Index recommendation is crucial for optimizing database performance. However, existing heuristic- and learning-based methods often rely on inefficient exhaustive search and estimated costs, leading to low efficiency (due to the vast search space) and unsatisfactory actual latency (due to inaccurate estimations). Inspired by the refinement strategies of experienced DBAs-who efficiently identify and iteratively refine indexes with database feedback-we present LLMIA, an out-of-the-box, tuning-free index advisor leveraging large language models (LLMs) through in-context learning for index recommendation. LLMIA injects database expertise into the LLM using a high-quality demonstration pool and comprehensive workload feature extraction, while iteratively incorporating database feedback to guide the index refinement. This design enables LLMIA to emulate the decision-making process of expert DBAs: efficiently recommending and refining indexes for various workloads within just a few interactions with the DBMS. We validate LLMIA with extensive experiments on five standard OLAP benchmarks (TPC-H with different scales, JOB, TPC-DS, SSB), where it consistently outperforms or matches 12 baselines by producing superior index recommendations with minimal database interactions. Additionally, LLMIA demonstrates robust generalization on two real-world commercial workloads, delivering high-quality recommendations without the need for additional adaptation or retraining, highlighting its out-of-the-box capability.

Visual navigation is a fundamental capability for autonomous home-assistance robots, enabling the execution of long-horizon tasks such as object search. While recent methods have leveraged Large Language Models (LLMs) to incorporate commonsense reasoning and improve exploration efficiency, their planning processes remain constrained by textual representations, which cannot adequately capture spatial occupancy or scene geometry-critical factors for informed navigation decisions. In this work, we explore whether Vision-Language Models (VLMs) can achieve mapless visual navigation using only onboard RGB/RGB-D streams, unlocking their potential for spatial perception and planning. We achieve this by developing the imagination-powered navigation framework ImagineNav++, which imagines the future observation images at valuable robot views and translates the complex navigation planning process into a rather simple best-view image selection problem for VLMs. Specifically, we first introduce a future-view imagination module, which distills human navigation preferences to generate semantically meaningful candidate viewpoints with high exploration potential. These imagined future views then serve as visual prompts for the VLM to identify the most informative viewpoint. To maintain spatial consistency, we develop a selective foveation memory mechanism, which hierarchically integrates keyframe observations through a sparse-to-dense framework, thereby constructing a compact yet comprehensive memory for long-term spatial reasoning. This integrated approach effectively transforms the challenging goal-oriented navigation problem into a series of tractable point-goal navigation tasks. Extensive experiments on open-vocabulary object and instance navigation benchmarks demonstrate that our ImagineNav++ achieves SOTA performance in mapless setting, even surpassing most cumbersome map-based methods, revealing the importance of scene imagination and scene memory in VLM-based spatial reasoning.

Visual navigation is an essential skill for home-assistance robots, providing the object-searching ability to accomplish long-horizon daily tasks. Many recent approaches use Large Language Models (LLMs) for commonsense inference to improve exploration efficiency. However, the planning process of LLMs is limited within texts and it is difficult to represent the spatial occupancy and geometry layout only by texts. Both are important for making rational navigation decisions. In this work, we seek to unleash the spatial perception and planning ability of Vision-Language Models (VLMs), and explore whether the VLM, with only on-board camera captured RGB/RGB-D stream inputs, can efficiently finish the visual navigation tasks in a mapless manner. We achieve this by developing the imagination-powered navigation framework ImagineNav, which imagines the future observation images at valuable robot views and translates the complex navigation planning process into a rather simple best-view image selection problem for VLM. To generate appropriate candidate robot views for imagination, we introduce the Where2Imagine module, which is distilled to align with human navigation habits. Finally, to reach the VLM preferred views, an off-the-shelf point-goal navigation policy is utilized. Empirical experiments on the challenging open-vocabulary object navigation benchmarks demonstrates the superiority of our proposed system.

