in simulating non-linear dynamics and emphasise their wide-ranging applicability in accurately modelling dynamical systems over extended sequences.

Infinite impulse response filters are an essential building block of many time-varying audio systems, such as audio effects and synthesisers. However, their recursive structure impedes end-to-end training of these systems using automatic differentiation. Although non-recursive filter approximations like frequency sampling and frame-based processing have been proposed and widely used in previous works, they cannot accurately reflect the gradient of the original system. We alleviate this difficulty by re-expressing a time-varying all-pole filter to backpropagate the gradients through itself, so the filter implementation is not bound to the technical limitations of automatic differentiation frameworks. This implementation can be employed within audio systems containing filters with poles for efficient gradient evaluation. We demonstrate its training efficiency and expressive capabilities for modelling real-world dynamic audio systems on a phaser, time-varying subtractive synthesiser, and feed-forward compressor. We make our code and audio samples available and provide the trained audio effect and synth models in a VST plugin at https://diffapf.github.io/web/.

As IoT devices are becoming widely deployed, there exist many threats to IoT-based systems due to their inherent vulnerabilities. One effective approach to improving IoT security is to deploy IoT honeypot systems, which can collect attack information and reveal the methods and strategies used by attackers. However, building high-interaction IoT honeypots is challenging due to the heterogeneity of IoT devices. Vulnerabilities in IoT devices typically depend on specific device types or firmware versions, which encourages attackers to perform pre-attack checks to gather device information before launching attacks. Moreover, conventional honeypots are easily detected because their replying logic differs from that of the IoT devices they try to mimic. To address these problems, we develop an adaptive high-interaction honeypot for IoT devices, called HoneyIoT. We first build a real device based attack trace collection system to learn how attackers interact with IoT devices. We then model the attack behavior through markov decision process and leverage reinforcement learning techniques to learn the best responses to engage attackers based on the attack trace. We also use differential analysis techniques to mutate response values in some fields to generate high-fidelity responses. HoneyIoT has been deployed on the public Internet. Experimental results show that HoneyIoT can effectively bypass the pre-attack checks and mislead the attackers into uploading malware. Furthermore, HoneyIoT is covert against widely used reconnaissance and honeypot detection tools.

The British-led mission to test techniques to clear up space junk has demonstrated a harpoon in orbit. The RemoveDebris satellite fired the projectile into a target board held at a distance on the end of a boom. Video of the event shows the miniature spear fly straight and true, and with such force that it actually breaks the target structure. But, importantly, the harpoon's barbs deploy and hold on to the board, preventing it from floating away. Prof Guglielmo Aglietti, from the University of Surrey in Guildford, is the principal investigator on the mission.