Generative Zero-Shot Learning (GZSL) methods address the challenge of recognizing unseen classes by synthesizing visual features, thereby converting ZSL into a supervised learning task. However, existing approaches are predominantly constrained to two multi-stage strategies: pre-generation prior knowledge enhancement and post-generation feature refinement. These paradigms often suffer from error propagation across stages, ultimately limiting generation quality and representational fidelity. To overcome these limitations, we propose DKGZSL, a novel generative framework that injects dynamic visual-semantic knowledge directly into the feature synthesis process, effectively unifying generation and refinement into a single cohesive stage. Specifically, a Knowledge Transfer Network (KTN) is introduced to convert semantic information into hierarchical visual knowledge representations. To ensure accurate semanticvisual alignment, we further design a Semantic-Oriented Visual Refinement (SOVR) module that reshapes real visual features into semantically aligned and noise-suppressed representations, providing precise guidance for the KTN. Moreover, hierarchical knowledge extracted from each KTN layer is progressively transmitted to the generator via Meta-Fusion Units (MFUs), enabling dynamic semantic guidance and improving generation quality. Extensive experiments on three benchmark datasets demonstrate that DKGZSL achieves consistent state-of-the-art performance with both ResNet-101 and ViT-B/16 feature extractors. Comprehensive ablation studies further confirm the effectiveness and complementarity of each proposed component. The code is available at https://github.com/JingHu-gdut/DKGZSL.

Universal domain adaptation seeks to extend knowledge from a labeled source domain to an unlabeled target domain, unconstrained by label space alignment. It is challenging to align shared categories and separate private ones without prior category overlap information. Existing methods often rely heavily on source domain information to learn a transferable classifier, neglecting the relationships between the manifold structures inherent within the two domains. This paper introduces a novel framework, called Guiding cOntrastive Learning with out-of-distribution Detection (GOLD) for universal domain adaptation. GOLD employs an instance-prototype hybrid contrastive learning approach with self-attention to reveal domain structures. Subsequently, it constructs a residual subspace from source prototypes to filter unknown categories and refines neighborhood structures through instance-level virtual adversarial training to reduce noise. Experiments on three datasets show GOLD surpasses current methods in various UniDA settings.

Sketch-based 3D shape retrieval has attracted increasing attention in recent years. Most existing methods fail to address the zero-shot scenario, and the few dedicated to zero-shot learning encounter the following two issues: 1) the features learned by these methods lack informativeness and generalization, rendering them ineffective in identifying unseen samples; 2) the generation of low-quality samples, aimed at facilitating the recognition of unseen categories, paradoxically diminishes their ability to identify these unseen classes. This paper introduces a novel contrastive disentanglement generative adversarial networks (CoDi) tailored for zero-shot sketch-based 3D shape retrieval. Initially, we introduce a paradoxical feature construction approach designed to assist the networks in capturing certain low-level features. Despite their weak semantic relevance, these features play a crucial role in sample recognition. Subsequently, a SemContrast fusion module is employed to align the semantic space with the prototype embedding space of categories. This alignment facilitates knowledge transfer to unseen classes and promotes the generation of high-quality samples. The networks are jointly trained on real and generated samples to achieve retrieval for unseen categories. Extensive experiments demonstrate a significant improvement in retrieval performance for unseen categories using our method.

Cross-modal hashing has gained considerable attention in cross-modal retrieval due to its low storage cost and prominent computational efficiency. However, preserving more semantic information in the compact hash codes to bridge the modality gap still remains challenging. Most existing methods unconsciously neglect the influence of modality-private information on semantic embedding discrimination, leading to unsatisfactory retrieval performance. In this paper, we propose a novel deep cross-modal hashing method, called Semantic Disentanglement Adversarial Hashing (SDAH), to tackle these challenges for cross-modal retrieval. Specifically, SDAH is designed to decouple the original features of each modality into modality-common features with semantic information and modality-private features with disturbing information. After the preliminary decoupling, the modality-private features are shuffled and treated as positive interactions to enhance the learning of modality-common features, which can significantly boost the discriminative and robustness of semantic embeddings. Moreover, the variational information bottleneck is introduced in the hash feature learning process, which can avoid the loss of a large amount of semantic information caused by the high-dimensional feature compression. Finally, the discriminative and compact hash codes can be computed directly from the hash features. A large number of comparative and ablation experiments show that SDAH achieves superior performance than other state-ofthe- art methods.

