Today's dominant design for the Internet of Things (IoT) is a Cloud-based system, where devices transfer their data to a back-end and in return receive instructions on how to act. This view is challenged when delays caused by communication with the back-end become an obstacle for IoT applications with, for example, stringent timing constraints. In contrast, Fog Computing approaches, where devices communicate and orchestrate their operations collectively and closer to the origin of data, lack adequate tools for programming secure interactions between humans and their proximate devices at the network edge. This paper fills the gap by applying Action-Oriented Programming (AcOP) model for this task. While originally the AcOP model was proposed for Cloud-based infrastructures, presently it is re-designed around the notion of coalescence and disintegration, which enable the devices to collectively and autonomously execute their operations in the Fog by serving humans in a peer-to-peer fashion. The Cloud's role has been minimized—it is being leveraged as a development and deployment platform.

This paper proposes, for the first time without using any linearization or order reduction, an adaptive and model-based discharge pressure control design for the variable displacement axial piston pumps (VDAPPs), whose dynamical behaviors are highly nonlinear and can be described by a fourth-order differential equation. The rigorous stability proof, with an asymptotic convergence, is given for the entire system. In the proposed novel controller design method, the specifically designed stabilizing terms constitute an essential core to cancel out all the stability-preventing terms. The experimental results reveal that rapid parameter adaptation significantly improves the feedback signal tracking precision compared to a known-parameter controller design. In the comparative experiments, the adaptive controller design demonstrates the state-of-the-art discharge pressure control performance, enabling a possibility for energy consumption reductions in hydraulic systems driven with VDAPP.

Designing applications for scalability is key to improving their performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with data dependencies, memory management, data motion, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) improves programmer productivity when implementing hybrid workflows for multi-core and multi-GPU systems. The Hybrid Task Graph Scheduler (HTGS) is an abstract execution model, framework, and API that increases programmer productivity when implementing hybrid workflows for such systems. HTGS manages dependencies between tasks, represents CPU and GPU memories independently, overlaps computations with disk I/O and memory transfers, keeps multiple GPUs occupied, and uses all available compute resources. Through these abstractions, data motion and memory are explicit; this makes data locality decisions more accessible. To demonstrate the HTGS application program interface (API), we present implementations of two example algorithms: (1) a matrix multiplication that shows how easily task graphs can be used; and (2) a hybrid implementation of microscopy image stitching that reduces code size by ≈ 43% compared to a manually coded hybrid workflow implementation and showcases the minimal overhead of task graphs in HTGS. Both of the HTGS-based implementations show good performance. In image stitching the HTGS implementation achieves similar performance to the hybrid workflow implementation. Matrix multiplication with HTGS achieves 1.3x and 1.8x speedup over the multi-threaded OpenBLAS library for 16k × 16k and 32k × 32k size matrices, respectively.

ALMARVI is a collaborative European research project funded by Artemis involving 16 industrial as well as academic partners across 4 countries, working together to address various computational challenges in image and video processing in 3 application domains: healthcare, surveillance and mobile. This paper is an editorial for a special issue discussing the integrated system created by the partners to serve as a cross-domain solution for the project. The paper also introduces the partner articles published in this special issue to discuss the various technological developments achieved within ALMARVI spanning all system layers, from hardware to applications. We illustrate the challenges faced within the project based on use cases from the three targeted application domains, and how these can address the 4 main project objectives addressing 4 challenges faced by high performance image and video processing systems: massive data rate, low power consumption, composability and robustness. We present a system stack composed of algorithms, design frameworks and platforms as a solution to these challenges. Finally, the use cases from the three different application domains are mapped on the system stack solution and are evaluated based on their performance for each of the 4 ALMARVI objectives.

