Object detection in aerial imagery faces significant challenges caused by extreme scale variation, dense object layouts, and the prevalence of tiny instances. StandardIntersection over Union(IoU) and its variants are sensitive to small localization errors: a minor coordinate deviation may cause a large IoU penalty for tiny objects, and non-overlapping boxes yield no gradients, which limits localization accuracy. To address these issues, we propose ThermalIoU, a heat-diffusion-inspired bounding-box regression loss that represents rigid boxes as continuous diffusive fields. A box is treated as a flat-top initial temperature distribution and diffused by the Gaussian heat kernel, preserving its rectangular geometry while providing smooth boundary support. We further introduce a scale-decoupled and epoch-annealed diffusion parameter, so that small objects obtain broader early-stage gradients whereas larger objects retain sharper localization fields. Experiments on AI-TOD and VisDrone, together with ablations, efficiency measurements, and qualitative visualizations, show that ThermalIoU is a practical training loss with overall gains over CIoU and competitive performance against Inner-IoU and MPDIoU.

3D human avatar modeling is the key technology in the field of digital entertainment. However, existing methods still face significant challenges in handling complex human poses and recovering fine geometric details. In order to solve the above problems, a 3D Gaussian realistic human avatar modeling method based on the Poincaré ball and the triplane representation is proposed. Our method utilizes 3D Gaussian Splatting (3DGS) to model both 3D geometry and appearance. In our method, the triplane representation is introduced to generate the geometry features of the 3D human body, and the proposed hyperbolic U-net based on the Poincaré ball is used to generate the features of the UV maps. The shape decoder then uses the UV features and the geometry features to obtain the rendering parameters. The renderer uses the obtained rendering parameters to generate the 3D human avatar. Moreover, we design the hyperbolic perceptual loss to optimize the network. Experiments on several challenging datasets demonstrate the advantages of our method over the existing approaches with regard to both visual quality and the representative evaluating criteria.

Abstract Location‐related mobile social network services are popular nowadays, and their methods to obtain end users’ location information are based on people's self‐report location claims, using mobile devices to check positions and send them back to the service providers. However, this mechanism has a serious vulnerability that makes malicious users be able to access restricted resource by transmitting fake locations. Both academic and industrial researchers are recently aware of this problem's importance since the commercialized trend of location‐related mobile social network services. To address this issue, we propose mobile phone‐based physical‐social location, a mobile phone‐based location proof system to verify users’ location claims and defend various fake location information. Our core idea is that a user's location claim can be proved by a set of selective physical encountered people serving as “witnesses” who are co‐located with him/her in that area. The system is composed of proof generation and verification. In the proof generation phase, we leverage a certain number of co‐located people to generate certificates as location proofs during their encounters via bluetooth interface. In the verification phase, we propose an efficient verification scheme to make our system accurate and adaptive. We have implemented the MPSL system using real world Nokia N82 (Nokia, Espoo, Finland) phones. Our experimental results show that our mobile phone‐based system can achieve high verification accuracy and good performance. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we propose E-Shadow, a distributed mobile phone-based local social networking system. E-Shadow has two main components: (1) Local profiles. They enable E-Shadow users to record and share their names, interests, and other information with fine-grained privacy controls. (2) Mobile phone based local social interaction tools. E-Shadow provides mobile phone software that enables rich social interactions. The software maps proximate users’ local profiles to their human owners and enables user communication and content sharing. We have designed and implemented E-Shadow on mobile phones. In our E-Shadow system, we allow users to perform dynamic and layered information publishing, making use of interpersonal relevance in space and time. Our system also provides a mechanism to help users perform direction-driven localization of an E-Shadow and match it with its owner. Experiments on real world Windows Mobile phones and large-scale simulations show that our system disseminates information efficiently and helps receivers find the direction of a specific E-Shadow with accuracy. We believe our E-Shadow concept and system can lead to a more tightly-knit temporary community in one’s physical vicinity.

