Miracle-dz/UniFAR-FTUs huggingface dataset:
Boundaryless Cluster: Information and Communications Technology in Ireland

This paper examines the information and communications technology sector in the Republic of Ireland, focussing in particular on the growth of a regional software cluster in and around the city of Galway. Paradoxically, the withdrawal of Digital Equipment Corporation in the early 1990s provided the catalyst for Galway’s economic transformation, which was driven by a combination of foreign direct investment and the emergence of highly sophisticated local supply chains. The paper finds that the cluster dynamic had its source not only in the established skills and knowledge base but also in the network of institutions and initiatives that comprise Ireland’s national and regional innovation systems. While the cluster as a microcosm of recent Irish development exhibits dependency on foreign investment and technologies, its “boundaryless” character also provides unique opportunities for indigenous firms to access global markets.
Mapping the dynamics of social enterprises and ICTD in Cambodia

As Information and Communication Technology (ICT) and social enterprises become drivers of economic growth, the nexus provides opportunities for new models of business to bring benefits to communities in developing countries. Recognising the complex dynamics and range of actors in this diverse and emerging sector, this study chooses to document the external influences, use and impact of ICT on social enterprises (SEs) -enterprises that have both a business and social development goal. The problem investigated is the potential gag between the rhetoric of the promise of e-business versus the dynamics of enactment and impacts of ICT in practice in the social enterprise sector. The main contribution of the research is to identify the real development impact of ICT use by social enterprises by assessing the role of ICTs in achieving their goals. Its major finding is that SEs social and business missions are inextricably linked . and their use of ICTs and benefits from it can be used as a proxy to assess ICTD. Thus SEs provide a new paradigm in the study of ICTD.
Formalizing Domains, Ultrametric Spaces and Semantics of Programming Languages

We describe a Coq formalization of constructive ω-cpos, ultrametric spaces and ultrametric-enriched categories, up to and including the inverse-limit construction of solutions to mixed-variance recursive equations in both categories enriched over ω-cppos and categories enriched over ultrametric spaces. We show how these mathematical structures may be used in formalizing semantics for three representative programming languages. Specifically, we give operational and denotational semantics for both a simply-typed CBV language with recursion and an untyped CBV language, establishing soundness and adequacy results in each case, and then use a Kripke logical relation over a recursively-defined metric space of worlds to give an interpretation of types over a step-counting operational semantics for a language with recursive types and general references.
Research of inter-cluster multi-hop routing algorithm for wireless sensor networks

For wireless sensor networks in LEACH algorithm inter-cluster single-hop communication can not meet the requirements of network expansion of the problem, a multi-hop communication routing algorithm called CMRA is proposed. The algorithm uses the node communication energy consumption model derived network optimal cluster number per round under multi-hop communication; by analyzing the network total energy consumption determines the cost of choosing the next jump nodes and establishes a minimum energy path tree to the base station between cluster heads. The simulation results show that the algorithm can make up the lack of single-hop communication in LEACH algorithm, and prolong the network lifetime to a certain extent.
Achieving fairness in multicasting with almost stateless rate control

Several flow control algorithms have been proposed for attaining maxmin fair rates. All of these strategies use flow specific states at all nodes in the network, and are hence unsuitable for deployment in the current internet. We propose a new approach which attains maxmin fairness in unicast networks while maintaining per flow states at the edge nodes only. Next, we consider multirate, multicast network and argue that any flow control algorithm must use flow specific states at the forking points of the multicast tree. This happens because the packet forwarding rates must be different for different branches at the forking point, and thus the session specific forwarding rates must be stored at the forking point. Finally, we present a maxmin fair bandwidth allocation policy for multirate, multicast networks, which uses per flow states only at the forking nodes, and no per flow state at the intermediate nodes.
Low complexity linear MMSE equalization, channel decoding and estimation for frequency selective fast fading channels

The present work focuses on low complexity (LC) Linear Minimum Mean Square Error (LMMSE) schemes in conjunction with the existing paradigm of turbo equalization (TE). As a consequence, the LC LMMSE equalizer was extended to new scenarios with unknown frequency selective fast fading Rayleigh channels. In this paper, the generalized Valenti and Woerner estimator is coupled with a LMMSE and LC LMMSE equalizer and used to accurately estimate the channel. Extrinsic information transfer (EXIT) charts are computed for the resulting TE to predict the decoding and equalization convergence behaviour. In addition, a comparison between TE using LC LMMSE and LMMSE equalizer is performed using bit error rate (BER) performance simulations.
The Impact of Data Preprocessing on the Performance of a Naive Bayes Classifier