In recent years, there has been an increasing adoption of various Learning Management Systems (LMS) in higher education in Sub-Saharan countries. Despite the perceived benefits of these systems to leverage challenges facing education sector in the region, studies show that the majority of them tend to fail; partially or totally. This paper presents a model for evaluating LMS deployed in Higher Education Institutions in Sub-Saharan countries through adopting and extending the updated DeLone and McLean information system success model. The proposed model and the instrument have been validated through a survey of 200 students enrolled in various courses offered via Moodle LMS at University of Dar es Salaam, Tanzania. The findings of this study will help those who are involved in the implementation of LMS in higher education in Sub-Saharan countries to evaluate their existing systems and/or to prepare corrective measures and strategies to avoid future LMS failures.

This paper presents an analysis of an efficient parallel implementation of the active-set Newton algorithm (ASNA), which is used to estimate the nonnegative weights of linear combinations of the atoms in a large-scale dictionary to approximate an observation vector by minimizing the Kullback–Leibler divergence between the observation vector and the approximation. The performance of ASNA has been proved in previous works against other state-of-the-art methods. The implementations analysed in this paper have been developed in C, using parallel programming techniques to obtain a better performance in multicore architectures than the original MATLAB implementation. Also a hardware analysis is performed to check the influence of CPU frequency and number of CPU cores in the different implementations proposed. The new implementations allow ASNA algorithm to tackle real-time problems due to the execution time reduction obtained.

Dynamic simulation of distance to the physical surface could promote the development of new inexpensive tools for blind and visually impaired users. The StickGrip is a haptic device comprised of the Wacom pen input device added with a motorized penholder. The goal of the research presented in this paper was to assess the accuracy and usefulness of the new pen-based interaction technique when the position and displacement of the penholder in relation to the pen tip provided haptic feedback to the user about the distance to the physical or virtual surface of interaction. The aim was to examine how accurately people are able (1) to align the randomly deformed virtual surfaces to the flat surface and (2) to adjust the number of surface samples having a randomly assigned curvature to the template having the given curvature and kept fixed. These questions were approached by measuring both the values of the adjusted parameters and the parameters of the human performance, such as a ratio between inspection time and control time spent by the participants to complete the matching task with the use of the StickGrip device. The test of the pen-based interaction technique was conducted in the absence of visual feedback when the subject could rely on the proprioception and kinesthetic sense. The results are expected to be useful for alternative visualization and interaction with complex topographic and mathematical surfaces, artwork, and modeling.

A diversity of wireless technologies will collaborate to support the fifth-generation (5G) communication networks with their demanding applications and services. Despite decisive progress in many enabling solutions, next-generation cellular deployments may still suffer from a glaring lack of bandwidth due to inefficient utilization of radio spectrum, which calls for immediate action. To this end, several capable frameworks have recently emerged to all help the mobile network operators (MNOs) leverage the abundant frequency bands that are utilized lightly by other incumbents. Along these lines, the recent Licensed Shared Access (LSA) regulatory framework allows for controlled sharing of spectrum between an incumbent and a licensee, such as the MNO, which coexist geographically. This powerful concept has been subject to several early technology demonstrations that confirm its implementation feasibility. However, the full potential of LSA-based spectrum management can only become available if it is empowered to operate dynamically and at high space-Time-frequency granularity. Complementing the prior efforts, we in this work outline the functionality that is required by the LSA system to achieve the much needed flexible operation as well as report on the results of our respective live trial that employs a full-fledged commercial-grade cellular network deployment. Our practical results become instrumental to facilitate more dynamic bandwidth sharing and thus promise to advance on the degrees of spectrum utilization in future 5G systems without compromising the service quality of their users.

The Liquid Software metaphor refers to software that can operate seamlessly across multiple devices owned by one or multiple users. Liquid Software applications can take advantage of the computing, storage and communication resources available on all the devices owned by the user. Liquid Software applications can also dynamically migrate from one device to another, following the user’s attention and usage context. The key design goal in Liquid Software development is to minimize the additional efforts arising from multiple device ownership (e.g., installation, synchronization and general maintenance of personal computers, smartphones, tablets, home and car displays, and wearable devices), while keeping the users in full control of their devices, applications and data. In this paper we present the design space for Liquid Software, categorizing and discussing the most important architectural dimensions and technical choices. We also provide an introduction and comparison of two frameworks implementing Liquid Software capabilities in the context of the World Wide Web.

