Miracle-dz/UniFAR-FTUs huggingface dataset:
The Potential of Configurative Reference Modeling for Business to Government Reporting – A Modeling Technique and its Evaluation

The steady increase of information requirements imposed by governments and authorities has led to more and more regulations for both firms and governmental organizations. Business to Government (BtG) reporting summarizes all the tasks that are necessary to create legally required reports for the governments. Along with the steady increase of reporting regulations, we have observed a growing effort to manage data warehouses and reporting infrastructures. The overall goal of this paper is to develop a modeling technique that (a) is capable of managing regulatory report requirements, (b) can be used for the development of configurative reference models, and (c) increases the effectiveness and efficiency of conceptual data warehouse design. We demonstrate its usability by applying it to a constructed financial services case using the European Markets in Financial Instruments Directive (MiFID). Two modeling experiments with Information Systems (IS) master’s students provide evidence that configurative reference models applied to BtG reporting have a high probability of reducing data warehouse management effort.
Analysis for Multi-Coverage Problem in Wireless Sensor Networks

Since wireless sensor networks consist of a large number of tiny sensors with limited power supply, it becomes a major concern that how to extend sensor network lifetime and maintain sufficient sensing area at the same time. To achieve this goal, a broadly used strategy is to select some sensor nodes as working nodes to cover the deployment area and at the same time turn off redundant nodes. This paper proposes a mathematical model in which only if the proportion of the sensing range of nodes to the range of the deployment area is known, the number of the nodes needed to reach the expected coverage fraction can be calculated. This work is different from the most literature studying the coverage problem because it is not based on location information of sensor nodes, and thus the cost of hardware and the energy consumption on sensor nodes for deriving and maintaining location information can be considerably reduced. The simulated experiment suggests that in random deployment strategy, the error between the expected coverage fraction and the coverage fraction derived from the simulated experiment is no larger than 2% of the expected coverage fraction; when the expected coverage fraction and the coverage fraction derived from the simulated experiment is the same, the error between the number of working nodes derived from the calculation and the number of working nodes derived from the simulated experiment is less than 5% of the number derived from the calculation. It suggests that the results are identical to the experimental results. The analytical results in this paper can be widely adopted in handling sensor deployment, topology control, and other issues.
Learning embedded lines of attraction by self organization for pose and expression invariant face recognition

Abstract. Algorithms that mimic the computation and learning capabilities of the human brain are feasible solutions to many information-processing problems. We present a theoretical model based on the observation that images of similar visual perceptions reside in a complex manifold in an image space. To model the pattern manifold, we present a novel learning algorithm using a recurrent neural network based on the behavior of the brain. In designing a recurrent neural network, convergence dynamics of the network needs special consideration. We propose to modify this picture: If the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented with an unknown encoded representation of a visual image belonging to a different category. Based on this, we have developed a self-organizing line attractor to learn new patterns. A nonlinear dimensionality reduction technique is used to embed the points to a lower dimensional space that preserves the intrinsic dimensionality and metric structure of the data to enable fast and accurate recognition. Experiments performed on UMIST, CMU AMP, FRGC version-2, Japanese female face expression, and Essex Grimace databases show the effectiveness of the proposed approach in accurate recognition of complex patterns.
Measuring Radicalization in Online Social Networks Using Markov Chains

The growing penetration of online social networks (OSNs) has emerged as a strong operational tool for spreading online radicalization by adding intensity and acceleration to the operations of radical groups. The efficiency of ways and methods proposed to measure the dynamics of radicalization are limited by the availability of models for modeling the intricacies of human dynamics and heterogeneous time varying interactions. Most of the developed algorithms and tools mainly focus on the static network structures and tend to ignore the “origin” of a new radical idea or propagation of a “weak” topic which might find its relevance in the security domain later on with the passage of time. In this article we propose a mathematical model for measuring dynamic propagation of radicalization in OSNs using Discrete Time Markov Chains by discretizing the continuous time domain. We also discuss our experimental results obtained through the information network of Twitter. The approach presented here is scalable and can easily be extended for modeling diffusion of general topics in OSNs.
QALB: Qatar Arabic language bank

Automatic text correction has been attracting research attention for English and some other western languages. Applications for automatic text correction vary from improving language learning for humans and reducing noise in text input to natural language processing tools to correcting machine translation output for grammatical and lexical choice errors. Despite the recent focus on some Arabic language technologies, Arabic automatic correction is still a fairly understudied research problem. Modern Standard Arabic (MSA) is a morphologically and syntactically complex language, which poses multiple writing challenges not only to language learners, but also to Arabic speakers, whose dialects differ substantially from MSA. We are currently creating resources to address these challenges. Our project has two components: first is QALB (Qatar Arabic Language Bank), a large parallel corpus of Arabic sentences and their corrections, and second is ACLE (Automatic Correction of Language Errors), an Arabic text correc.
Simple neural networks for speech research

