Receiving calls is one of the most universal functions of smartphones, involving sensitive information and critical operations. Unfortunately, to prioritize convenience, the current call receiving process bypasses smartphone authentication mechanisms (e.g., passwords, fingerprint recognition, and face recognition), leaving a significant security gap. To address this issue, we propose SCR-Auth, a secure call receiver authentication scheme for smartphones that leverages outer ear echoes. It sends inaudible acoustic signals through the earpiece speaker to actively sense the call receiver’s outer ear structure and records the resulting echoes using the top microphone. These echoes are then analyzed to extract unique outer ear biometric information for authentication. It operates implicitly, without requiring extra hardware or imposing additional burden. Comprehensive experiments conducted under diverse conditions demonstrate SCR-Auth’s effectiveness and security, showing an average balanced accuracy of 96.95% and resilience against potential attacks.

Earables (ear wearables) are rapidly emerging as a new platform encompassing a diverse of personal applications, prompting the development of authentication schemes to protect user privacy. Existing earable authentication methods are all specifically designed for air-conduction earphones, which are not suited for bone conduction earphones (BCEs) that rely on bone conduction mechanisms. In this paper, we propose HeadSonic, a usable BCE authentication system based on the unique head-conducted sounds, which can be acquired when the user wears the BCE device. Specifically, the system emits a millisecond-level sound to initiate the authentication session. The signal captured by the BCE microphone is propagated through the user's head, which is unique in density, geometry, and bone-tissue ratio. It operates implicitly, while maintaining robustness across different behaviors. Extensive experiments involving 60 subjects demonstrate that HeadSonic achieves a commendable balanced accuracy of 96.59%, proving its efficacy and resilience against replay and synthesis attacks. Our dataset and source codes are available athttps://anonymous.4open.science/r/HeadSonic-1CE4.

Accurately predicting traffic conditions is vital for smart city development, yet it remains challenging due to the intricate spatio-temporal dependencies in road networks. Existing works often propose intra-mixing deep learning-based prediction models for individual nodes and share parameters among them or spatial intermixing deep learning-based models for traffic predictions. However, these approaches may neglect essential principles of information exchange in traffic flow or capture useless or even erroneous spatio-temporal dependencies. To address these limitations, we propose a Mixing Spatio-Temporal neural network (MiST) for enhancing traffic predictions. In MiST, we propose (i) a temporal encoder that embeds the traffic data along with periodic features, (ii) a spatial encoder that embeds the positional information in graph and hypergraph spectral domains, as well as spatial node identities, and (iii) a mixing spatio-temporal encoder that merges the diverse features provided by the temporal and spatial encoders. Our empirical evaluations on real-world traffic prediction tasks, including flow and speed predictions, validate the superiority of MiST, underscoring its innovative contribution to traffic prediction methodologies.

The problem of traffic speed predictions is still very challenging due to the complex and dynamic urban traffic conditions. Many existing works have implied the importance of integrating spatial correlations into models to explore nonlinear spatio-temporal dependencies and make traffic predictions in near future. However, some of the works only consider pairwise correlations and cannot model the hidden information among multiple nodes well, and the others only consider hyper-correlations (that can be shared by more than two nodes) and discount the role of the pairwise ones for propagating spatial dependencies. Therefore, we propose a Spatio-Temporal neural nEtwork based on both Pairwise and Hyper-correlations (STEPH) for traffic speed predictions. It is distinguished primarily by incorporating both types of spatial correlations into temporal information and designing new hybrid spatio-temporal blocks in neural networks to effectively overcome the challenge. Experiments on two real-world traffic datasets demonstrate the effectiveness of the proposed model, and show its superiority performance compared to other state-of-the-art baselines.

Transformers have emerged as the superior choice for face recognition tasks, but their insufficient platform acceleration hinders their application on mobile devices. In contrast, Convolutional Neural Networks (CNNs) capitalize on hardware-compatible acceleration libraries. Consequently, it has become indispensable to preserve the distillation efficacy when transferring knowledge from a Transformer-based teacher model to a CNN-based student model, known as Cross-Architecture Knowledge Distillation (CAKD). Despite its potential, the deployment of CAKD in face recognition encounters two challenges: 1) the teacher and student share disparate spatial information for each pixel, obstructing the alignment of feature space, and 2) the teacher network is not trained in the role of a teacher, lacking proficiency in handling distillation-specific knowledge. To surmount these two constraints, 1) we first introduce a Unified Receptive Fields Mapping module (URFM) that maps pixel features of the teacher and student into local features with unified receptive fields, thereby synchronizing the pixel-wise spatial information of teacher and student. Subsequently, 2) we develop an Adaptable Prompting Teacher network (APT) that integrates prompts into the teacher, enabling it to manage distillation-specific knowledge while preserving the model's discriminative capacity. Extensive experiments on popular face benchmarks and two large-scale verification sets demonstrate the superiority of our method.

