ABSTRACT Searchable Encryption (SE) enables users to perform searches on encrypted data while preserving data privacy. Since cloud servers are platforms that provide services for a large number of users, and data owners require access control over their data, SE schemes that support multi‐user settings and access control are therefore more suitable for cloud storage. However, in existing SE schemes that support multi‐user settings and access control, most only support single‐keyword or conjunctive keyword searches, and the search time grows linearly with the total amount of data. These limitations negatively impact both the accuracy and efficiency of search operations. This work proposes an SE scheme specifically designed for multi‐user settings. Data owners can enforce fine‐grained access control policies, while a specialized retrieval structure allows the cloud to assist users in performing Boolean keyword searches with improved efficiency. The search complexity of the proposed scheme is , where denotes the number of files relevant to the queried keyword. We demonstrate the scheme's effectiveness and practicality through performance analysis.

Byzantine-robust federated learning (FL) aims to obtain an accurate global model even with potentially Byzantine users. However, most existing schemes rely on measuring the overall differences between the entire gradient vectors of different users, which fail to effectively distinguish malicious gradients from benign ones caused by data heterogeneity under non-IID settings, thereby compromising model performance. To tackle this challenge, we propose BPFLH, a novel Byzantine-robust privacy preserving FL framework for heterogeneous data. BPFLH is the first to introduce Bray–Curtis dissimilarity into FL, capturing the element-wise differences among gradients from different users. This method reduces the risk of misclassifying benign gradi ents as malicious and enhance the model's robustness against Byzantine attacks in non-IID data environments. Furthermore, BPFLH leverages CKKS homomorphic encryption to protect local gradients, enabling secure aggregation and Byzantine user detection without compromising privacy. Extensive experiments on real-world datasets under various attack scenarios and data distributions demonstrate that BPFLH exhibits strong robustness against Byzantine attacks while preserving privacy and maintaining superior accuracy compared to existing Byzantine-robust FL methods, particularly in non-IID environments.

Multi-modal data provides abundant and diverse object information, crucial for effective modal interactions in Re-Identification (ReID) task. However, existing approaches often overlook the quality variations in local features and fail to fully leverage the complementary information across modalities, particularly in cases where features are of low quality. In this paper, we propose to address this issue by leveraging a novel graph reasoning model, termed the Modality-aware Graph Reasoning Network (MGRNet). Specifically, we first construct modality-aware graphs to enhance the extraction of fine-grained local details by effectively capturing and modeling the relationships between patches. Subsequently, the selective graph nodes swap operation is employed to alleviate the adverse effects of low-quality local features by considering both local and global information, enhancing the representation of discriminative information. Finally, the swapped modality-aware graphs are fed into the local-aware graph reasoning module, which propagates multi-modal information to yield a reliable feature representation. Another advantage of the proposed graph reasoning approach is its ability to reconstruct missing modal information by exploiting inherent structural relationships, thereby minimizing disparities between different modalities. Experimental results on four benchmarks (RGBNT201, Market1501-MM, RGBNT100, MSVR310) indicate that the proposed method achieves state-of-the-art performance in multi-modal object ReID. The code for our method will be available upon acceptance.

The ubiquitous sensing capabilities of the Internet of Things (IoT) enable large-scale user tracking by identifying users who appear in at least t distributed location datasets. However, the distribution of these datasets across multiple tracking entities significantly increases the risk of sensitive data exposure. To address this problem, threshold multi-party private set intersection (T-MPSI) provides a promising privacy-preserving solution. Although the known works about T-MPSI have made valuable contributions, especially in terms of security, the efficiency deficiency in current T-MPSI protocols becomes apparent in large-scale deployment for user tracking. The core challenge is to develop an efficient T-MPSI protocol under the relaxed security constraint that is acceptable for user tracking. We first design a lightweight batch replicated secret sharing private membership test protocol with high performance. Moreover, we develop a one-round secure aggregation algorithm that bridges the gap between the secure query and the secure comparison built upon replicated secret sharing. Building on these techniques, we present an efficient T-MPSI protocol tailored to the designated k-collusion model. Our protocol significantly enhances secure query efficiency and ensures that the communication complexity of secure comparison remains independent of the number of parties. We formally prove its security, and extensive experiments in a LAN setting demonstrate at least a 6× speedup for secure query and a 3× speedup for secure comparison over the state-of-the-art protocol. These results confirm the practicality and efficiency of the proposed protocol for privacy-preserving user tracking.

