Miracle-dz/UniFAR-FTUs huggingface dataset:
Open Innovation and Organizational Capacities: Case Study of an SME

INTRODUCTION The concept of " open innovation " has laid the groundwork for new perspectives on business innovation , starting first from case studies of major U.S. companies (Chesbrough, 2003). Chesbrough (2003), who first developed the concept, has highlighted how some companies like IBM, Xerox and Intel have used the knowledge and expertise of other companies to (re)define their business model and reposition themselves in the market, thus becoming more competitive and profitable. Until very recently, open innovation was essentially described in terms of technological in-ABSTRACT Open innovation has gained popularity in recent years. But is the concept new or does it express old realities? The literature review of this case study found that the term " open innovation " is recent and that its development has been facilitated by technological innovations. The case study collected data over a period of eight months from an agrifood SME in Quebec, Canada. The aim was to go beyond the basic model of open innovation and integrate dynamic, absorptive and appropriative capacities into a new working open innovation model. Initial results show that components associated with the basic open innovation model such as intellectual property, joint R&D and co-product development are present as well as certain organizational capacities. The difficulty of applying the concept is due to its interactions rather than its components taken individually. The study concludes that further work is needed to extend the applicability and the foundations of open innovation. Van de Vrande et al., 2009) have shown how some organizational concepts, especially in terms of dynamic, absorp-tive and appropriative capacities, can be usefully integrated into the concept of open innovation, providing a more solid foundation for the concept and facilitating its application. This expansion of the concept seems warranted, especially as these capacities had been largely overlooked until then (Chesbrough et al., 2006). In light of the foregoing, the author has chosen to make a case study of a small company with fewer than 10 workers. The aim of the study was to describe the process of open innovation and validate the presence of organizational capacities. The research goal is, therefore, to empirically validate a model of open innovation which incorporates organizational capacities. author has worked out on various aspects of this new model with the owners of the SME over a period of eight months. The chapter discusses about the frame of reference, the research method that has …
Simulated Annealing and Tabu Search based Hybrid Algorithm for Multi Depot Vehicle Routing Problem with Time Windows and Split Delivery

Effective co-ordination of distribution operations of a manufacturing organization is paramount since it represents the major cost component of the logistic supply chain. In order to deal with high demand of orders with shorter lead time, distributed warehouse concept is introduced and this is currently being practiced. Furthermore, due to inherent deficiencies in VRP, rules have been extended to accommodate large scale orders by splitting the delivery. Artificial Intelligences (AI) based approaches widely used in the literature to solve VRP problems with extensions. In this research, hybrid algorithm (SATS) is developed based on Simulated Annealing (SA) and Tabu Search (TS) techniques to improve the solution quality of the complex Multi Depot Vehicle Routing Problem with Time Windows and Split Delivery (MDVRPTWSD). The simulation results reveal that SATS outperform in solution quality and the computational time.
Analysis of a Step-Based Watershed Algorithm Using CUDA

This paper proposes and develops a parallel algorithm for the watershed transform, with application on graphics hardware. The existing proposals are discussed and its aspects briefly analysed. The algorithm is proposed as a procedure of four steps, where each step performs a task using different approaches inspired by existing techniques. The algorithm is implemented using the CUDA libraries and its performance is measured on the GPU and compared to a sequential algorithm running on the CPU, achieving an average speed of twice the execution time of the sequential approach. This work improves on previous results of hybrid approaches and parallel algorithms with many steps of synchronisation and iterations between CPU and GPU.
WEIGHTLESS REGULARISED IDENTIFICATION USING MULTI-OBJECTIVE OPTIMISATION METHOD

Although the regularisation increased the popularity of inverse analysis due to its capability of deriving a stable solution, the significant problem is that the solution depends upon the regularisation parameters chosen. This paper presents a technique for deriving solutions without the use of the parameters, and further an optimisation method, which can work efficiently for problems of concern. Numerical examples show that the technique can efficiently search for appropriate solutions.
Tracking Using Superpixel Features

While great success has been demonstrated in numerous tracking algorithm, some challenging problems still remain such as motion, shape deformation and occlusion. In this paper, the proposed algorithm can work robustly to overcome the occlusion and fast movement in real -- world scenarios. A discriminative model based on the Gaussian superpixel model is constructed to descript the change of the target and the background. The calculation of the superpixel's weight uses the special information and a penalize factor. Furthermore, the tracking result is selected from the candidates generated under the framework of particle filter. The candidate with the highest score would be set to the tracking result. Besides, the update strategy which updates according to the trend of candidate's score is adaptive in order to suit for the change of the target. The experimental results demonstrate that the proposed algorithm performs more stable compared with several state-of-the-art algorithms when dealing with occlusion and fast movement.
Global Inference and Learning Algorithms for Multi-Lingual Dependency Parsing