Recommendations are important web services in the era of information explosion. Particularly, group recommendations aim to suggest new items to groups such that the members of groups are likely interested in. However, existing works still suffer from sparsity and cold-start issues (e.g., cold-start groups or items) for groups with few interactions on items. Most of them model the preferences or features of entities (i.e., users, items and groups) from heterogeneous interactions (i.e., user-item, group-item and user-group interactions) between two distinct types of entities, while ignoring the homogeneous interactions (i.e., user-user, item-item and group-group interactions) between entities of one type. To this end, we propose a new model, called H3Rec, which learns the representations of entities by developing two graph embedding layers based on an interaction graph of all entities. Specifically, the two graph embedding layers make full use of the hidden information in theHigher-orderHeterogeneous andHomogeneous interactionsof the graph. Therefore, H3Rec can alleviate the sparsity and cold-start issues and improve the performance of group recommendations. The experimental results on two real world datasets in different domains show the superiority of H3Rec in group recommendations, especially for cold-start groups and items.

Traffic is very important to route planning and people’s daily lives. Traffic prediction is still very challenging as it is affected by many complex factors including dynamic spatio-temporal dependencies and external factors (e.g., road types and nearby points of interest) in the road network. Dynamic spatio-temporal dependencies simultaneously contain spatial and temporal dependencies. Existing models for predicting traffic of links only consider the spatial dependencies from the perspective of links or the whole road network by ignoring the spatial dependencies among regions. To this end, this paper proposes a new Spatio-Temporal Neural Network (STNN) with the encoder-decoder architecture to improve the accuracy of traffic predictions by additionally taking into account the region-based spatial dependencies and external factors. Specifically, STNN learns dynamic spatio-temporal dependencies from historical traffic time series via an encoder in the perspective of the road network, with two spatial models, i.e.,region-based spatial modelandlink-based spatial attention modelin the perspectives of regions and links, respectively. Further, STNN decodes the output from the encoder via a decoder with a temporal attention model for recording long-term dependencies and fuses external factors in the road network, to improve network-wide traffic predictions. We conduct extensive experiments to evaluate the performance of STNN on three real-world traffic datasets, which shows that STNN is significantly better than the state-of-the-art models.

Nowadays, many researchers study on characterizing complex and dynamic traffic environments by modeling the spatio-temporal dependencies in a road network for traffic predictions. However, existing works fail to investigate comprehensive spatio-temporal dependencies, because most of them ignore the spatial dependencies from the topological graph structure information in the road network, or only consider the temporal dependencies between fine-grained time slots whereas ignore those among coarse-grained time periods, in which a time period is composed of a number of time slots. To this end, we propose a new encoder-decoder framework called GAMIT with graphical space and multi-grained time by developing a spatiotemporal recurrent neural network (STRNN) for traffic predictions, where the graphical space consists of spatial networks which represent road networks. STRNN first devises a spatiotemporal convolution block to capture the fine-grained spatiotemporal dependencies between time slots in the road network. Then, STRNN uses the recurrent architecture to catch the coarsegrained spatio-temporal dependencies among time periods. The encoder finally applies STRNN to learn the multi-grained spatiotemporal dependencies which are fed into the decoder for computing traffic predictions based on STRNN as well. To evaluate the performance of GAMIT, we conduct extensive experiments on two real traffic flow datasets. Experimental results show that GAMIT outperforms the state-of-the-art traffic prediction models.