Backdoor attacks pose significant security threats to deep neural networks. A backdoored model performs well on benign samples, but if the hidden backdoor is activated by a trigger defined by the attacker, the model's predictions will be maliciously altered. Existing backdoor attacks typically adopt a trigger-agnostic setup, where different poisoned samples in the backdoor attack methods contain the same trigger, which is usually visible or fragile. To address these limitations, we propose a sample-specific, sample-edge-adaptive backdoor attack method. Specifically, we utilize edge detection algorithms to identify edge structures in images as the target poisoning region, embedding the edge information into the least significant bits of the image using a steganographic method. This generates a sample-specific trigger pattern. Since image structure retains its semantic meaning during data transformation, this trigger pattern exhibits inherent robustness to data conversion. Our proposed attack is extensively evaluated across various network models and datasets, demonstrating its generalizability, superior stealthiness, better robustness compared to existing backdoor attack methods, and strong resistance against state-of-the-art defense techniques while maintaining high benign accuracy.

Reversible data hiding in encrypted JPEG images (JPEG-RDH-EI) is one of the key technologies for securely storing and managing confidential images in cloud environments. However, existing methods typically operate only in the coefficient domain or the encoding domain, failing to fully exploit the characteristics of encrypted images, which results in limited embedding capacity and significant file expansion. To address these limitations, this paper proposes a high-capacity JPEG-RDH-EI scheme based on multi-domain embedding. To reduce file expansion, a rotation model is devised that leverages the correlation of DCT coefficients within a range that does not notably alter the run length. This model embeds secret data by strategically rotating and rearranging these DCT coefficients. On this basis, the scheme constructs an optimal VLC mapping relationship and adaptively adjusts embedding parameters according to the characteristics of the encrypted image, based on the RSV histogram shifting technique, achieving data embedding in the encoding domain, thereby further enhancing embedding capacity. The proposed scheme supports fully separable data extraction and image recovery, making it suitable for various application scenarios. Evaluation results demonstrate that the maximum embedding capacity of this scheme is 2 to 3 times greater than that of existing advanced schemes, with some images achieving a maximum embedding capacity of over 100,000 bits. Moreover, the proposed scheme achieves an average unit file expansion of only about 0.03, outperforming current advanced methods.

Diffusion models have garnered significant attention in deep generative modeling due to their exceptional ability to generate diverse and high-quality samples across various data modalities. However, their security vulnerabilities, particularly backdoor attacks, remain largely unexplored, resulting in unpredictable and potentially malicious image generation. This paper introduces SBADiffusion, an invisible backdoor attack method that integrates steganography and quantization techniques to embed undetectable triggers into diffusion models. Specifically, steganography generates triggers by embedding subtle noise patterns into images, enabling these triggers to carry secret information without being perceptible to the human eye. Quantization technique further enhances the stealth of the triggers by optimizing their embedding to minimize visible artifacts or irregularities. Through a meticulously designed framework, this method achieves a delicate balance between visual stealth and attack reliability. Experimental results demonstrate that even at a low poisoning rate of 10 %, the attack success rate exceeds 90 %, and the mean squared error (MSE) of the poisoned images is significantly lower than that of existing methods. These findings not only highlight the stealth and precision of the proposed method but also reveal its potential for malicious exploitation, underscoring the urgent need to develop robust defense mechanisms against such threats in diffusion models.