This paper gives an overview of the work of McDonald et al. (McDonald et al. 2005a, 2005b; McDonald and Pereira 2006;McDonald et al. 2006) on global inference and learning algorithms for data-driven dependency parsing. Further details can be found in the thesis of McDonald (McDonald 2006). This paper is primarily intended for the audience of the ESSLLI 2007 course on data-driven dependency parsing.
A structured argumentation framework for detaching conditional obligations

We present a general formal argumentation system for dealing with the detachment of conditional obligations. Given a set of facts, constraints, and conditional obligations, we answer the question whether an unconditional obligation is detachable by considering reasons for and against its detachment. For the evaluation of arguments in favor of detaching obligations we use a Dung-style argumentation-theoretical semantics. We illustrate the modularity of the general framework by considering some extensions, and we compare the framework to some related approaches from the literature.
An event-driven multi-threading architecture for embedded systems

In this paper we present an event driven multi-threading architecture and its underlying event flow system model of computation as a framework for the implementation of complex reactive and communication systems. Existing process oriented specification languages can be used to specify the system and embedded in the model. The target architecture covers a wide variety of architectures, varying from small FSMs to large processors, which are interconnected by a network template which performs dynamic scheduling and communication for different levels of process granularity and timing. Interconnect and module implementation and optimisation is based on an event flow graph model (EFG). In this paper we present our system model and the architectural template and show how they can be applied to an industrial application example.
Existence for calculus of variations and optimal control problems on time scales

In this paper we prove the existence for optimal control problems with terminal constraints on time scales. A definition of the solution of semi-linear control systems involving Sobolev space W 1,2 T is proposed and new existence and uniqueness results of this kind of dynamic systems on time scales are presented under a weaker assumption. According to LT strong-weak lower semi-continuity of integral functionals, we establish the existence of optimal controls. In particular, the existence for calculus of variations on time scales is derived.
Improved energy-efficiency in cloud datacenters with interference-aware virtual machine placement

Virtualization is one of the main technologies used for improving resource efficiency in datacenters; it allows the deployment of co-existing computing environments over the same hardware infrastructure. However, the co-existing of environments — along with management inefficiencies — often creates scenarios of high-competition for resources between running workloads, leading to performance degradation. This phenomenon is known as Performance Interference, and introduces a non-negligible overhead that affects both a datacenter's Quality of Service and its energy-efficiency. This paper introduces a novel approach to workload allocation that improves energy-efficiency in Cloud datacenters by taking into account their workload heterogeneity. We analyze the impact of performance interference on energy-efficiency using workload characteristics identified from a real Cloud environment, and develop a model that implements various decision-making techniques intelligently to select the best workload host according to its internal interference level. Our experimental results show reductions in interference by 27.5% and increased energy-efficiency up to 15% in contrast to current mechanisms for workload allocation.
The Role of IL-6 for Predicting Neonatal Sepsis: A Systematic Review and Meta-Analysis

Objective Neonatal sepsis (NS) is a common and life-threatening disorder in infants. Previous studies showed that interleukin-6 (IL-6) may be a valid non-invasive and rapid method for diagnosis of NS. We conducted this review to assess the validity of IL-6 for predicting NS. Methods This was a systematic review with meta-analysis. Embase, Medline and Web of Science databases were searched between January 1990 and December 2009. The search terms used were “cytokine”, “neonate”, “sepsis” and “interleukin-6". We used standard methods recommended for meta analyses of diagnostic test evaluations. The analysis was based on a summary ROC (SROC) curve. Meta-regression analysis was used to assess the effects of some confounding factors on the results of meta-analysis. Potential presence of publication bias was tested using funnel plots and the Egger test. Findings Meta-analysis was performed on 13 publications including 353 infants with sepsis and 691 control infants. The pooled sensitivity and specificity of IL-6 was 0.79 and 0.84, respectively. The maximum joint sensitivity and specificity (i.e., the Q value) in SROC curve was 0.82 and the area under curve (AUC) was 0.89 (95% CI: 0.84-0.94). Meta-regression analysis showed that the diagnostic accuracy of IL-6 was not affected by confounding variables. The evaluation of publication bias showed that the Egger test was not significant (P=0.07). Conclusion IL-6 seems to be a valid marker for predicting NS. It may be considered for early diagnosis of sepsis in neonatal care units.
Multi-stage localization given topological map for autonomous robots

