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Examines the property of semantic correctness of programs written in a shared-memory parallel language. Requirements in parallel programming; Importance of sequential consistency of a multiprocess device to parallel programming; Theorem of semantic equivalence.

Transfer learning, which focuses on finding a favorable representation for instances of different domains based on auxiliary data, can mitigate the divergence between domains through knowledge transfer. Recently, increasing efforts on transfer learning have employed deep neural networks (DNN) to learn more robust and higher level feature representations to better tackle cross-media disparities. However, only a few articles consider the correction and semantic matching between multi-layer heterogeneous domain networks. In this article, we propose a deep semantic mapping model for heterogeneous multimedia transfer learning (DHTL) using co-occurrence data. More specifically, we integrate the DNN with canonical correlation analysis (CCA) to derive a deep correlation subspace as the joint semantic representation for associating data across different domains. In the proposed DHTL, a multi-layer correlation matching network across domains is constructed, in which the CCA is combined to bridge each pair of domain-specific hidden layers. To train the network, a joint objective function is defined and the optimization processes are presented. When the deep semantic representation is achieved, the shared features of the source domain are transferred for task learning in the target domain. Extensive experiments for three multimedia recognition applications demonstrate that the proposed DHTL can effectively find deep semantic representations for heterogeneous domains, and it is superior to the several existing state-of-the-art methods for deep transfer learning. [ABSTRACT FROM AUTHOR]

The next generation of Internet services is driven by users and user-generated content. The complex nature of user behavior makes it highly challenging to manage and secure online services. On one hand, service providers cannot effectively prevent attackers from creating large numbers of fake identities to disseminate unwanted content (e.g., spam). On the other hand, abusive behavior from real users also poses significant threats (e.g., cyberbullying). In this article, we propose clickstream models to characterize user behavior in large online services. By analyzing clickstream traces (i.e., sequences of click events from users), we seek to achieve two goals: (1) detection: to capture distinct user groups for the detection ofmalicious accounts, and (2) understanding: to extract semantic information from user groups to understand the captured behavior. To achieve these goals, we build two related systems. The first one is a semisupervised system to detect malicious user accounts (Sybils). The core idea is to build a clickstream similarity graph where each node is a user and an edge captures the similarity of two users' clickstreams. Based on this graph, we propose a coloring scheme to identify groups of malicious accounts without relying on a large labeled dataset. We validate the system using groundtruth clickstream traces of 16,000 real and Sybil users from Renren, a large Chinese social network. The second system is an unsupervised system that aims to capture and understand the fine-grained user behavior. Instead of binary classification (malicious or benign), this model identifies the natural groups of user behavior and automatically extracts features to interpret their semantic meanings. Applying this system to Renren and another online social network,Whisper (100K users), we help service providers identify unexpected user behaviors and even predict users' future actions. Both systems received positive feedback from our industrial collaborators including Renren, LinkedIn, and Whisper after testing on their internal clickstream data. [ABSTRACT FROM AUTHOR]

Differential privacy is a notion of confidentiality that allows useful computations on sensible data while protecting the privacy of individuals. Proving differential privacy is a difficult and error-prone task that calls for principled approaches and tool support. Approaches based on linear types and static analysis have recently emerged; however, an increasing number of programs achieve privacy using techniques that fall out of their scope. Examples include programs that aim for weaker, approximate differential privacy guarantees and programs that achieve differential privacy without using any standard mechanisms. Providing support for reasoning about the privacy of such programs has been an open problem. We report on CertiPriv, a machine-checked framework for reasoning about differential privacy built on top of the Coq proof assistant. The central component of CertiPriv is a quantitative extension of probabilistic relational Hoare logic that enables one to derive differential privacy guarantees for programs from first principles. We demonstrate the applicability of CertiPriv on a number of examples whose formal analysis is out of the reach of previous techniques. In particular, we provide the first machine-checked proofs of correctness of the Laplacian, Gaussian, and exponential mechanisms and of the privacy of randomized and streaming algorithms from the literature. [ABSTRACT FROM AUTHOR]