The convergence of machine learning and searchable encryption enhances the ability to protect the privacy and security of data and enhances the processing power of confidential data. To enable users to efficiently perform machine learning tasks on encrypted data domains, we delve into oblivious keyword search with authorization (OKSA). The OKSA scheme effectively maintains the privacy of the user’s query keywords and prevents the cloud server from inferring ciphertext information through the searching process. However, limitations arise because the traditional OKSA approach does not support multi‐keyword searches. If a data file is associated with multiple keywords, each keyword and corresponding data must be encrypted one by one, resulting in inefficiency. We introduce an innovative approach aimed at enhancing the efficiency of search processes while addressing the limitation of current encryption and search systems that handle only a single keyword. This method, known as the oblivious multiple keyword search with authorization (OMKSA), is designed for more effective keyword retrieval. One of our important innovations is that it uses the arithmetic techniques of bilinear pairs to generate new tokens and new search methods to optimize communication efficiency. Moreover, we present a detailed and rigorous demonstration of the security for our proposed protocol, aligned with the predefined security model. We conducted a comparative experiment to determine which of the two schemes, OKSA and OMKSA, is more efficient when querying multiple keywords. Based on our experimental results, our OMKSA is very efficient for data searchers. As the number of query keywords increases, the computational overhead of connected keyword searches remains stable. Finally, as we move into the 5G era, the potential applications of OMKSA are huge, with clear implications for areas such as machine learning and artificial intelligence. Our findings pave the way for further exploration and deployment of these frontier areas.

With the rapid advancement of Internet of Things (IoT) technology, large volumes of data are exchanged among users via cloud servers. However, in an untrusted cloud server environment, the risk of data tampering is significant. For instance, a cloud server may fail to update its records promptly after receiving updated data from a data sender. Consequently, when the data receiver retrieves the relevant information, the cloud server may return outdated data, leading to security issues in data utilization. To address this problem, we propose a scheme that facilitates efficient verification in untrustworthy cloud environments. Our research approach is to utilize cryptographic accumulators within the oblivious searchable encryption model to achieve efficient verification. The data sender first uses a cryptographic accumulator to calculate the cumulative value of all messages to be uploaded, which are publicly accessible. In addition, the accumulator generates witness values for messages authorized to the data recipient. Before retrieving data, the data receiver can leverage the cryptographic accumulator to verify the timeliness of incoming messages, ensuring that the data is current and free from tampering. Furthermore, the data sender retains the flexibility to dynamically update the data stored in the cloud and efficiently refresh both the encrypted accumulator and its corresponding witness value. This article presents a rigorous security proof and a comparative experiment was carried out, supported by both analytical evaluations and experimental results, which collectively confirm the practical applicability of the proposed scheme in the context of the IoT.

Deep learning-based super-resolution (SR) reconstruction is a critical approach which is used to generate high-resolution images from corresponding low-resolution images. However, the CNN-based methods are ineffective in capturing global information, whereas the Transformer-based methods have limited ability to model long-range dependencies caused by window self-attention. Besides, it is a challenging task to recover lost high-frequency information from downsampled images. In this paper, a Hybrid Information Enhanced Network (HIEN) is proposed for MRI super-resolution task. Specifically, we propose a Spatial-Channel Hybrid Attention (SCHA) to enhance specific semantics representation by combining spatial and channel self-attention together. To recover more high-frequency components, we propose a Dynamic High-Frequency Pass Filter (DHPF) to preserve high-frequency information adaptively in pixel-wise. The results of our extensive experiments indicate that HIEN outperforms other state-of-the-art methods.

Cochlear implantation is the most effective treatment for severe deafness, which requires accurate localization of temporal bone anatomy. Preoperative CT image segmentation is an essential technique to determine the location of relevant tissues in the temporal bone. However, manual segmentation is usually time-consuming and suffers from low accuracy due to the complex and small structures of these tissues in temporal bone CT. To address this issue, we proposed a CNN-Transformer structure-based 3D multi-structured model for the automatic segmentation of fine and complex tissues such as the cochlea, facial nerve, ossicles, vestibule and semicircular canal in the temporal bone CT. Our model adopts a new Transformer deformation structure, which effectively utilizes the spatial attention mechanism to capture feature dependencies, and uses the channel attention mechanism to fuse different channel semantic representations to improve segmentation accuracy. Extensive experiments on a private temporal bone CT dataset show that our model achieves higher DSS and JSS scores, and lower HD95 and ASSD scores for all targets compared with other existing segmentation methods, demonstrating its superior performance.

