Hyperbolic Adversarial Learning for Personalized Item Recommendation

Personalized recommendation systems are indispensable intelligent components for social media and e-commerce. Traditional personalized item recommendation models are vulnerable to adversarial perturbations, resulting in poor robustness. Although adversarial learning-based recommendation models are able to improve the robustness, they inherently model the interaction relationships between users and items in Euclidean space, where it is difficult for them to capture the hierarchical relationships among entities. To address the above issues, we propose a hyperbolic adversarial learning based personalized item recommendation model, called HALRec. Specifically, HALRec models the interactions in hyperbolic space and utilizes hyperbolic distances to measure the similarities among entities. Moreover, instead of in Euclidean space, HALRec exploits the adversarial learning technique in hyperbolic space, i.e., HAL-Rec maximizes the hyperbolic adversarial perturbations loss while minimizing the hyperbolic based Bayesian personalized ranking loss. Hence, HALRec inherits the advantages of hyperbolic representation learning in capturing hierarchical relationships and adversarial learning in enhancing the robustness of the recommendation model. In addition, we utilize tangent space optimization to simplify the learning of model parameters. Experimental results on real-world datasets show that our proposed hyperbolic adversarial learning-based personalized item recommendation method outperforms the state-of-the-art personalized recommendation algorithms

Der Weg von Opfern von Online-Kriminalität durch die britischen Strafverfolgungsbehörden. Translated title: Understanding the journeys of online crime victims through law enforcement in Britain.

People rely on digital devices to conduct their lives and businesses online, however, the Internet has also enabled traditional crimes committed offline to migrate online, allowing these crimes to be committed transnationally. This creates difficulties for Britain’s law enforcement who have historically worked in forces within geographical boundaries, investigating crimes with offenders and victims at physical locations. Nowadays, victims can be scammed online from across the world and in different jurisdictions. Virtual currency does not require transportation, since it has no physical weight, so perpetrators can attack without moving from their digital devices or leaving physical clues. Victims seek support from law enforcement, support organisations, and social support from friends and family. The journeys of victims of online crime were explored during the main author’s PhD at Royal Holloway, University of London. The study found broken systems, under-reporting and victims taking different journeys depending on whether they are victims of cyber-dependent or cyber-enabled crimes

A Quantum of Learning: Using Quaternion Algebra to Model Learning on Quantum Devices

This article considers the problem of designing adaption and optimisation techniques for training quantum learning machines. To this end, the division algebra of quaternions is used to derive an effective model for representing computation and measurement operations on qubits. In turn, the derived model, serves as the foundation for formulating an adaptive learning problem on principal quantum learning units, thereby establishing quantum information processing units akin to that of neurons in classical approaches. Then, leveraging the modern HR-calculus, a comprehensive training framework for learning on quantum machines is developed. The quaternion-valued model accommodates mathematical tractability and establishment of performance criteria, such as convergence conditions

Audio-Visual Emotion Classification Using Reinforcement Learning-Enhanced Particle Swarm Optimisation

The extraction of fine-grained spatial-temporal characteristics for emotion classification is a challenging task owing to the subtlety and ambiguity of emotional expressions through video and audio channels. In this research, we propose an audio-visual ensemble model, comprising a two-stream 3D Convolutional Neural Network (CNN) architecture with RGB and optical flow as inputs for video emotion classification, as well as a variant of Wav2Vec2 for audio emotion recognition. The Wav2Vec2 variant integrates additional recurrent and attention layers with each transformer block to extract long- and short-term dependencies. A new Particle Swarm Optimisation (PSO) algorithm is proposed to fine-tune hyper-parameters of 3D CNNs and the enhanced Wav2Vec2, and formulate audio-visual ensemble models with the smallest sizes. It integrates a reinforcement learning (RL) algorithm, i.e. Asynchronous Advantage Actor-Critic (A3C), for search parameter and hybrid leader construction, and another RL algorithm, Proximal Policy Optimisation (PPO), for search action selection, as well as hypotrochoid and super formula-based search operations. Evaluated using audio-visual emotion datasets, our evolving ensemble model outperforms those devised by other search methods and existing state-of-the-art deep networks, significantly

Bus‐Based Sensor Deployment for Intelligent Sensing Coverage and k‐Hop Calibration

Drive‐by sensing is a promising concept that employs public transport as a mobile sensing platform to achieve high spatio‐temporal coverage for urban sensing tasks. At the same time, the low‐cost nature of mobile IoT sensors necessitates their more frequent calibration to ensure data accuracy and reliability. Manual or lab‐based calibration of a large number of mobile sensors may no longer be feasible and thus new approaches for automatic calibration are needed. Most prior work on optimal mobile sensor deployment focuses on coverage aspect without considering the sensor calibration. In this study, we present a joint approach for optimising the placement of bus‐based sensors for maximising the total unique sensing area and combining the optimal reference sensors geo‐placement for maximising k‐hop calibrate requirements on the selected routes. A metric‐based system developed in our model uses geographical set operations which includes both spatial and temporal joins to quantify the contribution of each bus route and rank them accordingly. We formulate the coverage optimisation problem as a mixed integer linear program (MILP), solve it with a greedy algorithm, and demonstrate this method’s potential using real‐world bus‐transit data from Toronto, Canada and Manchester, UK. Our approach involves a metric‐based system which quantifies each bus route unique coverage contribution for determining an optimal set of bus routes and bus stops for bus‐based and reference sensor deployment, to minimise sensor network costs and maximise spatio‐temporal coverage. The comparison with a random baseline algorithm indicates that our method outperforms in terms of deployment and coverage efficiency. Our results also include the potential of our weighted method in improving drive‐by sensing for air quality monitoring by comparing it with a separate benchmark scheme with different criteria

The phonological store of working memory:A critique and an alternative, perceptual-motor, approach to verbal short-term memory

A key quality of a good theory is its fruitfulness, one measure of which might be the degree to which it compels researchers to test it, refine it, or offer alternative explanations of the same empirical data. Perhaps the most fruitful element of Baddeley and Hitch’s (1974) Working Memory framework has been the concept of a short-term phonological store, a discrete cognitive module dedicated to the passive storage of verbal material that is architecturally fractionated from perceptual, language, and articulatory systems. This review discusses how the phonological store construct has served as the main theoretical springboard for an alternative perceptual-motor approach in which serial recall performance reflects the opportunistic co-opting of the articulatory planning system and, when auditory material is involved, the products of obligatory auditory perceptual organisation. It is argued that this approach, which rejects the need to posit a distinct short-term store, provides a better account of the two putative empirical hallmarks of the phonological store—the phonological similarity effect and the irrelevant speech effect—and that it shows promise too in being able to account for nonword repetition and word-form learning, the supposed evolved function of the phonological store. The neuropsychological literature cited as strong additional support for the phonological store concept is also scrutinised through the lens of the perceptual-motor approach for the first time and a tentative articulatory-planning deficit hypothesis for the ‘short-term memory’ patient profile is advanced. Finally, the relation of the perceptual-motor approach to other ‘emergent-property’ accounts of short-term memory is briefly considered