Cross-modal retrieval has attracted widespread attention in many cross-media similarity search applications, especially image-text retrieval in the fields of computer vision and natural language processing. Recently, visual and semantic embedding (VSE) learning has shown promising improvements on image-text retrieval tasks. Most existing VSE models employ two unrelated encoders to extract features, then use complex methods to contextualize and aggregate those features into holistic embeddings. Despite recent advances, existing approaches still suffer from two limitations: 1) without considering intermediate interaction and adequate alignment between different modalities, these models cannot guarantee the discriminative ability of representations; 2) existing feature aggregators are susceptible to certain noisy regions, which may lead to unreasonable pooling coefficients and affect the quality of the final aggregated features. To address these challenges, we propose a novel cross-modal retrieval model containing a well-designed alignment module and a novel multimodal fusion encoder, which aims to learn adequate alignment and interaction on aggregated features for effectively bridging the modality gap. Experiments on Microsoft COCO and Flickr30k datasets demonstrates the superiority of our model over the state-of-the-art methods.

This paper introduces a generative system for in-battle real-time commentary in mobile MOBA games. Event commentary is important for battles in MOBA games, which is applicable to a wide range of scenarios like live streaming, e-sports commentary and combat information analysis. The system takes real-time match statistics and events as input, and an effective transform method is designed to convert match statistics and utterances into consistent encoding space. This paper presents the general framework and implementation details of the proposed system, and provides experimental results on large-scale real-world match data.

Most existing unsupervised domain adaptation methods learn domain-invariant representations with entangled domain in-formation, semantic information, and instance information. Differently, in this paper, we propose a Triple Disentangling Network (TDN), to disentangle these three types of information and then predict the target labels merely using semantic information. Specifically, TDN consists of a reconstruction module and a disentanglement module. In the reconstruction module, TDN utilizes a variational auto-encoder to re-construct the domain, semantic, and instance latent variables behind the data. In the disentanglement module, adversar-ial learning, discriminative clustering, and instance separation are seamlessly integrated to disentangle these three sets of re-constructed latent variables. Significantly, TDN can not only effectively alleviate the negative transfer of outliers through disentangling instance information, but also disentangle se-mantic information more thoroughly by exploring discriminative structure knowledge. Experimental studies on two bench-mark datasets demonstrate the superiority of TDN.

As Data Science has recently become a trend of the innovation of building new academic programs in the U.S. higher education, a holistic review and analysis of the curriculum development in a close field called Informatics or Bioinformatics, occurred more than a decade ago, seems to be beneficial and necessary in helping and supporting the healthy growth of Data Science programs. In this paper, a thorough comparative study between Informatics and Data Science was presented through comparing and investigating the similarities and differences of their curriculum structures, bodies of domain knowledge, technical skill sets, research areas, faculty preparation, student background, as well as the lessons learnt from the development of Informatics programs and challenges in developing Data Science programs. It is believed that the recommendations drawn from this study will assist educators to build their Data Science programs with the confidence and determination but without hesitation and fear of misstepping along the way in promoting and building their new programs.

To expedite the forensic investigation process in the cloud, excessive and yet volatile data need to be acquired, transmitted, and analyzed in a timely manner. A common assumption for most existing forensic systems is that credible data can always be collected from a cloud infrastructure, which might be susceptible to various exploits. In this paper, we present the design, implementation, and evaluation of LiveForen, a system that enforces a trustworthy forensic data acquisition and transmission process in the cloud, whose computer platforms' integrity has been verified. To fulfill this objective, we propose two secure protocols that verify the fingerprints of the computer platforms, as well as the attributes of the human agents, by taking advantage of the trusted platform module and the attribute-based encryption. To transmit forensic data as a data stream and verify its integrity at the same time, a unique fragile watermark is embedded into the data stream without altering the data itself. The watermark allows not only the data integrity to be verified but also any malicious data manipulation to be localized, with minimum communication overhead. The experimental results demonstrate that LiveForen achieves good scalability and limited performance overhead for authentication, data transmission, and integrity verification in an Infrastructure-as-a-Service cloud environment.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

While the market for the E-Discovery (Electronic Discovery) is predicted to grow rapidly in coming years, the anticipated shortage of electronic discovery professionals is a result of the lack of education programs in this emerging field. In this paper, an undergraduate minor program in electronic discovery is proposed based on the experience and accomplishment of an established computer forensics program at a public university. Not only will the new program meet the demand of a booming job market, it is also an inspiring practice in expanding computer science education to a non-programming-centric field.