Abstract Multi-keyword ranked searchable encryption (MRSE) supports multi-keyword contained in one query and returns the top-k search results related to the query keyword set. It realized effective search on encrypted data. Most previous works about MRSE can only make the complete keyword search and rank on the server-side. However, with more practice, users may not be able to express some keywords completely when searching. Server-side ranking increases the possibilities of the server inferring some keywords queried, leading to the leakage of the user’s sensitive information. In this paper, we propose a new MRSE system named ‘multi-keyword ranked searchable encryption with the wildcard keyword (MRSW)’. It allows the query keyword set to contain a wildcard keyword by using Bloom filter (BF). Using hierarchical clustering algorithm, a clustering Bloom filter tree (CBF-Tree) is constructed, which improves the efficiency of wildcard search. By constructing a modified inverted index (MII) table on the basis of the term frequency-inverse document frequency (TF-IDF) rule, the ranking function of MRSW is performed by the user. MRSW is proved secure under adaptive chosen-keyword attack (CKA2) model, and experiments on a real data set from the web of science indicate that MRSW is efficient and practical.

Abstract Verifiable symmetric searchable encryption is a keyword search technology that supports verification of search results. Many schemes improve search performance by dividing each keyword label into segments and storing them in a Trie-tree at the expense of high storage. And the index will degenerate into a linear linked list when all keyword labels have the same prefix except for the last segment. But it will greatly affect the search efficiency. In this paper, we propose a verifiable symmetric searchable encryption scheme based on the AVL Tree (abbreviated as VSSE-AVL), which uses complete keyword labels to build the index. Compared with the Trie-tree index, VSSE-AVL not only balances storage and search performance, but also avoids degradation. To verify the correctness and completeness of empty search results, we store path information in each leaf node and node with only one child node. Considering the substitution attack, we bind the file identifier and the file so that the client will find out once the server returns inconsistent search results. Rigorous security analysis shows VSSE-AVL satisfies privacy and verifiability. Compared with the verifiable SSE-2 with the same security, the experimental evaluation shows that our proposed scheme performs better on storage, search and verification.

The correctness and reliability of data sources are the keys to the practicality of data collected by Internet of Things (IoT) devices. Attribute-based signature (ABS) is a cryptographic primitive for users to sign with their own attributes, which can be applied to the authentication process in IoT scenarios. The attribute authority is responsible for issuing the attribute key to the user in ABS. Multiple authorities can complete attribute management tasks to avoid the threat of a single authority. However, attribute authorities need to execute multiple interactions to collaborate to generate attribute keys for users, which brings a large transmission burden. In addition, a lot of resource-constrained terminals in the IoT mostly play the role of signer or verifier in authentication protocols. The signature generation and verification algorithms often have heavy pairing and exponentiation operations. Currently, no ABS scheme takes into account the efficiency of all participating entities simultaneously. In this article, we present an aggregated anonymous key issue (AAKI) protocol to reduce the transmission burden between multiple authorities. Meanwhile, the noninteractive zero-knowledge proof aggregate exponentiation (NI-ZKPoKAE) protocol is designed to aggregate the transmitted secret values in AAKI. To reduce the computational burden of signers and verifiers, Blakley secret sharing, where the Hadamard matrix is used more efficiently to handle the$(n, n)$-threshold, is used to construct an efficient and fine-grained multiauthority ABS (EFMA-ABS) scheme. This brings high efficiency to all three types of parties involved in IoT authentication. Our above-mentioned protocols have been proven to be feasible and effective.