Polarized Scanning Atmospheric Corrector (PSAC) onboard the Huanjing Jianzai (HJ)-2 A/B satellites is a cross-track scanning polarimetric remote sensor that measures the intensity and direction of light reflected by the Earth and its atmosphere by 9 full polarized spectral bands from near-ultraviolet (near-UV) to shortwave infrared (SWIR). In particular, geolocation accuracy is an important factor for polarization observations. An automatic coastline inflection method (CIM) is implemented for PSAC geolocation error estimation. Over five months of globally middle or low latitude coastline area measurements are used to obtain statistical result. The results of the comparison with the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) show PSAC geolocation error is smaller than 0.38 ground sample distance (GSD) or 3.25 km in 95% confidence level. In cross-track direction, the geolocation error estimation is affected by the instrument sampling characteristics like spatial response function (SRF). Thus, the correction method is proposed by establishing relationship between measurement radiance in CIM and offset proportion of PSAC GSD. The biases are obviously reduced after correction.

In online surveys, many people are reluctant to provide true answers due to privacy concerns. Thus, anonymity is important for online message collection. Existing solutions let each member blindly shuffle the submitted messages by using an IND-CCA2 secure cryptosystem. In the end, the message sender's identities are protected since no one knows the message submission order. These approaches cannot efficiently handle groups of large size.In this paper, we propose an efficient anonymous message submission protocol aimed at a practical group size. Our protocol is based on a secret sharing scheme and a symmetric key cryptosystem. We propose a novel method to aggregate members' messages into a message vector such that a group member knows only his own position in the submission sequence. The protocol is accountable for capturing malicious members breaking the protocol execution. We provide a theoretical proof showing that our protocol is anonymous under malicious attacks. We also discuss our simulation results to demonstrate the efficiency of our protocol.

Authenticating whether two smartphones are in close proximity is important in smartphone security. For example, the authentication result can be used to pair two devices and construct a secure communication channel between them. Many existing proximity authentication systems rely on short range networks-the communication is usually restricted in short range networks. However, this approach is inadequate when we want to verify whether the communication distance is within a few centimeters, i.e. near field. To address this challenge, many other techniques construct systems based on the near field communication (NFC) system. Unfortunately, only a small portion of smart devices in the current market are equipped with NFC chips. The purpose of this paper is to provide a close proximity authentication system which does not depend on NFC chips. We devise a system to achieve close proximity authentication by using correlated finger movements on the two smartphones. Human input usually contains errors and is of low entropy, which affects the usability and security of our system. We solve these issues in an efficient way, considering the limited computational resources on smart devices. Our system does not need any prior secret information shared between the two devices, and generates the same high-entropy cryptographic key for both devices in a successful authentication. The efficiency of the system is validated by evaluations on Motorola Droid smartphones.

Asserting whether two devices are in close proximity is very important to many smartphone assisted security systems. For example, the smartphone based two-factor authentication usually requires the smartphone to stay in close proximity to the other device during authentication. However, relay attacks pose a serious threat to existing approaches for proximity assertions. In this paper, we present a novel near field assertion system that restricts the distance between the two devices to the scale of several centimeters. Our system explores acoustic communications and can prevent relay attacks. The generated assertion is a confidential binary sequence known only to the two devices. Our system is fully automated and light-weight, as demonstrated by extensive evaluations on a prototype.

The polar format algorithm (PFA) is a classic algorithm for processing spotlight synthetic aperture radar (SAR) data and is extensively used in practical applications nowadays, mainly because its well performance in processing highly squinted data. Based on the chirp Z-Transform (CZT), PFA can be implemented accurately without any interpolation in the broadside case; however, for the squint mode case, since the input data are not uniformly distributed with same sampling intervals, the CZT algorithm requests interpolation in the azimuth dimension to obtain the uniform sample spacing, which leads to phase and amplitude error. To improve the processing accuracy under the squint mode, a CZT-based continuous varying PRF PFA is introduced. It achieves uniformly scaled grid along azimuth direction in wavenumber domain; as a result, interpolation in both range and azimuth dimensions can be avoided.