A NoSQL, also called a "Non-Relational" or "Not only SQL," database system provides an approach to data management and database design for very large sets of distributed data and real-time web applications. A NoSQL database system is also a popular data storage for information retrieval because it supports better scalability, availability, and faster data access while comparing with traditional relational database management systems (RDBMS). What the RDBMS data needs is predictable as its data is stored in structured tables by defining the relationship between the different columns. In contrary the data in NoSQL databases does not need to be stored in a structured or fixed fashion. When performance and real-time access are more concerned than consistency, such as indexing and retrieving large numbers of records, NoSQL databases are more suitable than relational databases. With their obvious advantages in better performance, scalability, and flexibility, NoSQL databases have been adopted lately by many small businesses as they are moving their increasing business data into the clouds. However, the research on the security of a specific NoSQL database system or NoSQL database systems in general is very limited. Although there are many storage advantages in NoSQL databases, the need of quick and easy access to data has been seriously affected by the security issue of NoSQL databases. This paper examines the maturity of security measures for MongoDB, a typical NoSQL database system, with aspects in both attack and defense at the code level. The experimental testing on NoSQL injections is performed with JavaScript and PHP. After the demonstration on how a server-side JavaScript injection attack against a NoSQL database system reveals the customer's private data, two methods are discussed in preventing this type of security problems from happening. It is believed that our study will help database developers not only realizing that NoSQL database systems are not designed with security as a priority but also learning how to build a security layer to their organizations' NoSQL applications to avoid NoSQL injections.

Abstract As a new frontier for fighting against cyber crime and cyber terrorism, digital forensics has experienced a rapid development in the last decade. Many countries have created new laws and legal procedures, developed new technologies, and enhanced education and research in this emerging field. Japan is no exception. In this paper, we first provide a nutshell of the Japanese political structures, legal systems, and law enforcement practice, and then present an overview of updated and new laws, awareness programs, and the research activities in digital forensics. Our views on the current issues and future work are also discussed. We believe that the result of our study will provide an opportunity for the world to see what has been done and what we are doing in this field in Japan. Meanwhile, it also provides a foundation to advance the digital forensics research and development in Japan as well as in the world. Copyright © 2010 John Wiley & Sons, Ltd.

We have developed an innovative online tool for teaching introductory programming courses with the goals of engaging students in learning to program and increasing students' learning confidence. This system provides an online runtime environment that supports Java online compilation, execution, and verification with either Java console, GUI, or Web-based programs. In addition, the system can also support a synchronous, in-class, and instructor-led setting with instant assessment as well as an asynchronous, off-class, self-paced, and independent-study supplemental environment.

The Hough transform (HT) is a commonly used technique for the identification of straight lines in an image. The Hough transform can be equivalently computed using the Radon transform (RT), by performing line detection in the frequency domain through use of central-slice theorem. In this research, an advanced Radon transform is developed using a multilayer fractional Fourier transform, a Cartesian-to-polar mapping, and 1-D inverse Fourier transforms, followed by peak detection in the sinogram. The multilayer fractional Fourier transform achieves a more accurate sampling in the frequency domain, and requires no zero padding at the stage of Cartesian-to-polar coordinate mapping. Our experiments were conducted on mix-shape images, noisy images, mixed-thickness lines and a large data set consisting of 751,000 handwritten Chinese characters. The experimental results have shown that our proposed method outperforms all known representative line detection methods based on the standard Hough transform or the Fourier transform.

Many national studies on computer forensics have been reported in the United Kingdom, the United States, and Australia, except Japan. As one of the economic powers in the world and the only Asian country represented in the G8, Japan has been playing a critical role in Asia as well as in the world in fighting against the cyber crime and cyber terrorism. In this paper, we attempted to analyze the current development of computer forensics in Japan by reviewing its political structures, legal systems, law enforcement infrastructures, and academic development in computer forensics. The result of our study will foster the mutual understanding and promote the cooperation and collaboration between Japan and the world on fighting against the cyber crimes and the cyber terrorisms.

In recent years, embedded systems are becoming increasingly important due to their wide applications in every aspect of our society. By the year 2010, it is forecasted that 90% of the overall program code developed will be for embedded computing systems. The rapid growth of embedded systems results in a shortage of professionals for embedded software development. Despite the high need of embedded system professionals, embedded system is currently not well represented in academic programs. In offering such courses many schools face the challenges of the lack of suitable and affordable labs and scarce dedicated staff and faculty. Suitable embedded software design textbooks are also in demand.