Key-aggregate searchable encryption (KASE) schemes support selective data sharing and keyword-based ciphertext searching by using the constant-size shared key and trapdoor, making these schemes attractive for resource-constrained users to store, share, and search encrypted data in public clouds. However, most previously proposed KASE schemes suffer from our proposed “off-line keyword guessing attack (KGA)” and some other weaknesses. Consequently, they fail to gain the keyword ciphertext indistinguishability and trapdoor indistinguishability, which are vital security goals of searchable encryption. Inspired by the relationship of public key encryption with keyword search (PEKS) and KASE, we design a new KASE scheme called key-aggregate searchable encryption with a designated server (dKASE). The dKASE scheme achieves our proposed keyword ciphertext indistinguishability against chosen keyword attack (KC-IND-CKA) and keyword trapdoor indistinguishability against keyword guessing attack (KT-IND-KGA) security models, where the latter model captures off-line KGA. Then, we extend the dKASE scheme to verifiable dKASE in multi-owner setting (dVKASEM) scheme. With dVKASEM, when multiple data owners authorize a user to access data, the user merely needs to store his single key and generate a single trapdoor to query these owners’ data. Besides, the adoption of the aggregate signature significantly reduces the overhead of verifying whether data has been tampered with. Performance analysis illustrates that our schemes are efficient.

Investor sentiment and emotions have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors for stock price prediction using machine learning. Existing prediction models mostly depend on the analysis of trading data and company profit. few prediction theories have been built based on individual investors' sentiments. The fundamental reason is the difficulty to measure individual investors' sentiment.

Using cloud storage services, users can store their data in the cloud to avoid the expenditure of local data storage and maintenance. To ensure the integrity of the data stored in the cloud, many data integrity auditing schemes have been proposed. In most, if not all, of the existing schemes, a user needs to employ his private key to generate the data authenticators for realizing the data integrity auditing. Thus, the user has to possess a hardware token (e.g., USB token, smart card) to store his private key and memorize a password to activate this private key. If this hardware token is lost or this password is forgotten, most of the current data integrity auditing schemes would be unable to work. In order to overcome this problem, we propose a new paradigm called data integrity auditing without private key storage and design such a scheme. In this scheme, we use biometric data (e.g., iris scan, fingerprint) as the user’s fuzzy private key to avoid using the hardware token. Meanwhile, the scheme can still effectively complete the data integrity auditing. We utilize a linear sketch with coding and error correction processes to confirm the identity of the user. In addition, we design a new signature scheme which not only supports blockless verifiability, but also is compatible with the linear sketch. The security proof and the performance analysis show that our proposed scheme achieves desirable security and efficiency.

IoT (Internet of things) devices often collect data and store the data in the cloud for sharing and further processing; This collection, sharing, and processing will inevitably encounter secure access and authentication issues. Attribute based signature (ABS), which utilizes the signer’s attributes to generate private keys, plays a competent role in data authentication and identity privacy preservation. In ABS, there are multiple authorities that issue different private keys for signers based on their various attributes, and a central authority is usually established to manage all these attribute authorities. However, one security concern is that if the central authority is compromised, the whole system will be broken. In this paper, we present an outsourced decentralized multi-authority attribute based signature (ODMA-ABS) scheme. The proposed ODMA-ABS achieves attribute privacy and stronger authority-corruption resistance than existing multi-authority attribute based signature schemes can achieve. In addition, the overhead to generate a signature is further reduced by outsourcing expensive computation to a signing cloud server. We present extensive security analysis and experimental simulation of the proposed scheme. We also propose an access control scheme that is based on ODMA-ABS.

In this paper, we investigate the effect that an auditory distraction with differing levels of intensity has on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. This work is based on the P300 speller BCI (oddball) paradigm and the xDAWN algorithm, with ten healthy subjects; while using a non-invasive Brain-Computer Interface (BCI) based on low fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the no music (M0), followed by music at 90% (M90), music at 60% (M60) and last music at 30% (M30), which results were in identical order to the subjects' preferences. In addition, the amplitude did not show any statistical significance in all scenarios while the latency exhibited a minor statistical difference. This work is part of a larger EEG based project where we are introducing different categories of distractions that are being considered alongside the development of a taxonomy. These results should give some insight into the practicability of the current P300 speller to be used for real-world applications.