Vision-based place recognition is of a particular importance for autonomous systems that aim to navigate intelligently in a human-inhabited environment. Given a topological map of an indoor environment, the autonomous system shall localize itself invariantly with different illumination and imaging conditions. To address these challenges, we propose to use global-local feature extraction and classification in multiple stages. Scale Invariant Feature Transform (SIFT) is used as a local feature detector and descriptor which has been proven to be a robust local invariant feature descriptor. Fourier Transform, Hue Saturation Value (HSV), and Hough Transform are used as global features. The Support Vector Machines (SVM) is used to localize the system by classifying the global features. However the K-nearest neighbors matching technique (K-NN) is used to support SVM's classification in ambiguous decisions by classifying the local features.
Web service for designing and implementing formal languages

An intelligent Web service and its interface for designing and implementing formal languages are described in the paper. The paper describes also background tools LISA and GIE used in the Web service and some basics of their usage. Three language design approaches are presented. Firstly, from scratch using LISA specifications, secondly using stored specifications from other developers, and thirdly with a set of positive and negative example strings/programs. Advanced user interface with shared and documented repository of solutions make Web service suitable for beginners as well as compiler experts. Web service has been designed in a way which makes it suitable for integration in standalone applications
Local and global knowledge to improve the quality of sensed data

Wireless sensor networks (WSNs) allow for wireless communications between embedded devices that can be deployed for long periods in harsh environments. Because of their flexibility, WSNs are applicable to a wide variety of domains and a lot of research has been done to determine the best topology for a network, or the best routing protocol. Much of this research is aimed to solve a specific problem and can be difficult to translate to other scenarios. There is already substantial research on the the various routing protocols that have been developed for WSNs. [3] and [2] survey routing protocols highlighting the constraints of deploying a WSN, such as battery life, transmission medium or coverage, and how each protocol addresses changes in the topology of a network as well as aiming to be as energy efficient as possible. In this paper, we explore the higher level architecture of a sensor network and propose the Knowledge Based Hierarchical Architecture for Sensing (K-HAS), an architecture designed to utilise the knowledge related to its environment in order to classify sensed data. We define sensed data as data that originates from a node that is related to what that node has been tasked to sense. There has been research into sensor networks that use context-awareness in order to improve the quality of the sensed data, as well as the lifetime of the network. In [25], sensors have been used to monitor the movements of patients and adapt their power usage based on the behaviour of the patient. K-HAS aims to extend context-awareness in order to use the knowledge of its environment to classify the sensed data. We call a sensor’s knowledge of its environment local knowledge, Local and Global Knowledge to Improve the Quality of Sensed Data
Structured Prediction, Dual Extragradient and Bregman Projections

We present a simple and scalable algorithm for maximum-margin estimation of structured output models, including an important class of Markov networks and combinatorial models. We formulate the estimation problem as a convex-concave saddle-point problem that allows us to use simple projection methods based on the dual extragradient algorithm (Nesterov, 2003). The projection step can be solved using dynamic programming or combinatorial algorithms for min-cost convex flow, depending on the structure of the problem. We show that this approach provides a memory-efficient alternative to formulations based on reductions to a quadratic program (QP). We analyze the convergence of the method and present experiments on two very different structured prediction tasks: 3D image segmentation and word alignment, illustrating the favorable scaling properties of our algorithm.
Frequency-domain mapping approach of stability bounds for loop shaping of bilateral controllers

Bilateral control architectures include multiple control elements. In general, the relation between a single control element and the stability of the entire system is non-linear. Therefore, stability is standard evaluated a posteriori, rendering the control design process to be complex and highly iterative. A priori understanding of stability constraints would simplify the design of control elements and, as performance is fundamentally limited by stability, could provide specific guidelines whether and how performance of the bilateral teleoperation system can be optimized. This paper presents a numerical visualization method that enables stability-based control design using classical loopshaping techniques: Frequency-domain Mapping of Bilateral Stability (FMBS). Unlike current stability-based control design approaches, the FMBS method i) is not limited to a fixed control element, a fixed control architecture or system dynamics and ii) enables the implementation of all often used stability criteria. The advantages of the FMBS method are theoretically validated through the use of two test cases, extracted from literature. Using the FMBS method, it is shown that control elements can be redesigned to achieve superior performance.
TIER 2 Capital and Bank Behaviour