Background. Architectural smells and code smells are symptoms of bad code or design that can cause different quality problems, such as faults, technical debt, or difficulties with maintenance and evolution. Some studies show that code smells and architectural smells often appear together in the same file. The correlation between code smells and architectural smells, however, is not clear yet; some studies on a limited set of projects have claimed that architectural smells can be derived from code smells, while other studies claim the opposite. Objective. The goal of this work is to understand whether architectural smells are independent from code smells or can be derived from a code smell or from one category of them. Method. We conducted a case study analyzing the correlations among 19 code smells, six categories of code smells, and four architectural smells. Results. The results show that architectural smells are correlated with code smells only in a very low number of occurrences and therefore cannot be derived from code smells. Conclusion. Architectural smells are independent from code smells, and therefore deserve special attention by researchers, who should investigate their actual harmfulness, and practitioners, who should consider whether and when to remove them.

Large-scale perturbation databases, such as ConnectivityMap (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

RVC-CAL is an actor-based dataflow language that enables concurrent, modular and portable description of signal processing algorithms. RVC-CAL programs can be compiled to implementation languages such as C/C++ and VHDL for producing software or hardware implementations. This paper presents a methodology for automatic discovery of piecewise-deterministic (quasi-static) execution schedules for RVC-CAL program software implementations. Quasi-static scheduling moves computational burden from the implementable run-time system to design-time compilation and thus enables making signal processing systems more efficient. The presented methodology divides the RVC-CAL program into segments and hierarchically detects quasi-static behavior from each segment: first at the level of actors and later at the level of the whole segment. Finally, a code generator creates a quasi-statically scheduled version of the program. The impact of segment based quasi-static scheduling is demonstrated by applying the methodology to several RVC-CAL programs that execute up to 58 % faster after applying the presented methodology.

We analyze barriers to task-based information access in molecular medicine, focusing on research tasks, which provide task performance sessions of varying complexity. Molecular medicine is a relevant domain because it offers thousands of digital resources as the information environment. Data were collected through shadowing of real work tasks. Thirty work task sessions were analyzed and barriers in these identified. The barriers were classified by their character (conceptual, syntactic, and technological) and by their context of appearance (work task, system integration, or system). Also, work task sessions were grouped into three complexity classes and the frequency of barriers of varying types across task complexity levels were analyzed. Our findings indicate that although most of the barriers are on system level, there is a quantum of barriers in integration and work task contexts. These barriers might be overcome through attention to the integrated use of multiple systems at least for the most frequent uses. This can be done by means of standardization and harmonization of the data and by taking the requirements of the work tasks into account in system design and development, because information access is seldom an end itself, but rather serves to reach the goals of work tasks.

This paper provides an analytic performance evaluation of the bit error rate (BER) of underlay decode-and-forward cognitive networks with best relay selection over Rayleigh multipath fading channels. A generalized BER expression valid for arbitrary operational parameters is firstly presented in the form of a single integral, which is then employed for determining the diversity order and coding gain for different best relay selection scenarios. Furthermore, a novel and highly accurate closed-form approximate BER expression is derived for the specific case where relays are located relatively close to each other. The presented results are rather convenient to handle both analytically and numerically, while they are shown to be in good agreement with results from respective computer simulations. In addition, it is shown that as in the case of conventional relaying networks, the behaviour of underlay relaying cognitive networks with best relay selection depends significantly on the number of involved relays.

This paper considers a scenario when we have multiple pre-trained detectors for detecting an event and a small dataset for training a combined detection system. We build the combined detector as a Boolean function of thresholded detector scores and implement it as a binary classification cascade. The cascade structure is computationally efficient by providing the possibility to early termination. For the proposed Boolean combination function, the computational load of classification is reduced whenever the function becomes determinate before all the component detectors have been utilized. We also propose an algorithm, which selects all the needed thresholds for the component detectors within the proposed Boolean combination. We present results on two audio-visual datasets, which prove the efficiency of the proposed combination framework. We achieve state-of-the-art accuracy with substantially reduced computation time in laughter detection task, and our algorithm finds better thresholds for the component detectors within the Boolean combination than the other algorithms found in the literature.

