Analysis of User Editing Patterns in Ontology Development Projects

The number of victims suffering from crypto ransomware is increasing. Methods for detecting ransomware when it accesses target files or when it uses encrypting APIs have been studied. However, the former method is operated within an analysis sandbox, and the latter method can be avoided if the ransomware uses its own encrypting functions. To protect users, a detection method should be able to detect ransomware in the user's real-time environment and make it difficult for the ransomware to avoid detection. This paper proposes a detection method that satisfies these requirements by using human file-operating characteristics as a whitelist. We evaluate the effectiveness of our prototype method, which inspects the consistency between displayed documents and the user's editing operations.

Today, distributed teams collaboratively create and maintain more and more ontologies. To support this type of ontology development, software engineers are introducing a new generation of tools. However, we know relatively little about how existing large-scale collaborative ontology development works and what user workflows the tools must support. In this paper, we analyze our experience in supporting several such projects. We describe a visual and interactive project-management tool that we have developed, which helps ontology developers explore historical ontology change and discussion data. We present the results of qualitative and quantitative studies of the collaborative activity associated with three large-scale ontology-development projects. Based on the analysis, we conclude that domain and ontology experts have different patterns of ontology editing behavior, which has important implications for ontology-development tools.

This paper deals with information discovery and aggregation in large scale wireless sensor networks applied for mission-critical applications like military reconnaissance. To support query processing based on the gathered information, an efficient and reliable information discovery mechanism is proposed for sensor networks. We extend the basic Comb-Needle Discovery Support Model [3] by including Cluster-based data aggregation mechanism, which helps minimize the communication cost. Clusterbased approach groups the sensor nodes in the sensor network. Each node of a group will send information to its Cluster Head, which then aggregates and forwards the information to the base station (Sink). We compare the performance of proposed Cluster-based Comb-Needle Model with the basic Comb-Needle Model using the parameters, namely, energy consumption and communication cost. The experimental results through Avrora Simulator on TinyOS Platform reveal that Clusterbased Comb-Needle Model for Data Aggregation helps a sensor network in conserving its energy, and thereby extending its life time.

To address the two most critical issues in P2P file-sharing systems: efficient information discovery and authentic data acquisition, we propose a Gnutella-like file-sharing protocol termed Adaptive Gnutella Protocol (AGP) that not only improves the querying efficiency in a P2P network but also enhances the quality of search results at the same time. The reputation scheme in the proposed AGP evaluates the credibility of peers based on their contributions to P2P services and subsequently clusters nodes together according to their reputation and shared content, essentially transforming the P2P overlay network into a topology with collaborative and reputed nodes as its core. By detecting malicious peers as well as free-riders and eventually pushing them to the edge of the overlay network, our AGP propagates search queries mainly within the core of the topology, accelerating the information discovery process. Furthermore, the clustering of nodes based on authentic and similar content in our AGP also improves the quality of search results. We have implemented the AGP with the PeerSim simulation engine and conducted thorough experiments on diverse network topologies and various mixtures of honest/dishonest nodes to demonstrate improvements in topology transformation, query efficiency, and search quality by our AGP.

Data augmentation plays a critical role in improving model performance across various domains, but it becomes challenging with graph data due to their complex and irregular structure. To address this issue, we propose EPIC (Edit Path Interpolation via learnable Cost), a novel interpolation-based method for augmenting graph datasets. To interpolate between two graphs lying in an irregular domain, EPIC leverages the concept of graph edit distance, constructing an edit path that represents the transformation process between two graphs via edit operations. Moreover, our method introduces a context-sensitive cost model that accounts for the importance of specific edit operations formulated through a learning framework. This allows for a more nuanced transformation process, where the edit distance is not merely count-based but reflects meaningful graph attributes. With randomly sampled graphs from the edit path, we enrich the training set to enhance the generalization capability of classification models. Experimental evaluations across several benchmark datasets demonstrate that our approach outperforms existing augmentation techniques in many tasks.

Biotechnology market expected to reach $414.5 bn globally in 2017

Modern applications are often split into separate client and server tiers that communicate via message passing over the network. One well-understood threat to privacy for such applications is the leakage of sensitive user information either in transit or at the server. In response, an array of defensive techniques have been developed to identify or block unintended or malicious information leakage. However, prior work has primarily considered privacy leaks originating at the client directed at the server, while leakage in the reverse direction -- from the server to the client -- is comparatively under-studied. The question of whether and to what degree this leakage constitutes a threat remains an open question. We answer this question in the affirmative with Hush, a technique for semi-automatically identifying Server-based InFormation OvershariNg (SIFON) vulnerabilities in multi-tier applications. In particular, the technique detects SIFON vulnerabilities using a heuristic that overshared sensitive information from server-side APIs will not be displayed by the application's user interface. The technique first performs a scalable static program analysis to screen applications for potential vulnerabilities, and then attempts to confirm these candidates as true vulnerabilities with a partially-automated dynamic analysis. Our evaluation over a large corpus of Android applications demonstrates the effectiveness of the technique by discovering several previously-unknown SIFON vulnerabilities in eight applications.