XML data projection (or pruning) is a natural optimization for main memory query engines: given a query Q over a document D, the subtrees of D that are not necessary to evaluate Q are pruned, thus producing a smaller document D'; the query Q is then executed on D', hence avoiding to allocate and process nodes that will never be reached by Q. In this article, we propose a new approach, based on types, that greatly improves current solutions. Besides providing comparable or greater precision and far lesser pruning overhead, our solution--unlike current approaches--takes into account backward axes, predicates, and can be applied to multiple queries rather than just to single ones. A side contribution is a new type system for XPath able to handle backward axes. The soundness of our approach is formally proved. Furthermore, we prove that the approach is also complete (i.e., yields the best possible type-driven pruning) for a relevant class of queries and Schemas. We further validate our approach using the XMark and XPathMark benchmarks and show that pruning not only improves the main memory query engine's performances (as expected) but also those of state of the art native XML databases. [ABSTRACT FROM AUTHOR]

Schema matching and value mapping across two information sources, such as databases, are critical information aggregation tasks. Before data can be integrated from multiple tables, the columns and values within the tables must be matched. The complexities of both these problems grow quickly with the number of attributes to be matched and due to multiple semantics of data values. Traditional research has mostly tackled schema matching and value mapping independently, and for categorical (discrete-valued) attributes.We propose novel methods that leverage value mappings to enhance schema matching in the presence of opaque column names for schemas consisting of both continuous and discrete-valued attributes. An additional source of complexity is that a discrete-valued attribute in one schema could in fact be a quantized, encoded version of a continuous-valued attribute in the other schema. In our approach, which can tackle both "onto" and bijective schema matching, the fitness objective for matching a pair of attributes from two schemas exploits the statistical distribution over values within the two attributes. Suitable fitness objectives are based on Euclidean-distance and the data log-likelihood, both of which are applied in our experimental study. A heuristic local descent optimization strategy that uses two-opt switching to optimize attribute matches, while simultaneously embedding value mappings, is applied for our matching methods. Our experiments show that the proposed techniques matched mixed continuous and discrete-valued attribute schemas with high accuracy and, thus, should be a useful addition to a framework of (semi) automated tools for data alignment. [ABSTRACT FROM AUTHOR]

Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read/Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future. [ABSTRACT FROM AUTHOR]

An alarming trend in recent malware incidents is that they are armed with stealthy techniques to detect, evade, and subvert malware detection facilities of the victim. On the defensive side, a fundamental limitation of traditional host-based antimalware systems is that they run inside the very hosts they are protecting ("in-the-box"), making them vulnerable to counter detection and subversion by malware. To address this limitation, recent solutions based on virtual machine (VM) technologies advocate placing the malware detection facilities outside of the protected VM ("out-ofthe- box"). However, they gain tamper resistance at the cost of losing the internal semantic view of the host, which is enjoyed by "in-the-box" approaches. This poses a technical challenge known as the semantic gap. In this article, we present the design, implementation, and evaluation of VMwatcher-an "outof- the-box" approach that overcomes the semantic gap challenge. A new technique called guest view casting is developed to reconstruct internal semantic views (e.g., files, processes, and kernel modules) of a VM nonintrusively from the outside. More specifically, the new technique casts semantic definitions of guest OS data structures and functions on virtual machine monitor (VMM)- level VM states, so that the semantic view can be reconstructed. Furthermore, we extend guest view casting to reconstruct details of system call events (e.g., the process that makes the system call as well as the system call number, parameters, and return value) in the VM, enriching the semantic view. With the semantic gap effectively narrowed, we identify three unique malware detection and monitoring capabilities: (i) view comparison-based malware detection and its demonstration in rootkit detection; (ii) "out-of-the-box" deployment of off-the-shelf anti malware software with improved detection accuracy and tamper-resistance; and (iii) nonintrusive system call monitoring for malware and intrusion behavior observation. We have implemented a proof-of-concept VMwatcher prototype on a number of VMM platforms. Our evaluation experiments with real-world malware, including elusive kernel-level rootkits, demonstrate VMwatcher's practicality and effectiveness. [ABSTRACT FROM AUTHOR]