In the research of text mining, document classification is a growing field. Even though we have many existing classifying approaches, Naïve Bayes Classifier is simple and effective at classification. Data preprocessing is the important step in the data mining process. It prepares the raw data for the further process. The aim of this paper is to identify the impact of preprocessing the dataset on the performance of a Naïve Bayes Classifier. The Naïve Bayes Classifier is suggested as the most effective method to identify the spam emails. The Impact of preprocessing phase on the performance of the Naïve Bayes classifier is analyzed by comparing the output of both the preprocessed dataset result and non-preprocessed dataset result. The test results show that combining Naïve Bayes classification with the proper data preprocessing can improve the prediction accuracy.
Experimental demonstration of the compensation of nonlinear propagation in a LTE RoF system with a directly modulated laser

This paper reveals that nonlinear propagation is a critical problem for the long term evolution (LTE) technology based on the radio-over-fibre (RoF) system. Therefore, a direct modulation based frequency dithering (DMFD) method is proposed for nonlinear propagation compensation, namely the self phase modulation (SPM) and stimulated brillouin scattering (SBS). We found that DMFD method operates substantially different in RoF systems. The major difference is that DMFD signal frequency fd has to be much smaller than the RoF carrier frequency fRF; thus the condition of {fd <;<; fRF} has to be stringently obeyed. Analysis of the optical launch power (OLP) with DMFD method reveals that the SBS threshold is above ~6 dBm for LTE-RoF system. In addition, we also unfold that DMFD method does not induce an additional distortion for the linear and optimum OLP regions, which are frequency chirp dependent. Hence the proposed method improves the LTE-RoF system without any shortcoming. Finally, at OLP values of 8 dBm and 10 dBm, LTE-RoF system exhibits an average error vector magnitude (EVM) improvement of ~4.32% and ~6.18%, respectively, for the 50 km transmission span.
Description of an IPv6 Linux-based UTRAN testbed

Network modelling and traffic characterization are essential research activities towards the optimization of UMTS. We present here our IPv6 Linux-based UTRAN testbed which aims at emulating a real UTRAN segment. This emulation is relying on statistical traffic distributions, random walk UE mobility and line-of-sight power-based handover management. By interconnecting those different modules, we expose the first testbed results about the handover management and our views regarding the QoS management
Adjoint modeling for atmospheric pollution process sensitivity at regional scale

[1] During the summer 1998, a strong pollution event was documented over Paris as part of the Etude et Simulation de la Qualite de l'air en Ile-de-France (ESQUIF) project (second intensive observation period (IOP2)). From 7 to 9 August 1998 the pollution event changes from a well-marked ozone plume issued from Paris to a more general pollution over the whole Ile-de-France region. Using a three-dimensional chemistry-transport model and its adjoint part, the sensitivity of ozone, Ox, and NOx peaks to model parameters is investigated. For two locations, Paris and a suburban site, the influence of both meteorological and chemical model parameters on the simulated field concentrations is hourly quantified for each day. Processes leading to a urban polluted event are compared. It is shown that the pollutant concentrations are mainly driven by traffic and solvent surface emissions and meteorological parameters such as temperature. Since the adjoint approach is limited to infinitesimal model perturbation, some scenario simulations are carried out to evaluate the linearity of the impact of the most sensitive parameters within the uncertainty range. It is shown that the sensitivities determined from the adjoint approach can be extrapolated until their uncertainty ranges except for the wind speed.
Optimized Neural Network for Classification of Multispectral Images

The proposed work involves the multiobjective PSO based optimization of artificial neural network structure for the classification of multispectral satellite images. The neural network is used to classify each image pixel in various land cove types like vegetations, waterways, man-made structures and road network. It is per pixel supervised classification using spectral bands (original feature space). Use of neural network for classification requires selection of most discriminative spectral bands and determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) to determine discriminative spectral bands and the number of hidden layer node simultaneously. The result obtained using such optimized neural network is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of all classifiers is evaluated quantitatively using Xie-Beni and â indexes. The result shows the superiority of the proposed method.
Modeling Intrusion Detection System by Discovering Association Rule in Rough Set Theory Framework

In Intrusion Detection Systems, many intelligent information processing methods, data miming technology and so on have been applied to generating attack signatures automatically, updating signatures easily and improving detection accuracy with ultra data set. This paper presents an improved association rule discovering system under rough set theory framework of modeling IDSs. The system makes association rule applicable in classifying fields. The system exploits data reductions, rule selection, feature selection to improve detection accuracy and reduce false alarm and unreal alarm. Empirical results illustrate that the intrusion detection model can detect intrusion accurately.
Mesh Based Semantic Modelling for Indoor and Outdoor Scenes