Summary With the wide deployment of cloud computing, outsourcing complicated computational tasks to cloud service providers has attracted much attention. An increasing number of clients with computationally constrained devices choose to outsource their heavy tasks to cloud servers to reduce the computational overhead in local. However, how to preserve the integrity of computational results becomes a challenge since commercial cloud servers are not trusted. Public verifiability is an effective mechanism to allow clients to verify the integrity of the results returned by the servers. Because the results are sensitive in many applications, it raises the problem of privacy leakage in the public verification process. In this paper, we propose an efficient verifiable computation scheme while keeping output privacy. The proposed scheme achieves blind verifiability such that the verifiers who have not the additional information (retrieve key) can verify the integrity of the result without learning the result. Furthermore, by combining with (k,n)‐threshold sharing, our scheme allows the clients jointly learn the results.

Cloud storage auditing allows the users to store their data to the cloud with a guarantee that the data integrity can be efficiently checked. In order to release the user from the burden of generating data signatures, the proxy with a valid warrant is introduced to help the user process data in lightweight cloud storage auditing schemes. However, the proxy might be revoked or the proxy’s warrant might expire. These problems are common and essential in real-world applications, but they are not considered and solved in existing lightweight cloud storage auditing schemes. In this paper, we propose a lightweight identity-based cloud storage auditing scheme supporting proxy update, which not only reduces the user’s computation overhead but also makes the revoked proxy or the expired proxy unable to process data on behalf of the user any more. The signatures generated by the revoked proxy or the expired proxy can still be used to verify data integrity. Furthermore, our scheme also supports workload-based payment for the proxy. The security proof and the performance analysis indicate that our scheme is secure and efficient.

The cloud-assisted Internet of Things (CIoT) is booming, which utilizes powerful data processing capabilities of the cloud platform to solve massive Internet of Things (IoT) data. However, the CIoT faces new security challenges, such as the confidentiality of the outsourced data. Data encryption is a fundamental technique that can guarantee the confidentiality of outsourced data, but it limits target encrypted data retrieval from cloud platform. Public key encryption with keyword search (PEKS) provides a promising solution to address this problem. In PEKS, a cloud server can be authorized to search the keyword in encrypted documents and retrieve associated encrypted documents for the receiver. However, most existing PEKS schemes merely focus on keyword search function while ignoring the associated documents encryption/decryption function. Thus, in practice, a PEKS scheme must cooperate with another separated public key encryption (PKE) scheme to fulfill a completely secure data sharing scheme. To address this problem, in this paper, we propose a secure data sharing scheme with designated server that combines PKE scheme with PEKS scheme, which provides both keyword search and documents encryption/decryption functions. Furthermore, only the designated server can search the keyword via encrypted documents for enhanced security in our work. Moreover, our scheme also satisfies the public verifiability of search results, which includes both keywords and documents ciphertexts’ correctness and integrity. As to the security, our scheme provides stronger indistinguishability security of document and keyword in the proposed security model.

With the development of cloud services, outsourcing computation tasks to a commercial cloud server has drawn attention of various communities, especially in the Big Data era. Public verifiability offers a flexible functionality in real circumstance where the cloud service provider (CSP) may be untrusted or some malicious users may slander the CSP on purpose. However, sometimes the computational result is sensitive and is supposed to remain undisclosed in the public verification phase, while existing works on publicly verifiable computation (PVC) fail to achieve this requirement. In this paper, we highlight the property of result confidentiality in publicly verifiable computation and present confidentiality-preserving public verifiable computation (CP-PVC) schemes for multivariate polynomial evaluation and matrix-vector multiplication, respectively. The proposed schemes work efficiently under the amortized model and, compared with previous PVC schemes for these computations, achieve confidentiality of computational results, while maintaining the property of public verifiability. The proposed schemes proved to be secure, efficient, and result-confidential. In addition, we provide the algorithms and experimental simulation to show the performance of the proposed schemes, which indicates that our proposal is also acceptable in practice.

