Deterioration of software quality developed by multiple organizations has become a serious problem. To predict software degradation after an organizational change, this paper investigates the influence of quality deterioration on software metrics by analyzing three software projects. To detect factors indicating a low evolvability, we focus on the relationships between the change in software metric values and refactoring tendencies. Refactoring after an organization change impacts the quality.

Knowledge management is important for government agencies in improving service delivery to their customers and data inter-operation within and across organizations. Building organizational knowledge repository for government agency has unique challenges. In this paper, we propose that enterprise ontology can provide support for government agencies in capturing organizational taxonomy, best practices and global data schema. A case study of a large-scale adoption for the Thailand's Excise Department is elaborated. A modular design approach of the enterprise ontology for the excise tax domain is discussed. Two forms of organizational knowledge: global schema and standard practices were captured in form of ontology and rule-based knowledge. The organizational knowledge was deployed to support two KM systems: excise recommender service and linked open data. Finally, we discuss some lessons learned in adopting the framework in the government agency.

Multi-hypothesis prediction technique, which exploits inter-frame correlation efficiently, is widely used in block-based distributed compressive video sensing. To solve the problem of inaccurate prediction in multi-hypothesis prediction technique at a low sampling rate and enhance the reconstruction quality of non-key frames, we present a resample-based hybrid multi-hypothesis scheme for block-based distributed compressive video sensing. The innovations in this paper include: (1) multi-hypothesis reconstruction based on measurements reorganization (MR-MH) which integrates side information into the original measurements; (2) hybrid multi-hypothesis (H-MH) reconstruction which mixes multiple multi-hypothesis reconstructions adaptively by resampling each reconstruction. Experimental results show that the proposed scheme outperforms the state-of-the-art technique at the same low sampling rate.

The increase in network access devices and demand for high quality of service (QoS) by the users have led to insufficient capacity for the network operators. Moreover, the existing control equipment and mechanisms are not flexible and agile enough for the dynamically changing environment of heterogeneous cellular networks (HetNets). This non-agile control plane is hard to scale with ever increasing traffic demand and has become the performance bottleneck. Furthermore, the new HetNet architecture requires tight coordination and cooperation for the densely deployed small cell base stations, particularly for interference mitigation and dynamic frequency reuse and sharing. These issues further complicate the existing control plane and can cause serious inefficiencies in terms of users' quality of experience and network performance. This article presents an SDN control framework for energy efficient downlink/uplink scheduling in HetNets. The framework decouples the control plane from data plane by means of a logically centralized controller with distributed agents implemented in separate entities of the network (users and base stations). The scheduling problem consists of three sub-problems: (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. Moreover, these sub-problems are coupled and must be solved simultaneously. We formulate the DL/UL scheduling in HetNet as an optimization problem and use the Markov approximation framework to propose a distributed economical algorithm. Then, we divide the algorithm into three sub-routines for (i) user association, (ii) power control, (iii) resource allocation and (iv) interference mitigation. These sub-routines are then implemented on different agents of the SDN framework. We run extensive simulation to validate our proposal and finally, present the performance analysis.

Person re-identification is a challenging problem of matching observations of individuals across non-overlapping camera views. When pedestrians walk across disjoint camera views, continuous motion information is lost, and thus re-identification mainly relies on appearance matching. Person re-identification is actually a special case of near duplicate search in image retrieval. Given a probe, our task is to find the image containing the same person in galleries. At present many state-of-the-art methods in image retrieval are based on the Bag-of-Words (BOW) model. By adapting the BOW model to our task, Bag-of-Ensemble-Colors (BOEC) is proposed to tackle person re-identification in this paper. We combine low-level color histogram and semantic color names to represent human appearances. Meanwhile, some mature and efficient techniques in image retrieval are employed in the model containing soft quantization, burstiness punishing strategy, and negative evidence. In consideration apriori knowledge of human body structure, efficient spatial constraints are proposed to weaken the influence of background. Extensive experiments on VIPeR and ETHZ databases are performed to test the effectiveness of our approach, and promising results are obtained in the public databases. Compared with other unsupervised methods, we obtain state-of-the-art performances. The recognition rate is 32.23% on VIPeR dataset, 87% on ETHZ SEQ.#1, 83% on ETHZ SEQ.#2, and 91% on ETHZ SEQ.#3.