Location based service is an indispensable part of today's mobile era. While it brings a lot of benefits to people, the breach to individual location privacy is always a concern and impedes the smooth development of location based service. A user can be easily tracked once she subscribes or uses the service from an untrusted location based service server. In this paper, we try to address this problem by proposing a secure and efficient location based service system. In our system, a user does not leak any of her location information while she can still obtain the desired information associated with the location. We propose a novel method to map a user's current location to the index of the information stored in the location based service server. We demonstrated the efficiency of our system through simulations.

Reminder applications are essential applications in mobile devices. Since most smart devices are equipped with accurate localization capabilities, location based reminders emerge in recent smart devices. A user could add a location based reminder which reminds the user to do something once she enters or leaves a location. Convenient as these applications are, a user can be easily tracked once she installs these applications. We propose a secure and efficient location based reminder system. In our system, the reminder location and reminder message are stored in the form of ciphertext on the cloud server. The cloud server is able to preform a private location match without knowing anything about the user's location information. We propose a novel method to represent the user's reminder area in order to save the storage space and computation time of both users and the cloud server. We demonstrate the efficiency of our system in our simulations.

In this paper, we propose a secure cloud-assisted location based reminder system. The proposed system is secure and responsive. Our system outsources the location testing task --- testing whether the current location is near a reminder location --- to the cloud server such that the device synchronization is not necessary in the system. This feature makes the proposed system more responsive, especially when the reminder message is of large size, e.g., audio, images. Above all, the proposed system protects a user's location privacy and the confidentiality of the reminder message. The system is designed in a way that the cloud server can perform location testing for a user but cannot learn about her current location, reminder locations, and reminder messages. We prove the security of the proposed system and demonstrate its efficiency using simulations on a Motorola Droid smartphone.

Many smartphone applications use near field communication (NFC) systems to guarantee that two smartphones appear in near field when interactions take place. “Google Wallet” is one such example. This kind of guarantee is called near field authentication, which is to authenticate whether two smartphones stay closely to each other. Using NFC systems is a natural option for near field authentication. However, NFC systems rely on NFC chips, which are not available on many smartphones, especially the low-end smartphones. This obstructs the popularization of NFC applications. In this paper, we propose a simple and lightweight system to perform near field authentication without using NFC chips. The idea is to put two smartphones side by side and let the user slide his finger across the two smartphone screens. The two smartphones then extract correlated feature values from the movement on each screen to authenticate each other. When the two smartphones are in near field, our system generates the same cryptographic key for both. The key can be used by another upper system to carry out confidential communications. Our system is proved to be secure in the random oracle model. We demonstrate its efficiency on Motorola Droid smartphones in our evaluations.

Near field communication (NFC) systems provide a good location-limited channel so that many security systems can use it to force the participants to stay close to each other. Unfortunately, only a small number of smart devices in the market are equipped with NFC chips that are essential for NFC systems. The purpose of this paper is to provide the same feature, called near field authentication (NFA), without using NFC chips. We propose an easy-to-use system to achieve NFA by using human finger movement on the touch screens of two nearby smart devices. Our system does not need any prior secret information shared between two devices and generates the same high-entropy cryptographic key for both devices in a successful authentication. The efficiency of the system is demonstrated by our evaluation on a Motorola Droid smartphone.

In current location based social networks (LBSNs), users expose their location when they check in at a venue or search a place. The release of location privacy could lead to a severe breach of other privacy, such as identity or health condition. In this paper, we propose a framework to safeguard users' location information as well as the check-in records. Considering the special demands in LBSNs, we design a novel index structure to provide a fast search for users when they check in at the same venue frequently. At the same time, our framework outsources the heavy cryptographic computations to the server to reduce the computational overhead for mobile clients. Due to the dynamic feature of LBSNs, our framework uses a lightweight approach to handle a user's revoked friends and new friends. We prove the security of our framework in the random oracle model and demonstrate its efficiency on a Motorola Droid phone.

