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Build working implementations of hash tables, written in the C programming language. This book starts with simple first attempts devoid of collision resolution strategies, and moves through improvements and extensions illustrating different design ideas and approaches, followed by experiments to validate the choices. Hash tables, when implemented and used appropriately, are exceptionally efficient data structures for representing sets and lookup tables, providing low overhead, constant time, insertion, deletion, and lookup operations. \"The joys of Hashing\" walks you through the implementation of efficient hash tables and the pros and cons of different design choices when building tables. The source code used in the book is available on GitHub for your re-use and experiments. You will: Master the basic ideas behind hash tables ; Carry out collision resolution, including strategies for handling collisions and their consequences for performance ; Resize or grow and shrink tables as needed ; Store values by handling when values must be stored with keys to make general sets and maps.

Hash tables embody a paradox of deterministic structure that emerges from controlled randomness. They have evolved from simple associative arrays into algorithmic engines that operate near the physical and probabilistic limits of computation. This review unifies five decades of developments across universal and perfect hashing, collision-resolution strategies, and concurrent and hardware-aware architectures. The synthesis shows that modern hash tables act as thermodynamic regulators of entropy, able to transform stochastic mappings into predictable constant-time access. Recent advances in GPU and NUMA-aware designs, lock-free and persistent variants, and neural or quantum-assisted approaches further expand their capabilities. The analysis presents hash tables as models that evolve order within randomness and expand their relevance from classical computation to quantum and neuromorphic frontiers.

Direct usage of original biometric features/templates definitely leads to serious privacy leakage. In biometric cryptosystems, a biometric key is generated and then strictly protected with a one-way function. However, it is highly difficult to balance the template size and accuracy. Palmprint has many remarkable strengths, so it is considered as a promising biometric modality. In our previous work, deep hashing network (DHN) was leveraged to extract discriminative deep hashing code (DHC) of palmprint. In this paper, a palmprint fuzzy commitment (FC) is proposed based on DHC. A palmprint FC is proposed based on DHC. The DHC has high accuracy, small size, strong robustness, and is free from shift-matching for dislocation problems, so the proposed palmprint FC can satisfactorily balance the accuracy, storage cost and computational complexity. In addition, the DHCs of multi-spectral palmprints are concatenated and the bits are selected according to discrimination power analysis, so the accuracy is further improved. The sufficient experimental results show that, when B, N and R spectrums are fused and only 292 bits are selected, EER can be 0.0001%.

Hashing has been widely investigated for large-scale image retrieval due to its search effectiveness and computation efficiency. In this work, we propose a novel Semantic Guided Hashing method coupled with binary matrix factorization to perform more effective nearest neighbor image search by simultaneously exploring the weakly-supervised rich community-contributed information and the underlying data structures. To uncover the underlying semantic information from the weakly-supervised user-provided tags, the binary matrix factorization model is leveraged for learning the binary features of images while the problem of imperfect tags is well addressed. The uncovered semantic information enables to well guide the discrete hash code learning. The underlying data structures are discovered by adaptively learning a discriminative data graph, which makes the learned hash codes preserve the meaningful neighbors. To the best of our knowledge, the proposed method is the first work that incorporates the hash code learning, the semantic information mining and the data structure discovering into one unified framework. Besides, the proposed method is extended to one deep approach for the optimal compatibility of discriminative feature learning and hash code learning. Experiments are conducted on two widely-used social image datasets and the proposed method achieves encouraging performance compared with the state-of-the-art hashing methods.

In recent years, deep-network-based hashing has gained prominence in image retrieval for its ability to generate compact and efficient binary representations. However, most existing methods predominantly focus on high-level semantic features extracted from the final layers of networks, often neglecting structural details that are crucial for capturing spatial relationships within images. Achieving a balance between preserving structural information and maximizing retrieval accuracy is the key to effective image hashing and retrieval. To address this challenge, we introduce Multiscale Deep Feature Fusion for Supervised Hashing (MDFF-SH), a novel approach that integrates multiscale feature fusion into the hashing process. The hallmark of MDFF-SH lies in its ability to combine low-level structural features with high-level semantic context, synthesizing robust and compact hash codes. By leveraging multiscale features from multiple convolutional layers, MDFF-SH ensures the preservation of fine-grained image details while maintaining global semantic integrity, achieving a harmonious balance that enhances retrieval precision and recall. Our approach demonstrated a superior performance on benchmark datasets, achieving significant gains in the Mean Average Precision (MAP) compared with the state-of-the-art methods: 9.5% on CIFAR-10, 5% on NUS-WIDE, and 11.5% on MS-COCO. These results highlight the effectiveness of MDFF-SH in bridging structural and semantic information, setting a new standard for high-precision image retrieval through multiscale feature fusion.

Unsupervised cross-modal hashing has attracted much attention in large-scale cross-modal retrieval due to its low storage consumption and high retrieval efficiency. However, existing unsupervised hashing methods fail to capture the relevance of implicit knowledge in cross-modal large models (e.g.CLIP), which leads to an incomplete representation of the semantic information of the hashing codes. To solve this problem, we introduce in this paper a new approach called Dark Knowledge Association Guided Hashing (DKAGH) for unsupervised cross-modal retrieval. Specifically, we propose a new cross-modal interaction attention module to enhance heterogeneous semantic interactions while extracting rich implicit information in CLIP models via a similarity distillation module to optimise cross-modal similarity relations. We then propose a concept-aware semantic hashing module which designs concept-aware encoders to decouple the multimodal features for capturing implicit concept representation and explore contrast loss on concept-aware hashing codes to align the heterogeneous modalities for multimodal hash learning. Extensive experiments on three cross-modal retrieval datasets demonstrate that DKAGH achieves the state-of-the-art performance.