Tim Berners-Lee claimed in 2001 that "the power of the Web is in its universality. Access by every- one regardless of disability is an essential aspect". A considerable amount of work has been done to make the web accessible to those with sensory or motor disability, with an increasing number of government and enterprise intranet webs being "accessible", and also with some consortiums and groups seriously approaching this commitment. Some authors, such as Harrysson, have already highlighted the need for a cognitively accessible web. However, in spite of good intentions, there has been little work to date that has tackled this task. At least until now, the existing WAI and NI4 recommendations about cognitive disability are extremely difficult (if not impossible) to test, as they are only general recommendations. This article explains an alternative Web that has been constructed and tested on a sample of participants with cognitive disabilities (N = 20) with positive results encouraging us to dedicate more effort to fine tune their requirements regarding specific cognitive deficits and automating the process of creating and testing cognitively accessible web content. This alternative web implies the use of a simplified web browser and an adequate web design. Discussion of the need to have several levels of cognitive accessibility, equivalent (although not identical) content for this collective and the need for testable protocols of accessibility that support these people's needs is also included. This article finishes with conclusions about the potential impact of accessible pages in the daily life of people suffering from cognitive deficits, outlining the features to be considered within a user profile specification that support cognitive difficulties and with reflections about the suitability of Semantic Web Technologies for future developments in this field. [ABSTRACT FROM AUTHOR]

This article addresses the problem of processing the annotations of preexisting video productions to enable reuse and repurposing of metadata. We introduce the concept of automatic content-based editing of preexisting semantic home video metadata. We propose a formal representation and implementation techniques for reusing and repurposing semantic video metadata in concordance with the actual video editing operations. A novel representation for metadata editing is proposed and an implementation framework for editing the metadata in accordance with the video editing operations is demonstrated. Conflict resolution and regularization operations are defined and implemented in the context of the video metadata editing operations. [ABSTRACT FROM AUTHOR]

In this article, we describe the MX-Onto ontology for providing a Semantic Web compatible representation of music resources based on their context. The context representation is realized by means of an OWL ontology that describes music information and that defines rules and classes for a flexible genre classification. By flexible classification we mean that the proposed approach enables capturing the subjective interpretation of music genres by defining multiple membership relations between a music resource and the corresponding music genres, thus supporting context-based and proximity-based search of music resources. [ABSTRACT FROM AUTHOR]

The article cites a study on the operational semantics of a computational system. It investigates the potential of using term-level abstractions and proof-level abstractions in creating high-level specifications. The study presents a sequent calculus that provides the local notion of this scope. The resulting logic can be used to encode a number of examples involving abstractions.

Describes the generic properties of semantic data models and presents a representative selection of models that have been proposed in the mid-1970. Features that are lack in generation data models.

The article focuses on multimedia database management systems. Due to the heterogeneous nature of multimedia data management systems, which are intended to store, transport, display and in general manage such data, must have considerably more capabilities than conventional information-management systems. This article elaborates on issues pertaining to multimedia database management and discuss technical challenges involved in developing a general-purpose multimedia. One can view these issues as specification requirements for this multimedia and focus the discussion on a reference architecture for such a system, development of formal semantic modeling techniques for multimedia information, especially for video and image data. These models should be rich in capabilities for abstracting multimedia information and capturing semantics. They should be able to provide canonical representations of complex images, scenes, and events in terms of objects and their spatio-tethporal behavior. These models need to be compared and evaluated.

Comments on an article by Toby J. Teorey, Dongqing Yang and James P. Fry which presented an approach to designing relational databases. Information on several problems caused by redundant relationships; Impact of leaving the redundant relationships at the extended entity-relationship step; Function of additional semantic information.