Recent years have witnessed mass proliferation of mobile devices with rich wireless communication capabilities as well as emerging mobile device-based information dissemination applications that leverage these capabilities. This paper proposes TurfCast, a novel information dissemination service that selectively broadcasts information in particular "turfs,â abstract logical spaces in which receivers are situated. Such turfs can be temporal or spatial based on receivers' lingering time or physical areas, respectively. TurfCast has many applications such as electronic proximity advertising and mobile social networking. To enable TurfCast, we propose two supporting technologies: TurfCode and TurfBurst. TurfCode is a nested 0-1 fountain code that enables the broadcaster to transmit either all information or none at all to receivers. TurfBurst exploits the Shannon bound to differentiate among receivers: those who cannot receive information fast enough receive none at all, even if they linger near the broadcaster. We implement TurfCast on real-world devices and conduct experiments in both indoor and outdoor environments. Our experimental results illustrate TurfCast's potential for controlling information dissemination in wireless networks.

Nowadays, more and more objects can be represented with electronic identifiers, e.g., people can be recognized from their laptops' MACs, and products can be identified by their RFID numbers. Localizing electronic identifiers is more and more important for a fully digitalized life. However, traditional wireless localization techniques are not satisfactory in performance to determine these electronic identifiers' positions. Some of them require costly hardware to achieve high accuracy and, hence, are not practical. The others are inaccurate and not robust against environmental noises, e.g., RSSI-based localization. Therefore, an accurate and practical approach for localizing electronic identifiers is needed. In this paper, we propose a new localization technique called EV-Loc. In EV-Loc, we make use of visual signals to help improve the accuracy of wireless localization. Our technique fully takes advantage of the high accuracy of visual signals and pervasiveness of electronic signals. To effectively couple these two signals together, we have designed an E-V match engine to find the correspondence between an object's electronic identifier and its visual appearance. We have implemented our technique on mobile devices and evaluated it in the real world. The localization error is less than 1 m. We have also evaluated our approach using large-scale simulations. The results show that our approach is accurate and robust.

In this paper, we propose a new type of incentive-driven and privacy-preserving systems for large-scale message dissemination in mobile networks. To distribute incentives which encourage forwarding behaviors, such as monetary rewards, we want to keep track of the forwarder list. In our algorithms, we rely on a Probabilistic one-ownership forwarding algorithm to record the list, so that the exchanged messages can be kept short and privacy preserving. More specifically, only one hop of forwarder information, instead of the complete list, is recorded, and the information is updated probabilistically following two ownership flipping models, namely, One-Flip and Always-Flip models. We also use a Bluetooth Service Discovery Protocol (SDP) toolkit to enable fast, configuration-free message exchange. Throughout the paper, we use coupon as a typical type of message to illustrate the core ideas. We have implemented the coupon dissemination system in Java ME. Our experiments on real-world mobile phones, such as Nokia and Samsung phones, and large-scale simulations show that our system is efficient in peer-to-peer message distribution and capable of massive deployment. We believe our key methodology can serve as a general framework for facilitating information propagation on mobile phones, where incentives and privacy protection are both essential.

Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish non-service-related information without establishing a connection. We propose a novel iterative Bloom filter protocol that encodes topics to fit in SDP attributes and achieves a low false-positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose.

Recent years have witnessed mass proliferation of mobile devices with rich wireless communication capabilities as well as emerging mobile device based information dissemination applications that leverage these capabilities. This paper proposes TurfCast, a novel information dissemination service that selectively broadcasts information in particular “turfs,” abstract logical spaces in which receivers are situated. Such turfs can be temporal or spatial based on receivers' lingering time or physical areas, respectively. TurfCast has many applications such as electronic proximity advertising and mobile social networking. To enable TurfCast, we propose two supporting technologies: TurfCode and TurfBurst. TurfCode is a nested 0-1 fountain code that enables the broadcaster to transmit either all information or none at all to receivers. TurfBurst exploits the Shannon bound to differentiate among receivers: those who cannot receive information fast enough receive none at all, even if they linger near the broadcaster. We implement TurfCast on real-world devices and conduct experiments in both indoor and outdoor environments. Our experimental results illustrate TurfCast's potential for controlling information dissemination in wireless networks.