Context: Global software development (GSD), although now a norm in the software industry, carries with it enormous challenges mostly regarding communication and coordination. Aforementioned challenges are highlighted when there is a need to transfer knowledge between sites, particularly when software artifacts assigned to different sites depend on each other. The design of the software architecture and associated task dependencies play a major role in reducing some of these challenges. Objective: The current literature does not provide a cohesive picture of how the distributed nature of software development is taken into account during the design phase: what to avoid, and what works in practice. The objective of this paper is to gain an understanding of software architecting in the context of GSD, in order to develop a framework of challenges and solutions that can be applied in both research and practice. Method: We conducted a systematic literature review (SLR) that synthesises (i) challenges which GSD imposes on software architecture design, and (ii) recommended practices to alleviate these challenges. Results: We produced a comprehensive set of guidelines for performing software architecture design in GSD based on 55 selected studies. Our framework comprises nine key challenges with 28 related concerns, and nine recommended practices, with 22 related concerns for software architecture design in GSD. These challenges and practices were mapped to a thematic conceptual model with the following concepts: Organization (Structure and Resources), Ways of Working (Architecture Knowledge Management, Change Management and Quality Management), Design Practices, Modularity and Task Allocation. Conclusion: The synthesis of findings resulted in a thematic conceptual model of the problem area, a mapping of the key challenges to practices, and a concern framework providing concrete questions to aid the design process in a distributed setting. This is a first step in creating more concrete architecture design practices and guidelines.

The unprecedented proliferation of smart devices together with novel communication, computing, and control technologies have paved the way for A-IoT. This development involves new categories of capable devices, such as high-end wearables, smart vehicles, and consumer drones aiming to enable efficient and collaborative utilization within the smart city paradigm. While massive deployments of these objects may enrich people's lives, unauthorized access to said equipment is potentially dangerous. Hence, highly secure human authentication mechanisms have to be designed. At the same time, human beings desire comfortable interaction with the devices they own on a daily basis, thus demanding authentication procedures to be seamless and user-friendly, mindful of contemporary urban dynamics. In response to these unique challenges, this work advocates for the adoption of multi-factor authentication for A-IoT, such that multiple heterogeneous methods - both well established and emerging - are combined intelligently to grant or deny access reliably. We thus discuss the pros and cons of various solutions as well as introduce tools to combine the authentication factors, with an emphasis on challenging smart city environments. We finally outline the open questions to shape future research efforts in this emerging field.

Background: Molecular descriptors have been extensively used in the field of structure-oriented drug design and structural chemistry. They have been applied in QSPR and QSAR models to predict ADME-Tox properties, which specify essential features for drugs. Molecular descriptors capture chemical and structural information, but investigating their interpretation and meaning remains very challenging.Results: This paper introduces a large-scale database of molecular descriptors called COMMODE containing more than 25 million compounds originated from PubChem. About 2500 DRAGON-descriptors have been calculated for all compounds and integrated into this database, which is accessible through a web interface at http://commode.i-med.ac.at.

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer’s disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

At present, cellular coverage in many rural areas remains intermittent. Mobile operators may not be willing to deploy expensive network infrastructure to support low-demand regions. For that reason, solutions for the rapid deployment of base stations in areas with insufficient or damaged operator infrastructure are emerging. Utilization of unmanned aerial vehicles (UAVs) or drones serving as data relays holds significant promise for delivering on-demand connectivity as well as providing public safety services or aiding in recovery after communication infrastructure failures caused by natural disasters. The use of UAVs in provisioning high-rate radio connectivity and bringing it to remote locations is also envisioned as a potential application for fifth-generation (5G) communication systems. In this study, we introduce a prototype solution for an aerial base station, where connectivity between a drone and a base station is provided via a directional microwave link. Our prototype is equipped with a steering mechanism driven by a dedicated algorithm to support such connectivity. Our experimental results demonstrate early-stage connectivity and signal strength measurements that were gathered with our prototype. Our results are also compared against the free-space model. These findings support the emerging vision of aerial base stations as part of the 5G ecosystem and beyond.