Group recommendation aims to suggest preferred items to a group of users rather than to an individual user. Most existing methods on group recommendation directly learn theinherent interests of groups and users orinherent features of items, i.e., independently modeling the inherent embeddings of groups, users or items. However, the independent view severely suffers from the cold-start problem when making recommendations for occasional groups that are temporally formed by a set of users and have few interactions on items. Actually, the groups, users and items are interdependent because they interact with one another. The interdependencies constitute an interaction graph that provides multiple views to model the embeddings of groups, users and items from their interacting counterparts to improve recommendation for occasional groups. To this end, we propose a model, named GAME to learn the Graphical and Attentive Multi-view Embeddings (i.e., representations) for the groups, users and items from the independent view and counterpart views based on the interaction graph. In the counterpart views, the embedding of a group, user or item is aggregated from the interacting counterparts based on an attention mechanism that derives the adaptive weight for each counterpart. For instance, a user's embedding may be aggregated from her interacting items or groups. Further, GAME applies neural collaborative filtering to investigate the interactions between the multi-view embeddings of groups (or users) and items for group recommendation. Finally, we conduct extensive experiments on two real datasets. The experimental results show that GAME outperforms other state-of-the-art models, especially on both cold-start groups (i.e., occasional groups) and cold-start items.

Most of current group recommenders only consider the bottom-up influences, i.e., the preference of a group is greatly affected by the members in the group. For example, children usually dominate the preference of a family while senior experts often lead the preference of a professional group. However, in reality there also exist the top-down influences, i.e., a group inherently affects its every member, because a group often has some distinct themes which limit the preferences of all the members in the group. For instance, the members in a group for sports may prefer hiking and rock climbing, whereas the members in a group for entertainment would like to watch movies and play games. In other words, the influences between a group and its members are dual. To this end, this paper proposes a new model for Group Recommendation using Attentive Dual Influences (GRADI) that simultaneously explores both the bottom-up and top-down influences between a group and its members. The preference of a member in a group is represented as the group-specific member embedding by modeling the top-down influences from the group to the member. In addition, the preference of a group on a target item is represented as the item-specific group representation by considering the bottom-up influences from all the members to the group, where an attentive mechanism is developed to aggregate the preferences of all the members on a target item. Furthermore, GRADI investigates the interactions between groups and items with neural collaborative filtering. Results of extensive experiments conducted on two real-world datasets show that GRADI outperforms other state-of-the-art models.

As many location-based applications provide services for users based on traffic conditions, an accurate traffic prediction model is very significant, particularly for long-term traffic predictions (e.g., one week in advance). As far, long-term traffic predictions are still very challenging due to the dynamic nature of traffic. In this paper, we propose a model, called Spatio-Temporal Convolutional Neural Network (STCNN) based on convolutional long short-term memory units to address this challenge. STCNN aims to learn the spatio-temporal correlations from historical traffic data for long-term traffic predictions. Specifically, STCNN captures the general spatio-temporal traffic dependencies and the periodic traffic pattern. Further, STCNN integrates both traffic dependencies and traffic patterns to predict the long-term traffic. Finally, we conduct extensive experiments to evaluate STCNN on two real-world traffic datasets. Experimental results show that STCNN is significantly better than other state-of-the-art models.

In this paper, we study the issue of minimizing the total energy cost for green cloud data centers with time varying and location varying electricity prices and supply of renewable energy. Given the budget of energy cost, schedule the requests, servers, and power usage of different sources, such that the total cost can be minimized. We formulate the problem during the whole period of time as an MILP problem. We use Cplex to solve the problem. Experiment shows that our method can significantly reduce total cost after using ESDs.

Servers and network contribute about 60% to the total cost of data center in cloud computing. How to efficiently place virtual machines so that the cost can be saved as much as possible, while guaranteeing the quality of service plays a critical role in enhancing the competitiveness of service cloud provider. Considering the heterogeneous servers and the random property of multiple resources requirements of virtual machines, the problem is formulated as a multi-objective nonlinear programming in this paper. Virtual machine cluster with higher traffic is made staying together. This reduces the communication delay while saving the inter-server bandwidth consumption, especially the relatively scarce higher level bandwidth, by exploiting the topology information of data center. At the same time, statistic multiplex and newly defined “similarity” techniques are leveraged to consolidate virtual machines. The violation of resource capacity is kept at any designated minimal probability. Thus the quality of service will not be deteriorated while saving servers and network cost. An offline and an online algorithms are proposed to address this problem. Experiments compared with several baseline algorithms show the validity of the new algorithms: more cost is cut down at less computation effort.