Simple object access protocol (SOAP) is the communication protocol used by Web services to communicate between systems. Since SOAP messages have the ability to bypass firewalls and directly get processed by web servers, their security is critical to the security of the Web servers. This paper explores the security vulnerabilities of SOAP messages in a service-oriented architecture (SOA) environment and describes the implementation of the integrated application and protocol framework (IAPF) that can successfully combat the security threats. In addition to the discussion on how IAPF helps in the early detection of both XML injection and parameter tampering attacks, the details about the fundamental implementation of the IAPF mechanisms in supporting intrusion resistant SOAP messaging are also presented.

Providing consistent, instant, and detailed feedback to students has been a great challenge in teaching Web based computing. We present the prototype of an automated grading system called ProtoAPOGEE for enriching students' learning experience and elevating faculty productivity. Unlike other automated graders used in introductory programming classes, ProtoAPOGEE emphasizes the examination of quality attributes of student project submissions, in addition to the basic functionality requirements. The tool is able to generate step by step play-back guidance for failed test cases, hence providing informative feedback to help students make reflective and iterative improvements in learning.

Search Engine has proven its effectiveness for retrieval of information from World Wide Web. Traditionally, the search results are arranged in an ordered list by popularity and relevancy. However, the enormous size of matched Web pages causes inefficiency for users to locate the most relevant Web pages. A proper organization of the search result is important to improve its browsability of Web searching. In this paper, we proposed by performing Support Vector Clustering (SVC) on the search result to reorganize results in groups of similar context to facilitate effective browsing of search result by the users. SVC is a nonparametric clustering algorithm that can group clusters with arbitrary shapes and without the need to specify the number of clusters. It is a kernel clustering method that maps via a nonlinear function to a high dimension feature space. To obtain the optimal clustering result, choosing of the accurate parameters (kernel width and penalty coefficient) for SVC is crucial. In this paper, it proposed an automatic tuning method for SVC parameters to obtain the optimal result. The results from the experiment have proven the effectiveness and usefulness of above mentioned method. The performance is comparable to other popular clustering techniques.

The applicability of the security protocols, such as WS-Security, WS-Trust, WS-SecureConversation, WS-Federation, WS-Authorization, and WS-SecurityPolicy, is limited as they only protect SOA (Service Oriented Architecture) communication between two trusted parties with an established security association. The pervasiveness of Web services and SOAP API that can be invoked by anonymous consumers introduces security vulnerabilities are not addressed by the existing standards. In this paper, an Integrated Application and Protocol-based Framework is proposed to tackle the existing WS security problems. The proposed IAPF techniques are envisioned to be a part of the design and implementation structure of a Web service endpoint within the application and transaction handling logic of the SOAP/Web service producer. These techniques will empower application level Web services developers to design and implement SOA producers to the IAPF standard to firstly prevent DoS and DDoS based attacks and secondly mitigate the effects of these attacks.

Web service technology is a component-oriented and SOAP-based interoperable technology widely adopted in enterprise Application to Application (A2A), Business to Business (B2B), and Enterprise Application Integration (EAI) applications. Web Service component composite and connection models are the key issues to make Web service successful in the future. Web service standard and specification does not support event-based asynchronous callback which is very important for transaction or delayed business application. This paper presents a Callback Web services enable proxy approaches to implement callback in Web services without modifying existing Web services providers. In addition, a universal Callback reusable proxy design pattern is also proposed, which provides a platform, language, proprietary and technology independent service middleware for multiple clients to share and get services from multiple service providers in an asynchronous event-based implicit invocation communication mode .

This paper presents a preliminary survey of computer forensics programs in North America. It summarizes existing requirements for associate, bachelor's, and master's degree programs as well as certificate programs. It briefly discusses factors which must be considered when introducing a new program (curriculum design, faculty, students, facilities, and budget).

A prediction of the needed RF performance by using RF system simulation is indispensable when preparing commercial product for new market. This is in particular the reason for third generation (3G) wireless systems which are quite different from 2G TDMA/FDMA systems. In this case a simulation is performed for TD-SCDMA handset transmitter with the tool ADS (advanced design system). The described architecture in which the modulation is generated directly has single up conversion step. Both the baseband and RF part are simulated and analyzed, some usable results are achieved.