This publication addresses two bottlenecks in the construction of minimal coverability sets of Petri nets: the detection of situations where the marking of a place can be converted to ω, and the manipulation of the set A of maximal ω-markings that have been found so far. For the former, a technique is presented that consumes very little time in addition to what maintaining A consumes. It is based on Tarjan's algorithm for detecting maximal strongly connected components of a directed graph. For the latter, a data structure is introduced that resembles BDDs and Covering Sharing Trees, but has additional heuristics designed for the present use. Results from a few experiments are shown. They demonstrate significant savings in running time and varying savings in memory consumption compared to an earlier state-of-the-art technique.

Full use of the parallel computation capabilities of present and expected CPUs and GPUs requires use of vector extensions. Yet many actors in data flow systems for digital signal processing have internal state (or, equivalently, an edge that loops from the actor back to itself) that impose serial dependencies between actor invocations that make vectorizing across actor invocations impossible. Ideally, issues of inter-thread coordination required by serial data dependencies should be handled by code written by parallel programming experts that is separate from code specifying signal processing operations. The purpose of this paper is to present one approach for so doing in the case of actors that maintain state. We propose a methodology for using the parallel scan (also known as prefix sum) pattern to create algorithms for multiple simultaneous invocations of such an actor that results in vectorizable code. Two examples of applying this methodology are given: (1) infinite impulse response filters and (2) finite state machines. The correctness and performance of the resulting IIR filters and one class of FSMs are studied.

Dataflow programming has received increasing attention in the age of multicore and heterogeneous computing. Modular and concurrent dataflow program descriptions enable highly automated approaches for design space exploration, optimization and deployment of applications. A great advance in dataflow programming has been the recent introduction of the RVC-CAL language. Having been standardized by the ISO, the RVC-CAL dataflow language provides a solid basis for the development of tools, design methodologies and design flows. This paper proposes a novel design flow for mapping RVC-CAL dataflow programs to parallel and heterogeneous execution platforms. Through the proposed design flow the programmer can describe an application in the RVC-CAL language and map it to multi- and many-core platforms, as well as GPUs, for efficient execution. The functionality and efficiency of the proposed approach is demonstrated by a parallel implementation of a video processing application and a run-time reconfigurable filter for telecommunications. Experiments are performed on GPU and multicore platforms with up to 16 cores, and the results show that for high-performance applications the proposed design flow provides up to 4 × higher throughput than the state-of-the-art approach in multicore execution of RVC-CAL programs.

Context: DevOps is considered important in the ability to frequently and reliably update a system in operational state. DevOps presumes cross-functional collaboration and automation between software development and operations. DevOps adoption and implementation in companies is non-trivial due to required changes in technical, organisational and cultural aspects. Objectives: This exploratory study presents detailed descriptions of how DevOps is implemented in practice. The context of our empirical investigation is web application and service development in small and medium sized companies. Method: A multiple-case study was conducted in five different development contexts with successful DevOps implementations since its benefits, such as quick releases and minimum deployment errors, were achieved. Data was mainly collected through interviews with 26 practitioners and observations made at the companies. Data was analysed by first coding each case individually using a set of predefined themes and thereafter perform a cross-case synthesis. Results: Our analysis yielded some of the following results: (i) software development team attaining ownership and responsibility to deploy software changes in production is crucial in DevOps. (ii) toolchain usage and support in deployment pipeline activities accelerates the delivery of software changes, bug fixes and handling of production incidents. (ii) the delivery speed to production is affected by context factors, such as manual approvals by the product owner (iii) steep learning curve for new skills is experienced by both software developers and operations staff, who also have to cope with working under pressure. Conclusion: Our findings contributes to the overall understanding of DevOps concept, practices and its perceived impacts, particularly in small and medium sized companies. We discuss two practical implications of the results.