Semantic reconstruction of a scene is important for a variety of applications such as 3D modelling, object recognition and autonomous robotic navigation. However, most object labelling methods work in the image domain and fail to capture the information present in 3D space. In this work we propose a principled way to generate object labelling in 3D. Our method builds a triangulated meshed representation of the scene from multiple depth estimates. We then define a CRF over this mesh, which is able to capture the consistency of geometric properties of the objects present in the scene. In this framework, we are able to generate object hypotheses by combining information from multiple sources: geometric properties (from the 3D mesh), and appearance properties (from images). We demonstrate the robustness of our framework in both indoor and outdoor scenes. For indoor scenes we created an augmented version of the NYU indoor scene dataset (RGBD images) with object labelled meshes for training and evaluation. For outdoor scenes, we created ground truth object labellings for the KITTY odometry dataset (stereo image sequence). We observe a significant speed-up in the inference stage by performing labelling on the mesh, and additionally achieve higher accuracies.
Perspectives of a semantic schema

This paper presents the notion of forming user perspectives over a semantic schema. Perspectives are a mechanism for providing user-oriented views of abstract objects. The perspectives of interest to each user group are collected into user views to provide a modularization tool for semantic schemas. A novel aspect of the approach to forming perspectives is that the semantics expressed within a schema are actively used to present design alternatives in the formation of perspectives and in the manner in which exception violations are handled. As a result, perspectives and their associated update operations are developed in a manner that conforms to the integrity constraints of the underlying semantic schema.
A novel and efficient method to initialize FPGA embedded memory content in asymptotically constant time

This paper describes analysis and implementation of a new method for maintaining valid content of FPGA memory blocks with an asymptotically constant time synchronous clear ability, that can be useful for (re)initialization to one default value. A particular application can be for high-speed real-time LZ77 [1] lossless compression algorithms, where a dictionary has to be (re)initialized before each run of the implemented compression algorithm. The method is based on two most widely used techniques for clearing the memory content: a linear passage of the memory and clearing each cell by writing a default value and creating a register field providing an (in)valid bit for each memory cell. Our solution combines these two techniques together with the use of FPGA distributed memory blocks implemented in LUTs (Look-Up Tables) to overcome negative features of each previous method without losing the most of positive features. Our solution provides a balance between the two previous techniques and exceeds them in speed, resources utilization and latency of (re)initialization.
Cross-Layer Optimization and Receiver Localization for Cognitive Networks Using Interference Tweets

A cross-layer resource allocation scheme for underlay multi-hop cognitive radio networks is formulated, in the presence of uncertain propagation gains and locations of primary users (PUs). Secondary network design variables are optimized under long-term probability-of-interference constraints, by exploiting channel statistics and maps that pinpoint areas where PU receivers are likely to reside. These maps are tracked using a Bayesian approach, based on 1-bit messages - here refereed to as "interference tweet" - broadcasted by the PU system whenever a communication disruption occurs due to interference. Although nonconvex, the problem has zero duality gap, and it is optimally solved using a Lagrangian dual approach. Numerical experiments demonstrate the ability of the proposed scheme to localize PU receivers, as well as the performance gains enabled by this minimal primary-secondary interplay.
NEURAL MECHANISMS FOR SELF-ORGANIZATION OF EMERGENT SCHEMATA, DYNAMICAL SCHEMA PROCESSING, AND SEMANTIC CONSTRAINT SATISFACTION

The concept of schema and some general characteristics of models using schemata are discussed. It is shown by computer simulations how a combination of a number of simple neural circuits are capable of performing actions similar to those commonly attributed to schemata, especially self-organization of a representational code, recognition of spatial and temporal structure, adaptive performance and semantic constraint satisfaction.
Fine-Grained Activation for Power Reduction in DRAM

This DRAM architecture optimization, which appears transparent to the memory controller, significantly reduces power consumption. With trivial additional logic, using the posted-CAS command enables a finer-grained selection when activating a portion of the DRAM array. Experiments show that, in a high-use memory system, this approach can reduce total DRAM device power consumption by up to 40 percent.
Congestion in randomly deployed wireless ad-hoc and sensor networks

Congestion in wireless ad-hoc sensor networks not only causes packet loss and increases queueing delay, but also leads to unnecessary energy consumption. In these networks, two types of congestion can occur: node-level congestion, which is caused by buffer overflow in the node, or link-level congestion, when wireless channels are shared by several nodes arising in collisions.
Regional grey matter volume correlates of gambling disorder, gambling-related cognitive distortions, and emotion-driven impulsivity