With the development of cloud services, outsourcing computation tasks to a commercial cloud server has drawn attentions by various communities, especially in the Big Data age. Public verifiability offers a flexible functionality in real circumstance where the cloud service provider (CSP) may be untrusted or some malicious users may slander the CSP on purpose. However, sometimes the computational result is sensitive and is not willing to be exposed in the public verification phase. In this paper, we present a confidential-preserving public verifiable computation (CP-PVC) scheme for Evaluation of High Degree Polynomials. Compared with previous proposals, our scheme achieves confidentiality of computational result, while not sacrificing the property of public verifiability. We also provide the algorithm and experimental evaluation to show the efficiency of our scheme.

This paper proposes a novel scheme for RFID anti-counterfeiting by applying bisectional multivariate quadratic equations (BMQE) system into an RF tag data encryption. In the key generation process, arbitrarily choose two matrix sets (denoted as A and B) and a base Rab such that [AB] = λRABT, and generate 2n BMQ polynomials (denoted as p) over finite field Fq. Therefore, (Fq, p) is taken as a public key and (A, B, λ) as a private key. In the encryption process, the EPC code is hashed into a message digest dm. Then dmis padded to d'mwhich is a non-zero 2n×2n matrix over Fq. With (A, B, λ) and d'm, Smis formed as an n-vector over F2. Unlike the existing anti-counterfeit scheme, the one we proposed is based on quantum cryptography, thus it is robust enough to resist the existing attacks and has high security.

A General Similarity Measurement (GSM), which takes into account of both non-temporal and rich temporal aspects including temporal order, temporal duration and temporal gap, is proposed for state-sequence matching. It is believed to be versatile enough to subsume representative existing measurements as its special cases.

The notion of time is a very interesting and exciting subject both in science and everyday life. One of the fundamental questions is: what is time composed of? While the traditional time structure is based on a set of points, a notion that has been prevalently adopted in classical physics and mathematics, it has also been noticed that intervals have been widely adopted for expression of commonsense temporal knowledge, especially in the domain of artificial intelligence. However, there has been a long-standing debate on whether intervals should be treated as primitive or not, leading to two different approaches to the treatment of intervals. In the first, intervals are modelled as derived objects constructed from points, e.g. sets of points, or pairs of points. In the second, intervals are taken as primitive themselves. This article provides a critical examination of these two approaches. By means of proposing a definition of intervals in terms of points and types, we shall demonstrate that, while the two different approaches have been viewed as rivals in the literature, they are actually reducible to logically equivalent expressions under some requisite interpretations, and therefore they can also be viewed as allies.

The notion of time plays a vital and ubiquitous role of a common universal reference. In knowledge-based systems, temporal information is usually represented in terms of a collection of statements, together with the corresponding temporal reference. This paper introduces a visualized consistency checker for temporal reference. It allows expression of both absolute and relative temporal knowledge, and provides visual representation of temporal references in terms of directed and partially weighted graphs. Based on the temporal reference of a given scenario, the visualized checker can deliver a verdict to the user as to whether the scenario is temporally consistent or not, and provide the corresponding analysis/diagnosis.

Identification, when sought, is not necessarily obtained. Operational guidance that is normatively acceptable may be necessary for such cases. We proceed to formalize and illustrate modes of exchanges of individual identity, and provide procedures of recovery strategies in specific prescriptions from an ancient body of law for such situations when, for given types of purposes, individuals of some relevant kind had become intermixed and were undistinguishable. Rules were devised, in a variety of domains, for coping with situations that occur if and when the goal of identification was frustrated. We propose or discuss mathematical representations of such recovery procedures.