This article presents results on how students became engaged and motivated when using digital storytelling in knowledge creation in Finland, Greece and California. The theoretical framework is based on sociocultural theories. Learning is seen as a result of dialogical interactions between people, substances and artefacts. This approach has been used in the creation of the Global Sharing Pedagogy (GSP) model for the empirical study of student levels of engagement in learning twenty-first century skills. This model presents a set of conceptual mediators for student-driven knowledge creation, collaboration, networking and digital literacy. Data from 319 students were collected using follow-up questionnaires after the digital storytelling project. Descriptive statistical methods, correlations, analysis of variance and regression analysis were used. The mediators of the GSP model strongly predicted student motivation and enthusiasm as well as their learning outcomes. The digital storytelling project, using the technological platform Mobile Video Experience (MoViE), was very successful in teaching twenty-first century skills.

Increasingly, researchers have come to acknowledge that consumption activities entail both utilitarian and hedonic components. Whereas utilitarian consumption accentuates the achievement of predetermined outcomes typical of cognitive consumer behavior, its hedonic counterpart relates to affective consumer behavior in dealing with the emotive and multisensory aspects of the shopping experience. Consequently, while utilitarian consumption activities appeal to the rationality of customers in inducing their intellectual buy-in of the shopping experience, customers’ corresponding emotional buy-in can only be attained through the presence of hedonic consumption activities. The same can be said for online shopping. Because the online shopping environment is characterized by the existence of an IT-enabled web interface that acts as the focal point of contact between customers and vendors, its design should embed utilitarian and hedonic elements to create a holistic shopping experience. Building on Expectation Disconfirmation Theory (EDT), this study advances a research model that not only delineates between customers’ utilitarian and hedonic expectations for online shopping but also highlights how these expectations can be best served through functional and esthetic performance, respectively. Furthermore, we introduce online shopping experience (i.e., transactional frequency) as a moderator affecting not only how customers form utilitarian and hedonic expectations but also how they evaluate the functional and esthetic performances of e-commerce sites. The model is then empirically validated via an online survey questionnaire administered on a sample of 303 respondents. Theoretical contributions and pragmatic implications to be gleaned from our research model and its subsequent empirical validation are discussed.

Since the birth of computer and networks, fuelled by pervasive computing, Internet of Things and ubiquitous connectivity, the amount of data stored and transmitted has exponentially grown through the years. Due to this demand, new storage solutions are needed. One promising media is the DNA as it provides numerous advantages, which includes the ability to store dense information while achieving long-term reliability. However, the question as to how the data can be retrieved from a DNA-based archive, still remains. In this paper, we aim to address this question by proposing a new storage solution that relies on bacterial nanonetworks properties. Our solution allows digitally-encoded DNA to be stored into motility-restricted bacteria, which compose an archival architecture of clusters, and to be later retrieved by engineered motile bacteria, whenever reading operations are needed. We conducted extensive simulations, in order to determine the reliability of data retrieval from motility-restricted storage clusters, placed spatially at different locations. Aiming to assess the feasibility of our solution, we have also conducted wet lab experiments that show how bacteria nanonetworks can effectively retrieve a simple message, such as "Hello World", by conjugation with motility-restricted bacteria, and finally mobilize towards a target point for delivery.