ABSTRACT Reports of regional grey matter volume (GMV) anomalies in patients with gambling disorder (PGD) are inconsistent, which can be attributed to methodological disparity and inattention to individual variability. Voxel-based morphometry was used to compare GMV between 25 PGD and 25 healthy controls. Additionally, the study explored associations of interpretative bias (IB, the tendency to reinterpret gambling outcomes) and negative urgency (NU, the tendency to act rashly under negative affect) with GMV in patients. These measures were chosen based on their sound association with gambling disorder in related studies. GMV tests were corrected across the whole brain (using a combination of voxel and cluster-level thresholds for a clusterwise-equivalent p ≤ 0.05). GMV was smaller in PGD than in controls in the dorsomedial prefrontal cortex. In PGD, a stronger cognitive distortion (higher IB) was associated with reduced GMV in the dorsal anterior cingulate; and patients with higher levels of impulsivity (higher NU) presented reduced GMV in the right ventrolateral prefrontal cortex. These findings are consistent with recent studies exploring individual differences in GD. However, the area discriminating between groups showed no overlap with the ones associated with IB and NU. These traits are thus unlikely to be responsible for between-group GMV differences.
Peripheral nerve injuries: A retrospective survey of 456 cases

This 16‐year retrospective study reports the data on 456 consecutive patients with 557 peripheral nerve injuries (PNIs) between 1989 and 2004. Most patients were men (74%) and the mean age was 32.4 years. In 83% of cases the PNIs were isolated; combined lesions most commonly involved the ulnar and median nerves. Upper‐limb PNIs occurred in 73.5% of cases; the ulnar nerve was most often injured, either singly or in combination. Vehicle accidents affecting the brachial plexus or radial, sciatic, facial, and peroneal nerves were the most common cause of injury. Penetrating trauma commonly affected the ulnar and median nerves; falls and gunshot wounds frequently affected the ulnar, radial, and median nerves; and sports injuries, particularly soccer, affected mainly the peroneal and tibial nerves. More than half of the brachial plexus lesions after vehicular accidents were from motorcycle crashes. Muscle Nerve, 2006
Scaling-up social enterprises: The effects of geographic context

Abstract Social enterprises implement business-like approaches to address social problems. Scale-up of these enterprises beyond one geographical context can extend their impact to better match the scope of problems being addressed. Unfortunately, many social enterprises start but relatively few expand to new contexts, making scale-up one of the most important but least understood outcomes of social entrepreneurship. We explore this outcome empirically, extending existing research that is predominantly conceptual. The study adopts a multicase study research design. A dynamic capabilities framework reveals how resources are amassed and configured for expansion, a process that can be more difficult for social compared to commercial enterprises. Findings suggest scale-up may be a second act of social entrepreneurship because dissimilarities between initial and scale-up contexts necessitate product modification, different partnerships, and idiosyncratic resource configurations. We thus call into question existing literature’s focus on standardization – generic resource configurations – for scaling-up social enterprises to new geographical contexts.
Scoring functions for automatic arrangement of business interiors

In this paper we introduce a new approach to the problem of automatic interior arrangement. We focus on arranging business interiors. The new idea is to look at the problem from the recommender systems perspective. In earlier works automatic interior arrangements were addressed with either optimization methods or procedural tools. Procedural methods are based on a set of growth operations which enable us to generate acceptable scenes. Optimization methods are based on an assumption that we know a formula that describes the most appropriate arrangement and the scene is modified iteratively to maximize the formula. We introduce a custom procedural mechanism, but do not define an optimization function. We use instead a scoring function. Our system consists of three steps. Firstly, we utilize randomized and distributed procedural generation mechanism. Secondly, we evaluate all proposals with respect to a scoring function. Lastly, we select a few top ranked, but diversified recommendations for a user. Our scoring function is similar to an optimization function (or a cost function). However, it does not need to be hand written by an expert, which is required in case of an optimization function. Its parameters can be estimated and various forms of scoring functions (or recommender systems) can be compared.
Comparative Analysis from Experimental Psychology: Application of Behavior Modification Methods for Children with Down Syndrome Based on Islamic Value

The Holy Qur’an Surah An Nisa verses nine said that people must fear to let their child in weakly future even though they were special needs child. Statistics said that the presence of special needs kids have increased, particularly Indonesia. This research subjects are children with Down Syndrome. Down Syndrome is a congenital disorder arising from a chromosome defect, causing intellectual impairment and physical abnormalities. As a country with a largest Moslem netizen, the Moslem parents in Indonesia, especially the parents of Down Syndrome's child, must teach life skill which based on the Holy Qur’an and Blessed Sunnah such as Thaharah (purification). The method that was used is by Behavior modification through shaping and chaining. Shaping is a way of adding behaviors to a person’s repertoar. Chaining is the reinforcement of successive elements of a behavior chain. The research method is an experimental psychology.