We introduce propagation kernels , a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capture structural information encoded in node labels, attributes, and edge information. This has two benefits. First, off-the-shelf propagation schemes can be used to naturally construct kernels for many graph types, including labeled, partially labeled, unlabeled, directed, and attributed graphs. Second, by leveraging existing efficient and informative propagation schemes, propagation kernels can be considerably faster than state-of-the-art approaches without sacrificing predictive performance. We will also show that if the graphs at hand have a regular structure, for instance when modeling image or video data, one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes. We support our contributions by exhaustive experiments on a number of real-world graphs from a variety of application domains.

Blockchain has the potential to disrupt the current modes of patient data access, accumulation, contribution, exchange, and control. Using interoperability standards, smart contracts, and cryptographic identities, patients can securely exchange data with providers and regulate access. The resulting comprehensive, longitudinal medical records can significantly improve the cost and quality of patient care for individuals and populations alike. This work presents HealthChain, a novel patient-centered blockchain framework. The intent is to bolster patient engagement, data curation, and regulated dissemination of accumulated information in a secure, interoperable environment. A mixed-block blockchain is proposed to support immutable logging and redactable patient blocks. Patient data are generated and exchanged through Health Level-7 Fast Healthcare Interoperability Resources, allowing seamless transfer with compliant systems. In addition, patients receive cryptographic identities in the form of public and private key pairs. Public keys are stored in the blockchain and are suitable for securing and verifying transactions. Furthermore, the envisaged system uses proxy re-encryption (PRE) to share information through revocable, smart contracts, ensuring the preservation of privacy and confidentiality. Finally, several PRE improvements are offered to enhance performance and security. The framework was formulated to address key barriers to blockchain adoption in health care, namely, information security, interoperability, data integrity, identity validation, and scalability. It supports 16 configurations through the manipulation of 4 modes. An open-source, proof-of-concept tool was developed to evaluate the performance of the novel patient block components and system configurations. To demonstrate the utility of the proposed framework and evaluate resource consumption, extensive testing was performed on each of the 16 configurations over a variety of scenarios involving a variable number of existing and imported records. The results indicate several clear high-performing, low-bandwidth configurations, although they are not the strongest cryptographically. Of the strongest models, one's anticipated cumulative record size is shown to influence the selection. Although the most efficient algorithm is ultimately user specific, Advanced Encryption Standard-encrypted data with static keys, incremental server storage, and no additional server-side encryption are the fastest and least bandwidth intensive, whereas proxy re-encrypted data with dynamic keys, incremental server storage, and additional server-side encryption are the best performing of the strongest configurations. Blockchain is a potent and viable technology for patient-centered access to and exchange of health information. By integrating a structured, interoperable design with patient-accumulated and generated data shared through smart contracts into a universally accessible blockchain, HealthChain presents patients and providers with access to consistent and comprehensive medical records. Challenges addressed include data security, interoperability, block storage, and patient-administered data access, with several configurations emerging for further consideration regarding speed and security.

Background Molecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules. Results Here we set out to design a new fingerprint suitable for both small and large molecules by combining substructure and atom-pair concepts. Our quest resulted in a new fingerprint called MinHashed atom-pair fingerprint up to a diameter of four bonds (MAP4). In this fingerprint the circular substructures with radii of r  = 1 and r  = 2 bonds around each atom in an atom-pair are written as two pairs of SMILES, each pair being combined with the topological distance separating the two central atoms. These so-called atom-pair molecular shingles are hashed, and the resulting set of hashes is MinHashed to form the MAP4 fingerprint. MAP4 significantly outperforms all other fingerprints on an extended benchmark that combines the Riniker and Landrum small molecule benchmark with a peptide benchmark recovering BLAST analogs from either scrambled or point mutation analogs. MAP4 furthermore produces well-organized chemical space tree-maps (TMAPs) for databases as diverse as DrugBank, ChEMBL, SwissProt and the Human Metabolome Database (HMBD), and differentiates between all metabolites in HMBD, over 70% of which are indistinguishable from their nearest neighbor using substructure fingerprints. Conclusion MAP4 is a new molecular fingerprint suitable for drugs, biomolecules, and the metabolome and can be adopted as a universal fingerprint to describe and search chemical space. The source code is available at https://github.com/reymond-group/map4 and interactive MAP4 similarity search tools and TMAPs for various databases are accessible at http://map-search.gdb.tools/ and http://tm.gdb.tools/map4/ .

Face recognition is one of the most important research topics in computer vision. Indeed, the face is an important means of communication with humans and it is highly needed for daily contact. Face recognition technology is applied in many biometric applications such as security, video surveillance, access control systems, and forensics. In this technology, hashing has recently made encouraging progress due to its fast retrieval speed and low storage cost. In this work, we propose an effective face recognition framework based on hashing functions. It attempts to leverage a cascaded architecture with two stages of analyzing different visual information based on image hashing. Specifically, we first introduce a filter to overlook a large number of dissimilar identities in terms of local visual information. Similar identities are found quickly through random independent hash functions inspired by Locality Sensitive Hashing (LSH). Next, we further refine candidates and recognize the most similar identities according to global visual information. The global feature is obtained by hashing each face into a high-quality binary feature space using Discrete Cosine Transform (DCT) coefficients. The proposed method is evaluated on three well-known and one combined face dataset. The obtained results, and the provided face recognition application program, demonstrate that the proposed framework improves the recognition rate and significantly reduces recognition time.