The interpretive grounded theory (GT) study analyses information system (IS) enabled organizational change in two private sector organizations. These two organizations, who are long term partners, were developing a new IS product to divergent markets. The data was gathered through 15 interviews, conducted at the phase of initial rollouts. The findings focus on the results of the theoretical coding phase in which selective codes, referred to as change management activities, are related to each other. As a theoretical contribution, the dynamic structure presents how the change management activities appear differently, depending on a set of choices. Several paradoxical situations stemmed from inconsistencies and/or tensions, because the choices did not support the targeted change management activities. The study thus proposes that there is an increasing demand to analyze the sources of paradoxical situations. Paradoxical situations in these five opposing forces were identified: long term vs. short term, macro vs. micro, past vs. future, centralized vs. distributed, and control vs. trust/self-organization. Some paradoxical situations arose because of the nature of the trust-based IS partnership, while others were socially constructed as a result of unintended consequences of actions in relation to the strategic goals. Managerial efforts are increasingly required for identifying paradoxical situations at an early stage and for considering the right balance for the opposing forces in the dynamic IS change process.

Owing to a steadily increasing demand for efficient spectrum utilization as part of the fifth-generation (5G) cellular concept, it becomes crucial to revise the existing radio spectrum management techniques and provide more flexible solutions for the corresponding challenges. A new wave of spectrum policy reforms can thus be envisaged by producing a paradigm shift from static to dynamic orchestration of shared resources. The emerging Licensed Shared Access (LSA) regulatory framework enables flexible spectrum sharing between a limited number of users that access the same frequency bands, while guaranteeing better interference mitigation. In this work, an advanced user satisfaction-aware spectrum management strategy for dynamic LSA management in 5G networks is proposed to balance both the connected user satisfaction and the Mobile Network Operator (MNO) resource utilization. The approach is based on the MNO decision policy that combines both pricing and rejection rules in the implemented processes. Our study offers a classification built over several types of users, different corresponding attributes, and a number of MNO's decision scenarios. Our investigations are built on Criteria-Based Resource Management (CBRM) framework, which has been specifically designed to facilitate dynamic LSA management in 5G mobile networks. To verify the proposed model, the results (spectrum utilization, estimated Secondary User price for the future connection, and user selection methodology in case of user rejection process) are validated numerically as we yield important conclusions on the applicability of our approach, which may offer valuable guidelines for efficient radio spectrum management in highly dynamic and heterogeneous 5G environments.

Local binary pattern (LBP) is a texture operator that is used in several different computer vision applications requiring, in many cases, real-time operation in multiple computing platforms. The irruption of new video standards has increased the typical resolutions and frame rates, which need considerable computational performance. Since LBP is essentially a pixel operator that scales with image size, typical straightforward implementations are usually insufficient to meet these requirements. To identify the solutions that maximize the performance of the real-time LBP extraction, we compare a series of different implementations in terms of computational performance and energy efficiency, while analyzing the different optimizations that can be made to reach real-time performance on multiple platforms and their different available computing resources. Our contribution addresses the extensive survey of LBP implementations in different platforms that can be found in the literature. To provide for a more complete evaluation, we have implemented the LBP algorithms in several platforms, such as graphics processing units, mobile processors and a hybrid programming model image coprocessor. We have extended the evaluation of some of the solutions that can be found in previous work. In addition, we publish the source code of our implementations.

This paper highlights the performance of single path multiple access (SPMA) and discusses the performance comparison between higher order sectorization and SPMA in a macrocellular environment. The target of this paper is to emphasize the gains and significance of the novel concept of SPMA in achieving better and homogeneous SIR and enhanced system capacity in a macrocellular environment. This paper also explains the algorithm of SIR computation in SPMA. The results presented in this paper are based on sophisticated 3D ray tracing simulations performed with real world 3D building data and site locations from Seoul, South Korea. Macrocellular environment dominated with indoor users was considered for the research purpose of this paper. It is found that by increasing the order of sectorization, SIR along with spectral efficiency degrades due to the increase in inter-cell interference. However, as a result of better area spectral efficiency due to increased number of sectors (cells), the higher order sectorization offers more system capacity compared to the traditional 3-sector site. Furthermore, SPMA shows an outstanding performance and significantly improves the SIR for the individual user over the whole coverage area, and also remarkably increases the system capacity. In the environment under consideration, the simulation results reveal that SPMA can offer approximately 424 times more system capacity compared to the reference case of 3-sector site.