The so-called dividing instant (DI) problem is an ancient historical puzzle encountered when attempting to represent what happens at the boundary instant which divides two successive states. The specification of such a problem requires a thorough exploration of the primitives of the temporal ontology and the corresponding time structure, as well as the conditions that the resulting temporal models must satisfy. The problem is closely related to the question of how to characterize the relationship between time periods with positive duration and time instants with no duration. It involves the characterization of the ‘closed’ and ‘open’ nature of time intervals, i.e. whether time intervals include their ending points or not. In the domain of artificial intelligence, the DI problem may be treated as an issue of how to represent different assumptions (or hypotheses) about the DI in a consistent way. In this paper, we shall examine various temporal models including those based solely on points, those based solely on intervals and those based on both points and intervals, and point out the corresponding DI problem with regard to each of these temporal models. We shall propose a classification of assumptions about the DI and provide a solution to the corresponding problem.

This paper presents a reified temporal logic for representing and reasoning about temporal and non-temporal relationships between non-temporal assertions. A clear syntax and semantics for the logic is formally provided. Three types of predicates, temporal predicates, non-temporal predicates and meta-predicates, are introduced. Terms of the proposed language are partitioned into three types, temporal terms, non-temporal terms and propositional terms. Reified propositions consist of formulae with each predicate being either a temporal predicate or a meta-predicate. Meta-predicates may take both temporal terms and propositional terms together as arguments or take propositional terms alone. A standard formula of the classical first-order language with each predicate being a non-temporal predicate taking only non-temporal terms as arguments is reified as just a propositional term. A general time ontology has been provided which can be specialized to a variety of existing temporal systems. The new logic allows one to predicate and quantify over propositional terms while according a special status of time; for example, assertions such as ‘effects cannot precede their causes’ is ensured in the logic, and some problematic temporal aspects including the delay time between events and their effects can be conveniently expressed. Applications of the logic are presented including the characterization of the negation of properties and their contextual sentences, and the expression of temporal relations between actions and effects.

This paper presents a temporal database model which allows the expression of relative temporal knowledge of data transaction and data validity times. The system is founded on an extension to Allen's axiomitization of time, given previously by the authors, which takes both intervals and points as primitive time elements. A general retrieval mechanism is presented for a database with a purely relative temporal knowledge which allows queries with temporal constraints in terms of any logical combination of Allen's temporal predicates. When absolute temporal duration knowledge is added, the consistency checking algorithm upon which the inference mechanism is based reduces to a linear programming problem. A class of databases, termed time-limited databases, is introduced as a practical solution to the problem of computational complexity of retrieval. This class allows absolute and relative time knowledge in a form which is suitable for many practical applications, where relative temporal information is only occasionally needed. The architecture of such a system is given, and it is shown that the efficient retrieval mechanisms for absolute-time-stamped databases may be adapted to time-limited databases.

In this paper, a first-order theory of time is proposed as an underlying framework for most of the representative temporal models in artificial intelligence. The theory treats both points and intervals as primitive on an equal footing, and is shown to be powerful enough to subsume the interval based theories of Allen and Hayes, the point based theories of Bruce, of McDermott, and the interval and point based theories of Vilain and Knight and Ma. The approach is different from that of Ladkin, of Van Beek, of Dechter, Meiri and Pearl, and of Maiocchi, which is either to construct intervals out of points, or to treat points and intervals separately. Formal definitions are presented to characterize the open and closed nature of primitive intervals. The axiomatization allows non-linear time structures such as branching time and parallel time. Additional axioms specifying the linearity and density of time are separately presented.

This paper examines J.F. Allen's interval-based theory of action and time and the corresponding revisions suggested by A.P. Galton which have been proposed to accommodate the representation of facts concerning continuous change. Agreeing with Galton's argument that Allen's system needs revisions by means of diversifying the temporal ontology to include points, we show that Galton's determination to define time points in terms of the ‘meeting places’ of time intervals does not, as it stands, axiomatize points on the same footing as intervals, and hence that some problems still remain in these revisions. It is shown that it is necessary to revise the fundamental axioms about time itself so as to extend the abstract concept of time elements to include both intervals and points, and to extend the temporal relations between intervals to address points as well. We provide there a further revised theory which overcomes the problems in Allen's and Galton's systems. The revised system utilizes a new axiomatization of time, given previously by the authors, as the underlying temporal basis. A diversification of the range of properties/occurrences over intervals and points is also proposed which may replace both Allen's and Galton's results.