The consensus among financial regulators is that Tier 2 capital is inferior to Tier 1, both in terms of incentives for an ongoing bank and regarding the protection it offers in the case of failure. Some economists, while not denying the benefits of Tier 1, suggest that there may be benefits of market discipline to subordinated debt, which is part of Tier 2. Given the regulatory community is undertaking radical changes to current regulation, it is a paradox that virtually no empirical work has looked at the actual effects of Tier 2 on bank performance. This paper seeks to fill this gap, and finds a higher Tier 2 proportion to be consistent with poorer risk management.
A swarm intelligence based distributed localization technique for wireless sensor network

Wireless sensor network (WSN) refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. Sensor Localization is a fundamental challenge in WSN. In this paper localization is modeled as a multi dimensional optimization problem. A comparison study of energy of processing and transmission in a wireless node is done, main inference made is that transmission process consumes more than processing. An energy efficient distributed localization technique is proposed. Distributive localization is addressed using swarm techniques Particle Swarm Optimization (PSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) because of their quick convergence to quality solutions. The performances of both algorithms are studied. The accuracy of both algorithms is analyzed using parameters such as number of nodes localized, computational time and localization error. A simulation was conducted for 100 target nodes and 20 beacon nodes, the results show that the PSO based localization is faster and CLPSO is more accurate.
Automated Tagging of Software Projects Using Bytecode and Dependencies (N)

Several open and closed source repositories group software systems and libraries to allow members of particular organizations or the open source community to take advantage of them. However, to make this possible, it is necessary to have effective ways of searching and browsing the repositories. Software tagging is the process of assigning terms (i.e., tags or labels) to software assets in order to describe features and internal details, making the task of understanding software easier and potentially browsing and searching through a repository more effective. We present Sally, an automatic software tagging approach that is able to produce meaningful tags for Maven-based software projects by analyzing their bytecode and dependency relations without any special requirements from developers. We compared tags generated by Sally to the ones in two widely used online repositories, and the tags generated by a state-of-the-art categorization approach. The results suggest that Sally is able to generate expressive tags without relying on machine learning-based models.
Matched filter based timing synchronization for orthogonal frequency division multiplexing systems

A robust matched filter based timing synchronization scheme for orthogonal frequency division multiplexing (OFDM) systems is presented to achieve improved performance. Training symbol based timing synchronization often exhibits large estimation variance, resulting in performance degradation. The proposed method performs training symbol based timing synchronization at the receiver using matched filter, resulting in remarkable performance improvement in dispersive channels as compared to other methods. The same training symbol is used for channel estimation leading to channel equalization for further improvement in performance. Computer simulations are used to evaluate bit error rate performance for different channels, highlighting the improvement obtained through the proposed method.
Submeter based Training of Multi-class Support Vector Machines for Appliance Recognition in Home Electricity Consumption Data

In this paper we employ smart meter and support vector machines (SVM) for the problem of recognizing household appliances’ load patterns in measured load time series, which is an important step for various applications in energy consulting, process recognition or health care applications. We present an automated data collection and preprocessing approach that intrinsically avoids many privacy (and security) issues by keeping the whole process local to the household. In the experimental part we investigate multi-class SVMs in the problem domain of automatically recognizing appliances in load profiles of smart meters. For the learning phase, we use low intrusive submeters to automatically and locally generate household specific test data for the supervised training and validation of the SVMs. We analyze classifiers w.r. t. various training sets and feature spaces. Comparing data from household simulator and real household data, we find that excellent recognition rates can be achieved even with low resolution data and rather unsophisticated feature space.
Holographic mesons in various dimensions

Following the analysis of [1], we calculate the spectrum of fluctuations of a probe Dk-brane in the background of N Dp-branes, for k = p,p+2,p+4 and p<5. The result corresponds to the mesonic spectrum of a (p+1)-dimensional super-Yang-Mills (SYM) theory coupled to `dynamical quarks', i.e. fields in the fundamental representation — the latter are confined to a defect for k = p and p+2. We find a universal behaviour where the spectrum is discrete and the mesons are deeply bound. The mass gap and spectrum are set by the scale M ~ mq/geff(mq), where mq is the mass of the fundamental fields and geff(mq) is the effective coupling evaluated at the quark mass, i.e. geff2(mq) = gYM2N mqp−3. We consider the evolution of the meson spectra into the far infrared of three-dimensional SYM, where the gravity dual lifts to M-theory. We also argue that the mass scale appearing in the meson spectra is dictated by holography.