This article introduces a self-organizing model which builds the topology of a DHT mapping system for ICN. Due to its self-organizing operation and low average degree of maintenance, the management overhead of the system is reduced dramatically, which yields inherent scalability. The proposed model can improve latency by around 10% compared to an existing approach which has a near optimal average distance when the number of nodes and degree are given. In particular, its operation is simple which eases maintenance concerns. Moreover, we analyze the model theoretically to provide a deeper understanding of the proposal.

Collaborative business has been increasingly developing with the environment of globalization and advanced information technologies. In a collaboration environment with multiple organizations, participants from different organizations always have different views about modeling the overall business process due to different knowledge and cultural backgrounds. Moreover, flexible support, privacy preservation and process reuse are important issues that should be considered in business process management across organizational boundaries. This paper presents a novel approach of modeling interorganizational business process for collaboration. Our approach allows for modeling loosely coupled interorganizational business process considering different views of organizations. In the proposed model, organizations have their own local process views of modeling business process instead of sharing pre-defined global processes. During process cooperation, local process of an organization can be invisible to other organizations. Further, we propose the coordination mechanisms for different local process views to detect incompatibilities among organizations. We illustrate our proposed approach by a case study of interorganizational software development collaboration.

Software engineering education at universities plays an increasingly important role as software quality is becoming essential in realizing a safe and dependable society. This paper proposes a practical state transition model (Practical-STM) based on the Organizational Expectancy Model for the improvement of software process education based on the Personal Software Process (PSP) from a motivation point of view. The Practical-STM treats an individual trainee of the PSP course as a state machine, and formalizes a motivation process of a trainee using a set of states represented by factors regarding motivation and a set of operations carried out by course instructors. The state transition function of this model represents the features or characteristics of a trainee in terms of motivation. The model allows a formal description of the states of a trainee in terms of motivation and the educational actions of the instructors in the PSP course. The instructors are able to decide effective and efficient actions to take toward the trainees objectively by presuming a state and a state transition function of the trainees formally. Typical patterns of state transitions from an initial state to a final state, which is called a scenario, are useful for inferring possible transitions of a trainee and taking proactive operations from a motivation point of view. Therefore, the model is useful not only for improving the educational effect of the PSP course, but also for the standardization of the course management and the quality management of the instructors.

Mobility load balancing (MLB) is a key technology for self-organization networks (SONs). In this paper, we explore the mobility load balancing problem and propose a unified cell specific offset adjusting algorithm (UCSOA) which more accurately adjusts the largely uneven load between neighboring cells and is easily implemented in practice with low computing complexity and signal overhead. Moreover, we evaluate the UCSOA algorithm in two different traffic conditions and prove that the UCSOA algorithm can get the lower call blocking rates and handover failure rates. Furthermore, the interdependency of the proposed UCSOA algorithm's performance and that of the inter-cell interference coordination (ICIC) algorithm is explored. A self-organization soft frequency reuse scheme is proposed. It demonstrates UCSOA algorithm and ICIC algorithm can obtain a positive effect for each other and improve the network performance in LTE system.

In large-scale collaborative computing, users and resource providers organize various Virtual Organizations (VOs) to share resources and services. A VO organizes other sub-VOs for the purpose of achieving the VO goal, which forms hierarchical VO environments. VO participants agree upon a certain policies, such as resource sharing amount or user accesses. In this letter, we provide an optimal resource sharing mechanism in hierarchical VO environments under resource sharing agreements. The proposed algorithm enhances resource utilization and reduces mean response time of each user.

In this paper, an artificial sub-threshold oscillating spiking neuron is presented and its response phenomena to an input spike-train are analyzed. In addition, a dynamic parameter update rule of the neuron for achieving synchronizations to the input spike-train having various spike frequencies is presented. Using an analytical two-dimensional return map, local stability of the parameter update rule is analyzed. Furthermore, a pulse-coupled network of the neurons is presented and its basic self-organizing function is analyzed. Fundamental comparisons are also presented.

This paper examines the structural relationships between Information Technology (IT) governance and Enterprise Architecture (EA), with the objective of enhancing business value in the enterprise society. Structural models consisting of four related hypotheses reveal the relationship between IT governance and EA in the improvement of business values. We statistically examined the hypotheses by analyzing validated questionnaire items from respondents within firms listed on the Japanese stock exchange who were qualified to answer them. We concluded that firms which have organizational ability controlled by IT governance are more likely to deliver business value based on IT portfolio management.