The automatic User Interface (UI) composition of UI components thanks to UI composition operators (composers) is one of the approaches to improve the UI development, to produce adaptive UI and to master the development of large scale UI. In this setting, the verification concern of UI component composition is still a challenge. This work addresses the composability of components and their services depending on proposed UI composition operators defined according to standard abstraction levels. The main contributions of this paper are a component model to carry on a containing UI composition, the identification of the main aspects of the UI composition, the proposition of some new UI composers including semantic and temporal aspects and the checking of composability properties of composers to verify the correctness of the UI composition.

The UMIST Abstract Data Store is a software tool which supports abstract data types together with flexible mechanisms for specifying, for each abstract data type, alternative user interface and memory representations appropriate to different physical media. These mechanisms facilitate the definition of types, the specification of their alternative representations and the creation and manipulation of their values in a persistent fashion. The media supported may include such things as disks and visual displays and collections of these connected together via a network. This paper focuses on the mechanisms which have evolved in this environment for specifying safe user interfaces to complex data structures.

Digital video is becoming more ubiquitous. Unfortunately, editing videos remains difficult for several reasons. It has dual tracks of audio and video and may require working at the smallest level of detail. Silver is an authoring tool that uses video metadata to overcome these problems. It provides mulitiple views with different content types and at different levels of abstraction. This paper focuses on Silver's smart selection and editing operations, which work at a high semantic level and handle different boundaries in audio and video. Our research suggests several ways in which video editing tools can assist users in the composition and reuse of video.

Mobile computing devices and their applications that encompass context aware components are becoming increasingly more prevalent. The context-awareness of these types of applications typically focuses on the services offered. In this paper we describe a framework that supports the monitoring and analysis of mobile application usage patterns with the goal of updating user models for adaptive services and user interface personalisation. This paper focuses on two aspects of the framework. The first is the modelling and storage of the usage data. The second focuses on the data mining component of the framework, outlining the five different capabilities of the adaptation in addition to the algorithms used. The proposed framework has been evaluated through specific case studies, with the results attained demonstrating the effectiveness of the data mining capabilities and in particular the adaptation of the User Interface. The accuracy and efficiency of the algorithms used are also evaluated with three users. The results of the evaluation show that the aims of the data mining component were achieved with the personalisation and adaptation of content and user interface, respectively.

Experimental examinations of the 2D-thermal-hydraulic code FRECON for single phase natural convection flows in complex structures

Large scale and very large scale systems are considered in computer sciences, economics, ecology, biology, engineering, cybernetics and many other areas. Interconnection of their elements, sels or subsystems and complexity of their topological structure is not less important than their quantative characteristics or parameters. In this paper we give brief review of topological methods of pattern analysis of n-dimensional digital images in finite spaces, which was presented on two previous SMS conferences and in some other publications, New properties of digital fundamental group, which are important for their calculation, are investigated. Digital fundamental group of product spaces is calculated through fundamental group of multipliers. Some other higher dimensional invariants of finite digital spaces are considered and their application to pattern analysis is investigated. These methods of algebraic topological shape analysis uniquely characterize digital patterns with precision to homotopy equivalence. It shows that pattern analysis has to be conducted on interdisciplinary basis.

This paper describes a sketch user interface enhanced by feedback for 3D CAD model retrieval. Users can express their intent by sketching 2D shape in the way as engineers draw three views of 3D models. It not only supports users’ free form sketches, but also accepts users’ further editing operations and feedbacks. The interaction paradigm proposed in this paper is supported by a 3D shape matching method, in which three problems are solved: determination of projecting planes and directions, 2D view generation, and similarity measuring between views. In addition, experiments are conducted to evaluate the performance of this sketch user interface by 3D model retrieval.

Commutative Replicated Data Types (CRDTs) are an emerging tool for real-time collaborative editing. Existing work on CRDTs mostly focuses on documents as a list of text content, but large documents (having over 7,000 pages) with complex sectional structure need higher-level organization. We introduce the Causal Graph, which extends the Causal Tree CRDT into a graph of nodes and transitions to represent ordered trees. This data structure is useful in driving document outlines for large collaborative documents, resolving structures with over 100,000 sections in less than a second.