Somjit Arch-int

Association rule hiding in risk management for retail supply chain collaboration

Association rule hiding is an efficient solution that helps enterprises avoid the risk caused by sensitive knowledge leakage when sharing data in their collaborations. This study examines how data sharing has the potential to create risk for enterprises in retail supply chain collaboration and proposes a new algorithm to remove sensitive knowledge from the released database based on the intersection lattice of frequent itemsets. The proposed algorithm specifies the victim item such that the modification of this item causes the least impact on frequent itemsets and the non-sensitive association rule. In the experiment described in this paper, this algorithm is used in risk avoidance for a retailer sharing data in retail supply chain collaboration. The results indicate that our approach is applicable in a real context and outperforms previous mechanisms.

Business process analysis and simulation for the RFID and EPCglobal Network enabled supply chain: A proof-of-concept approach

RFID technology and the EPCglobal Network Standard have attracted growing interest from a wide range of industries and research academics. This paper proposes a significant approach to adopting RFID system into a simple supply chain using simulation technique. The simple supply chain and the business process are stated. The suggested open source solutions with the supporting components compliant with the EPCglobal Network Standard are used to emulate the system. The supply chain process remodeling and EPC information flow are evaluated after the simulation runs follow the designed scenarios. Due to the limitations on the real-world implementation, the simulation is useful for supply chain firms and research academics to study and illustrate the new remodeled process and to understand the practical implications of the technology without high investment and long time implementation.

Semantic Ontology Mapping for Interoperability of Learning Resource Systems using a rule-based reasoning approach

Abstract There is an increasing demand for sharing learning resources between existing learning resource systems to support reusability, exchangeability, and adaptability. The learning resources need to be annotated with ontologies into learning objects that use different metadata standards. These ontologies have introduced the problems of semantic and structural heterogeneity. This research proposes a Semantic Ontology Mapping for Interoperability of Learning Resource Systems. To enable semantic ontology mapping, this research proposes conflict detection and resolution techniques for both semantic and structural conflicts. The Semantic Bridge Ontology has been proposed as a core component for generating mapping rules to reconcile terms defined in local ontologies into terms defined in the target common ontology. This work defines the reasoning rules to classify related learning objects to enhance the powerful deductive reasoning capabilities of the system. As a consequence, ontology-based learning object metadata are generated and used by the semantic query engine to facilitate user queries of learning objects across heterogeneous learning resource systems.

An adaptive multi bit-plane image steganography using block data-hiding

Embedding a secret message into the pixels of a cover image yields a visual distortion if these pixels belong to smooth regions. Thus, this prompted the development of some edge-based approaches in which only the edge pixels are used to hide secret bits. As a result, the visual quality of stego images is improved. However, the capacity is limited due to some unused regions in cover images. In this paper, an adaptive multi bit-planes image steganography using block data-hiding (MPBDH) is proposed. This method employs more than one bit-plane and applies an adaptive complexity threshold computation to select the complex regions of a cover image used in data hiding. Consequently, the embedding capacity and security performance are significantly improved in comparison with previous approaches based on pixel and block complexity. The results, which are obtained from experiments performed on 10,000 natural gray-images, indicate that the embedding capacity and security introduced in the proposed approach overcome the problems of previous approaches. The proposed approach is hence suitable for secure communications.

Personalized Care Recommendation Approach for Diabetes patients Using Ontology and SWRL

Although there are many health organizations that publish documents of detection and self-care recommendation for diabetes patients, this information is too general and inconsistent with each patient’s conditions. This research proposed the personalized care recommendation approach for diabetes patients using ontology and SWRL. The objective is to develop the diabetes knowledge-based ontologies (DKOs) expressed in Web Ontology Language (OWL) for describing the patient profile, the general self-care practices for diabetes patients, complication, sign, symptom, and disorder of the diabetes patients. The DKOs are mapped and incorporated with rules created by using the Semantic Web Rule Language (SWRL) for inferring new knowledge or new relationships according to each patient condition. The semantic rules enable the semantic recommendation for personalized care of the diabetes patient corresponding to each patient’s condition.