Dataflow descriptions have been used in a wide range of Digital Signal Processing (DSP) applications, such as multi-media processing, and wireless communications. Among various forms of dataflow modeling, Synchronous Dataflow (SDF) is geared towards static scheduling of computational modules, which improves system performance and predictability. However, many DSP applications do not fully conform to the restrictions of SDF modeling. More general dataflow models, such as CAL (Eker and Janneck 2003), have been developed to describe dynamically-structured DSP applications. Such generalized models can express dynamically changing functionality, but lose the powerful static scheduling capabilities provided by SDF. This paper focuses on the detection of SDF-like regions in dynamic dataflow descriptions-in particular, in the generalized specification framework of CAL. This is an important step for applying static scheduling techniques within a dynamic dataflow framework. Our techniques combine the advantages of different dataflow languages and tools, including CAL (Eker and Janneck 2003), DIF (Hsu et al. 2005) and CAL2C (Roquier et al. 2008). In addition to detecting SDF-like regions, we apply existing SDF scheduling techniques to exploit the static properties of these regions within enclosing dynamic dataflow models. Furthermore, we propose an optimized approach for mapping SDF-like regions onto parallel processing platforms such as multi-core processors.

Partial order methods alleviate state explosion by considering only a subset of actions in each constructed state. The choice of the subset depends on the properties that the method promises to preserve. Many methods have been developed ranging from deadlock-preserving to CTL(Formula presented.)-preserving and divergence-sensitive branching bisimilarity preserving. The less the method preserves, the smaller state spaces it constructs. Fair testing equivalence unifies deadlocks with livelocks that cannot be exited and ignores the other livelocks. It is the weakest congruence that preserves whether or not the system may enter a livelock that it cannot leave. We prove that a method that was designed for trace equivalence also preserves fair testing equivalence. We demonstrate its effectiveness on a protocol with a connection and data transfer phase. This is the first practical partial order method that deals with a practical fairness assumption.

As it has evolved, the Internet has had to support a broadening range of networking technologies, business models and user interaction modes. Researchers and industry practitioners have realised that this trend necessitates a fundamental rethinking of approaches to network and service management. This has spurred significant research efforts towards developing autonomic network management solutions incorporating distributed self-management processes inspired by biological systems. Whilst significant advances have been made, most solutions focus on management of single network domains and the optimisation of specific management or control processes therein. In this paper we argue that a networking infrastructure providing a myriad of loosely coupled services must inherently support federation of network domains and facilitate coordination of the operation of various management processes for mutual benefit. To this end, we outline a framework for federated management that facilitates the coordination of the behaviour of bio-inspired management processes. Using a case study relating to distribution of IPTV content, we describe how Federal Relationship Managers realising our layered model of management federations can communicate to manage service provision across multiple application/storage/ network providers. We outline an illustrative example in which storage providers are dynamically added to a federation to accommodate demand spikes, with appropriate content being migrated to those providers servers under control of a bio-inspired replication process.

We present a structural data set of the 20 proteinogenic amino acids and their amino-methylated and acetylated (capped) dipeptides. Different protonation states of the backbone (uncharged and zwitterionic) were considered for the amino acids as well as varied side chain protonation states. Furthermore, we studied amino acids and dipeptides in complex with divalent cations (Ca²⁺, Ba²⁺, Sr²⁺, Cd²⁺, Pb²⁺, and Hg²⁺). The database covers the conformational hierarchies of 280 systems in a wide relative energy range of up to 4 eV (390 kJ/mol), summing up to a total of 45,892 stationary points on the respective potential-energy surfaces. All systems were calculated on equal first-principles footing, applying density-functional theory in the generalized gradient approximation corrected for long-range van der Waals interactions. We show good agreement to available experimental data for gas-phase ion affinities. Our curated data can be utilized, for example, for a wide comparison across chemical space of the building blocks of life, for the parametrization of protein force fields, and for the calculation of reference spectra for biophysical applications.