{(E)-2-Bromo-4-chloro-6-[3-(dimethyl­ammonio)propyl­imino­meth­yl]­phenol­ato}­dichloridozinc(II)

The title compound, [ZnCl2(C12H16BrClN2O)], is a mononuclear zinc(II) complex. The ZnII atom is four-coordinate in a tetra­hedral geometry, binding to the phenolate O and imine N atoms of the zwitterionic Schiff base ligand and to two Cl− ions. In the crystal structure, mol­ecules are linked through inter­molecular N—H⋯Cl hydrogen bonds to form chains running along the a axis

Evolving models for incrementally learning emerging activities

Ambient Assisted Living (AAL) systems are increasingly being deployed in real-world environments and for longperiods of time. This significantly challenges current approaches that require substantial setup investment and cannot account forfrequent, unpredictable changes in human behaviours, health conditions, and sensor deployments. The state-of-the-art method-ology in studying human activity recognition is cultivated from short-term lab or testbed experimentation, i.e., relying on well-annotated sensor data and assuming no change in activity models. This paper propose a technique,EMILEA, to evolve an ac-tivity model over time with new types of activities. This technique novelly integrates two recent advances in continual learning:Net2Net – expanding the architecture of a model while transferring the knowledge from the previous model to the new modeland Gradient Episodic Memory – controlling the update on the model parameters to maintain the performance on recognisingpreviously learnt activities. This technique has been evaluated on two real-world, third-party, datasets and demonstrated promising results on enhancing the learning capacity to accommodate new activities that are incrementally introduced to the modelwhile not compromising the accuracy on old activities.PostprintPeer reviewe

Synch-Graph : multisensory emotion recognition through neural synchrony via graph convolutional networks

Human emotions are essentially multisensory, where emotional states are conveyed through multiple modalities such as facial expression, body language, and non-verbal and verbal signals. Therefore having multimodal or multisensory learning is crucial for recognising emotions and interpreting social signals. Existing multisensory emotion recognition approaches focus on extracting features on each modality, while ignoring the importance of constant interaction and co- learning between modalities. In this paper, we present a novel bio-inspired approach based on neural synchrony in audio- visual multisensory integration in the brain, named Synch-Graph. We model multisensory interaction using spiking neural networks (SNN) and explore the use of Graph Convolutional Networks (GCN) to represent and learn neural synchrony patterns. We hypothesise that modelling interactions between modalities will improve the accuracy of emotion recognition. We have evaluated Synch-Graph on two state- of-the-art datasets and achieved an overall accuracy of 98.3% and 96.82%, which are significantly higher than the existing techniques.Postprin

Investigating multisensory integration in emotion recognition through bio-inspired computational models

Emotion understanding represents a core aspect of human communication. Our social behaviours are closely linked to expressing our emotions and understanding others emotional and mental states through social signals. The majority of the existing work proceeds by extracting meaningful features from each modality and applying fusion techniques either at a feature level or decision level. However, these techniques are incapable of translating the constant talk and feedback between different modalities. Such constant talk is particularly important in continuous emotion recognition, where one modality can predict, enhance and complement the other. This paper proposes three multisensory integration models, based on different pathways of multisensory integration in the brain; that is, integration by convergence, early cross-modal enhancement, and integration through neural synchrony. The proposed models are designed and implemented using third-generation neural networks, Spiking Neural Networks (SNN). The models are evaluated using widely adopted, third-party datasets and compared to state-of-the-art multimodal fusion techniques, such as early, late and deep learning fusion. Evaluation results show that the three proposed models have achieved comparable results to the state-of-the-art supervised learning techniques. More importantly, this paper demonstrates plausible ways to translate constant talk between modalities during the training phase, which also brings advantages in generalisation and robustness to noise.PostprintPeer reviewe

Micropore-Boosted Layered Double Hydroxide Catalysts：EIS Analysis in Structure and Activity for Effective Oxygen Evolution Reaction

Since the oxygen evolution catalysis process is vital yet arduous in energy conversion and storage devices, it is highly desirous but extremely challenging to engineer earth-abundant, noble-metal-free nanomaterials with superior electrocatalytic activity toward effective oxygen evolution reactions (OERs). Herein, we construct a prismlike cobalt–iron layered double hydroxide (Co–Fe LDH) with a Co/Fe ratio of 3:1 utilizing a facile self-templated strategy. Instead of carbon-species-coupled treatment, we focus on ameliorating the intrinsic properties of LDHs as OER electrocatalysts accompanied by the hierarchical nanoflake shell, well-defined interior cavity, and numerous microporous defects. In contrary to conventional LDHs synthesized via a one-pot method, Co–Fe LDHs fabricated in this work possess a huge specific surface area up to 294.1 m^2 g^(–1), which not only provides abundant active sites but also expedites the kinetics of the OER process. The as-prepared Co–Fe LDH electrocatalysts exhibit advanced electrocatalytic performance and a dramatic stability of the OER in an alkaline environment. In particular, the contribution of micropore defects is clearly discussed according to the electrochemical impedance spectroscopy analysis, in which the time constant of the OER at the micropore defect is several orders of magnitude smaller than that at the exterior of Co–Fe LDHs, forcefully verifying the intrinsic catalytic activity enhancement derived from the micropore defects. This work provides a promising model to improve OER electrocatalyst activity via produce defects and research the contribution of micropore defects

A robust reputation-based location-privacy recommender system using opportunistic networks

Location-sharing services have grown in use commensurately with the increasing popularity of smart phones. As location data can be sensitive, it is important to preserve people’s privacy while using such services, and so location-privacy recommender systems have been proposed to help people configure their privacy settings.These recommenders collect and store people’s data in a centralised system, but these themselves can introduce new privacy threats and concerns.In this paper, we propose a decentralised location-privacy recommender system based on opportunistic networks. We evaluate our system using real-world location-privacy traces, and introduce a reputation scheme based on encounter frequencies to mitigate the potential effects of shilling attacks by malicious users. Experimental results show that, after receiving adequate data, our decentralised recommender system’s performance is close to the performance of traditional centralised recommender systems (3% difference in accuracy and 1% difference in leaks). Meanwhile, our reputation scheme significantly mitigates the effect of malicious users’input (from 55% to 8% success) and makes it increasingly expensive to conduct such attacks.Postprin

Detecting abnormal events on binary sensors in smart home environments

With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies.PostprintPeer reviewe

Partition-based Nonrigid Registration for 3D Face Model

This paper presents a partition-based surface registration for 3D morphable model(3DMM). In the 3DMM, it often requires to warp a handcrafted template model into different captured models. The proposed method first utilizes the landmarks to partition the template model then scale each part and finally smooth the boundaries. This method is especially effective when the disparity between the template model and the target model is huge. The experiment result shows the method perform well than the traditional warp method and robust to the local minima