A novel secure block data-hiding algorithm using cellular automata to enhance the performance of JPEG steganography

The computational complexity of matrix embedding (ME)-based steganography is high due to the use of the Hamming parity check matrix and matrix multiplication. Furthermore, with only one embedding change solution, ME cannot minimize embedding distortions when applied to joint photographic experts group (JPEG) steganography. In this paper, we propose a novel block data-hiding (BDH) algorithm to improve the performance of JPEG steganography. BDH allows a byte of data to be hidden in a binary block by modifying a maximum of two bits of the binary block must be changed. Moreover, BDH can be applied to the new channel selection rule for JPEG steganography with more available embedding solutions and less computational complexity than modified matrix embedding (MME). In addition, to increase the security of hidden data against extraction attacks, the secret message is encrypted by a cipher based on cellular automata (CA). The bitwise XOR operator is employed in the encryption process to maintain speed and security when a non-repeating key stream is used. The experimental results indicate that the proposed approach has a high anti-detection property against ensemble classifiers (universal steganalyzers), high security against recovery attacks, and good perceptual quality.

Ontology-based Metadata Integration Approach for Learning Resource Interoperability

Owing to the increasing demand of learning resource sharing, data integration is a promising approach to support system interoperability across heterogeneous e-Learning systems. One of the main problems to deal with in data heterogeneity is semantic conflict and although a variety of methodologies have been proposed which focus on name conflicts, structure conflicts have received little attention. This paper proposes an approach of ontology-based metadata integration for sharing learning resources between the heterogeneous Learning Management Systems. In order to achieve semantic interoperability, the standard metadata DC/LOM, WordNet and mapping rules are proposed to cope with not only name conflict but also structure conflict arising in the real-world system. The preliminary result provides the ability of learning resource interoperability between the different repositories that can establish a community-shared and reusable semantic pattern base over educational institutes.

An RFID-based traceability system

Traceability system in the food supply chain is becoming more necessary. RFID and EPCglobal Network Standards are emerging technologies that bring new opportunities to develop the high performance traceability system. This research proposes the analysis, design, and development of the RFID and EPCglobal Network Standards based traceability system that adheres to the requirements of global food traceability in terms of completeness of traceability information. The additional components, including lot management system and electronic transaction management system, encourage the traditional system in order to fulfill the missing information. The proposed system was developed and applied in a rice supply chain. Results from experimentation showed that the additional components can significantly improve the completeness of traceability information. The collaboration between EPCglobal Network Standards and electronic transaction management system can improve the performances in RFID operations.

Association Rule Hiding Based on Intersection Lattice

Association rule hiding has been playing a vital role in sensitive knowledge preservation when sharing data between enterprises. The aim of association rule hiding is to remove sensitive association rules from the released database such that side effects are reduced as low as possible. This research proposes an efficient algorithm for hiding a specified set of sensitive association rules based on intersection lattice of frequent itemsets. In this research, we begin by analyzing the theory of the intersection lattice of frequent itemsets and the applicability of this theory into association rule hiding problem. We then formulate two heuristics in order to (a) specify the victim items based on the characteristics of the intersection lattice of frequent itemsets and (b) identify transactions for data sanitization based on the weight of transactions. Next, we propose a new algorithm for hiding a specific set of sensitive association rules with minimum side effects and low complexity. Finally, experiments were carried out to clarify the efficiency of the proposed approach. Our results showed that the proposed algorithm, AARHIL, achieved minimum side effects and CPU-Time when compared to current similar state of the art approaches in the context of hiding a specified set of sensitive association rules.

Semantic Information Integration for Electronic Patient Records Using Ontology and Web Services Model

Electronic Patient Records (EPR) systems are developed proprietarily and often only serve one specific requirement within a healthcare institute. Heterogeneous EPR platforms introduce a problem in cross-system patient information exchange due to the lack of a uniform system and an accepted standard. This makes it very difficult for clinicians to capture the complete clinical history of a patient that may be spread out over a number of different healthcare institutes. This research proposes an architecture of semantic information integration for electronic patient records using ontology and Web Services models. The research exploits Web services to enable dynamic interoperability between different EPR systems. In order to enrich service discovery and solve semantic service discrepancies the concepts of service descriptions are semantically mapped with the Semantic Bridge Ontology (or SBO) expressed in OWL. Moreover, patient information obtained from each Web service is thus modeled onto domain ontology and integrated with the SBO to form the Ontology-based Patient Record Metadata. The proposed Ontology-based Patient Record Metadata provides a means for coping with the semantic service discrepancies and enables an inference engine to discover patient information dispersed over different healthcare systems.