Recent success of online social networks (OSNs) motivates the study of security issues in OSNs. A fundamental but challenging security issue in OSNs is to authenticate a friend's real identity. A solution to this issue will benefit a number of OSN security protocols. Existing solutions require users securely obtain some secret information from their friends before authentication takes place, which is not always possible in OSNs. In this paper, we propose a new authenticated key exchange protocol based on the exclusive secrets shared between friends. It provides identity authentication and key exchange in a plain setting, i.e., users do not need to securely exchange or distribute any information beforehand. The protocol is designed to work with low-entropy input information, because human beings are not good at dealing with a large amount of information. Another advantage of our protocol is its tolerance of input errors considering human error is always a possibility. We prove the security of the protocol in the universal composability (UC) framework and demonstrate its efficiency.

In this paper, we construct a system which can hide users' identity when they visit untrusted third party storage sites. We also define a fine-grained access control policy for the data owner to freely define who can access the record. That is to say, the data owner divide his friends into several groups and issues them corresponding credentials for accessing his data. However, he can adds a friend at any time in a revocation list (RL) so that that friend could not access the data owner's data any more even if he has credentials. We theoretically prove the security of our protocols, and evaluate the performance of our protocols through simulations.

Authenticating users in online social networks (OSNs) is different from traditional authentication, because the participants in the authentication may not share any prior secret information. In this paper, we propose a decentralized authentication framework to help users authenticate unknown users in an OSN. In our framework, a user requests certificates from other trusted users to prove his identity in authentication. However, collecting certificates is constrained by the fact that trust is usually attritted with the length of a trust chain. Considering this constraint, our framework utilizes a decentralized online learning approach to help users collect more certificates. We further prove that the number of the collected certificates by each user using our protocols is close to optimum.

Group ranking is a necessary process used to find the best participant from a group. Group ranking has many applications, including online marketing, personal interests matching and proposal ranking. In an online virtual environment, participants want to do group ranking without leaking any of their private information. In this work, we generalize this scenario as a privacy preserving group ranking problem and formulate the privacy requirements of this problem. We propose a fully distributed privacy preserving group ranking framework and prove its security in the honest but curious model. The core of our framework is a novel multiparty sorting protocol, which guarantees that an adversary cannot link the private information to its owner's identity as long as the owner's final ranking is hidden from the adversary. Our protocol is efficient in computational overhead and communication rounds compared to existing works, as demonstrated by our analysis and simulation.

In online surveys, many people are not willing to provide true answers due to privacy concerns. Thus, anonymity is important for online message collection. Existing solutions let each member blindly shuffle the submitted messages by using the IND-CCA2 secure cryptosystem. In the end, all messages are randomly shuffled and no one knows the message order. However, the heavy computational overhead and linear communication rounds make it only useful for small groups. In this paper, we propose an efficient anonymous message submission protocol aimed at a practical group size. Our protocol is based on a simplified secret sharing scheme and a symmetric key cryptosystem. We propose a novel method to let all members secretly aggregate their messages into a message vector such that a member knows nothing about other members' message positions.We provide a theoretical proof showing that our protocol is anonymous under malicious attacks. We then conduct a thorough analysis of our protocol, showing that our protocol is computationally more efficient than existing solutions and results in a constant communication rounds with a high probability.

Making new connections is a crucial service in current online social networks. However, such a service can also raise a big security concern. For example, two strangers become friends in the online social network and they want to communicate securely. The messages transmitted between them could be encrypted using their public keys and decrypted using their private keys. However, one can not determine whether this public key indeed belongs to the claimed user. In this paper, we design a system to authenticate two strangers in fast mixing online social networks. We make a thorough analysis on our system and show that there is a high probability of finding witness users in fast mixing social networks.

Word shift is a fundamental format based text steganography. It embeds secret information in text by shifting words slightly. Compared with study on steganography, research on its steganalysis is still in its infancy. In this paper, we present a blind steganalysis method to detect word shift in PDF document. Our method is to find features sensitive to word shift and use classifier to learn and remember feature differences between natural document and stego one. In order to design sensitive features, we propose two concepts "neighbor difference" and "environment equal", which reveal the spaces' statistical property. Then, we divided the PDF document into two types to make our method work efficiently. At last, we design a series of experiments to demonstrate performance of our method. The detection accuracy of our method can be up to 93.3%. Our initial results shows that our proposed concepts are very useful in text steganalysis and offer help for other related works.