Topological structure of peer-to-peer (P2P) networks affects their operating performance. Thus, various models have been proposed to construct an efficient topology for the P2P networks. However, due to the simultaneous failures of peers and other disastrous events, it is difficult to maintain the originally designed topological structure that provides the network with some performance benefits. For this reason, in this paper we propose a simple local rewiring method that changes the network topology to have small diameter as well as highly clustered structure. Moreover, the presented evaluation study shows how these topological properties are involved with the performance of P2P networks.

A new generation network is requested to accommodate an enormous number of heterogeneous nodes and a wide variety of traffic and applications. To achieve higher scalability, adaptability, and robustness than ever before, in this paper we present new network architecture composed of self-organizing entities. The architecture consists of the physical network layer, service overlay network layer, and common network layer mediating them. All network entities, i.e. nodes and networks, behave in a self-organizing manner, where the global behavior emerges through their operation on local information and direct and/or indirect mutual interaction. The center of the architecture is so-called self-organization engines, which implement nonlinear self-organizing dynamics originating in biology, physics, and mathematics. In this paper, we also show some examples of self-organization engines.

The self-organized microwrinkles can serve as a surface alignment layer to align nematic liquid crystals, which is primarily based on the groove mechanism. The azimuthal anchoring energy is discussed and estimated from the groove topography and the actual twist angle in the twisted nematic cell.

Applications usually have their own phases in heap memory usage. The traditional garbage collector fails to match various application phases because the same heuristic on the object behavior is used throughout the entire execution. This paper introduces a phase-adaptive garbage collector which reorganizes the heap layout and adjusts the invocation time of the garbage collection according to the phases. The proposed collector identifies phases by detecting the application methods strongly related to the phase boundaries. The experimental results show that the proposed phase-adaptive collector successfully recognizes application phases and improves the garbage collection time by as much as 41%.

The encoding/decoding process of JPEG2000 requires much more computation power than that of conventional JPEG mainly due to the complexity of the entropy encoding/decoding. Thus usually multiple entropy codec hardware modules are implemented in parallel to process the entropy encoding/decoding. This module, however, requests many small-size memories to store intermediate data, and when multiple modules are implemented on a chip, employment of the large number of SRAMs increases difficulty of whole chip layout. In this paper, an efficient memory organization framework for the entropy encoding/decoding module is proposed, in which not only existing memory organizations but also our proposed novel memory organization methods are attempted to expand the design space to be explored. As a result, the efficient memory organization for a target process technology can be explored.

A novel adaptive reputation-based virtual organization formation is proposed. It restrains the bad performers effectively based on the consideration of the global experience of the evaluator and evaluates the direct trust relation between two grid nodes accurately by consulting the previous trust value rationally. It also consults and improves the reputation evaluation process in PathTrust model by taking account of the inter-organizational trust relationship and combines it with direct and recommended trust in a weighted way, which makes the algorithm more robust against collusion attacks. Additionally, the proposed algorithm considers the perspective of the VO creator and takes required VO services as one of the most important fine-grained evaluation criterion, which makes the algorithm more suitable for constructing VOs in grid environments that include autonomous organizations. Simulation results show that our algorithm restrains the bad performers and resists against fake transaction attacks and badmouth attacks effectively. It provides a clear advantage in the design of a VO infrastructure.

Superconductivity and superconducting electronics have quite a prominent place in the European research environment and can look back onto a successful history. In recent years the European Framework programs helped to enhance the interaction between the different national research institutions, universities and industry. For applications of superconductivity this was accomplished by the European Network of Excellence SCENET and its sister organization ESAS. In this context a virtual European foundry network was established (Fluxonics), which forms a platform for the superconducting electronics activities in Europe and realizes support for the design and the fabrication of superconducting circuits for research laboratories and industry. Lately quite some development on the digital side and the cooling of superconducting electronics devices has taken place in Europe; most of it within the Fluxonics network. Some of these advances will be reported in this overview article.

This paper presents a middleware system for multi-agents on a distributed system as a general test-bed for bio-inspired approaches. The middleware is unique to other approaches, including distributed object systems, because it can maintain and migrate a dynamic federation of multiple agents on different computers. It enables each agent to explicitly define its own deployment policy as a relocation between the agent and another agent. This paper describes a prototype implementation of the middleware built on a Java-based mobile agent system and its practical applications that illustrates the utility and effectiveness of the approach in real distributed systems.