Video cameras have been deployed at almost every critical location, and they keep generating huge volumes of video data. The current visual processing technologies are not efficient in handling all these data for surveillance purposes, and a large amount of human power is needed to process them. In this paper, we propose the E-V system, which uses electronic footprints to help sort through this swamp of data. Electronic footprints are wireless signals emitted by mobile devices carried by people. They are ubiquitous and amenable to collection and indexing. We study how to use electronic footprints to help quickly and accurately identify object's appearance model from large volumes of video data. We have formulated the problem and provided efficient algorithms to achieve the identification on large data sets. Real world experiments and large-scale simulations have been done, which confirms the feasibility and efficiency of the proposed algorithms.

Nowadays, an increasing number of objects can be represented by their wireless electronic identifiers. For example, people can be recognized by their phone numbers or their phones' WiFi' MAC addresses and products can be identified by their RFID numbers. Localizing objects with electronic identifiers is increasingly important as our lives become increasingly "digitalized". However, traditional wireless localization techniques cannot meet the fast growing needs of accurate and cost efficient localization. Some of these techniques require expensive hardware to achieve high accuracy, which is impractical for massive deployment. Others, such as WiFi RSSI based localization, are inaccurate and not robust to environmental noise. In this paper, we propose a new localization technique called EV-Loc. In EV-Loc, we use visual signals to help improve the accuracy of wireless localization. Our technique fully leverages visual signals' high accuracy and electronic signals' pervasiveness. To effectively couple these two signals, we design an E-V match engine to find the correspondence between an object's electronic identifier and its visual appearance. We implement our technique on mobile devices and evaluate it in real-world scenarios. The localization error is less than 1 m. We also evaluate our approach using large scale simulations. The results show that our approach is accurate and robust.

In this paper, we propose E-Shadow, a distributed mobile phone-based local social networking system. E-Shadow has two main components: (1) Local profiles. They enable EShadow users to record and share their names, interests, and other information with fine-grained privacy controls. (2) Mobile phone based local social interaction tools. E-Shadow provides mobile phone software that enables rich social interactions. The software maps proximate users' local profiles to their human owners and enables user communication and content sharing. We have designed and implemented E-Shadow on mobile phones. In our E-Shadow system, we allow users to perform dynamic and layered information publishing, making use of interpersonal relevance. Our system also provides a mechanism to help users perform direction-driven localization of an E-Shadow and match it with its owner. Experiments on real world Windows Mobile phones and large-scale simulations show that our system disseminates information efficiently and helps receivers find the direction of a specific E-Shadow with accuracy. We believe our E-Shadow concept and system can lead to a more tightly-knit temporary community in one's physical vicinity.

In this paper, we propose P3-coupon, a Prompt and Privacy-preserving electronic coupon distribution system based on a Probabilistic one-ownership forwarding algorithm. In this algorithm, only one hop of forwarder (coupon owner) information instead of the complete forwarder list is recorded to keep the coupon short and privacy-preserving. Such information is updated probabilistically following two ownership flipping models, namely, One-Flip and Always-Flip models. We also use a Bluetooth Service Discovery Protocol (SDP) toolkit to enable fast, configuration-free coupon exchange. We have implemented the system in Java ME. Our experiments on real world mobile phones, such as Nokia and Samsung phones, and large scale simulations show that our system is efficient in peer to peer coupon distribution and capable of massive deployment. We believe our key methodology can serve as a general framework for facilitating information propagation on mobile phones, which requires promptness and privacy protection.

Small talk is an important social lubricant that helps people, especially strangers, initiate conversations and make friends with each other in physical proximity. However, due to difficulties in quickly identifying significant topics of common interest, real-world small talk tends to be superficial. The mass popularity of mobile phones can help improve the effectiveness of small talk. In this paper, we present E-SmallTalker, a distributed mobile communications system that facilitates social networking in physical proximity. It automatically discovers and suggests topics such as common interests for more significant conversations. We build on Bluetooth Service Discovery Protocol (SDP) to exchange potential topics by customizing service attributes to publish non-service-related information without establishing a connection. We propose a novel iterative Bloom filter (IBF) protocol that encodes topics to fit in SDP attributes and achieves a low false positive rate. We have implemented the system in Java ME for ease of deployment. Our experiments on real-world phones show that it is efficient enough at the system level to facilitate social interactions among strangers in physical proximity. To the best of our knowledge, E-SmallTalker is the first distributed mobile system to achieve the same purpose.