The paper is about a model of a physical system, the lips, throat and glottis, used in speech production. Simple steps are outlined for getting a neural network to model the system. The approach is illustrated by examples. An interesting example is a two tube model of the throat. Starting with an "electrical engineering model" of the glottis in the form of the Kelly-Lochbaum equations, an Overall Signal Flow diagram, we get: (a) the neural network layout directly; (b) the weights between various neural processing elements. The problem of designing the layout topology for optimum performance neural network has received much attention. Hence the importance of (a) which guarantees a network with the precisely the same characteristics as the electrical engineering model. If the electrical engineering model is optimum, then the derived ANN will be optimum. The model will give the steady state output wave resulting from a steady state glottal wave. The glottal wave can be obtained by a glottograph or by photoglottography. One can choose the glottograph because it interferes little with the speech process. The glottal wave thus obtained can test the model's ability to produce the corresponding speech wave. The N section tube model is lossless. From this it follows that it can be inverted to give the glottal wave given the speech wave. The inverted network may be able to act as a tool to investigate whether there is a reliable correlation between emotion of the speaker, such as fear, anger, even love, and the glottal wave.
A Semantic Web Portal ToConstruction Knowledge Exchange

A current construction challenge is the creation of a construction university to help promote innovation and best practice throughout the industry, with the aim of developing a strong network of organisations able to share knowledge at different levels. Establishing the Construction Knowledge Exchange will be an important first step towards this aim. The Semantic Web provides an appropriate platform for knowledge exchange, and the benefits of implementing Semantic Web technologies can be easily identified as Semantic Web technologies have the potential to increase the information consistency and the information processing quality of Web portals. To exchange knowledge, i.e. semantic exchange, requires a shared mechanism to classify domain knowledge-items or information into interrelated concepts, i.e. ontology. The ontology has been developed in the OWL Web Ontology Language and is the backbone of a Semantic Web Portal (the β-CKE portal). The β-CKE portal provides appropriate usability and availability to represent a user-friendly interface for a virtual community, and furthermore advanced functionalities for community users where enhanced search accomplishes significantly better search results than other information retrieval techniques. The evaluation of the β-CKE portal has revealed two additional assets: 1) a depicted map of the areas of construction expertise, which is very useful not only to stimulate the communication between people of different institutions working on the same field, but also to identify strengths and weaknesses, and 2) strengthens in promoting communication and information exchange not only making the information more accessible by being independent of the concrete device (laptop, mobile phone, etc), but also making the information more understandable by bridging the language gap between the academic and the industrial community by executing a dynamic mapping process keeping the autonomy of the different input data of these two communities.
Task matching and scheduling based on co-evolutionary model

Task matching and scheduling plays an important role in parallel and distributed systems. The efficacy and efficiency of conventional single population-based evolutionary algorithms (CEAs) decrease with the number of independent tasks. By analyzing the mechanism that makes CEA in-scalable, this paper proposes a task matching and scheduling algorithm based on the computational model of cooperative co-evolution, which is inspired by the co-evolutionary phenomena of natural species. Then, we discuss some problems related to the proposed algorithm such as construction of initial population, genetic operators including improved crossover and migration as a kind of mutation, cooperative interactions among species and fitness computation of individual. The algorithm was analyzed mathematically, which shows that the exponential increase index of the co-evolution based scheduling algorithm is higher than that of CEA. Simulation results verify the theoretical result.
A Probabilistic Forecasting Approach to Wind Turbine Control

In wind-based energy operations, turbine performance, part deterioration rates and regular maintenance costs play a significant role in the economic viability of such projects. Problems to determine in advance the optimal future state of variables such as generator torque or blade pitch angle may be framed as anticipatory control problems, though successful application in real-world operations requires reliable forecasts of the future state of local wind speed. The authors propose a general probabilistic forecasting approach within the geographically robust CRPS minimization estimation framework suited to make use of AMeDAS weather variable observations, with the chief goal of evaluating the utility of the AMeDAS observations in the context of a rotor speed control problem where one seeks to maximize long-term power output at a specific turbine. Tested over a full year’s worth of observations from sites across Japan, and using deterministic and non-deterministic evaluation metrics compared against standard references, the capacity of the proposed model as a forecaster at the 10-minute horizon was verified, and in doing so confirmed the potential wide-scale utility of the AMeDAS network in this and related anticipatory control problems.
SDH radio-relay systems for regional areas

CCITT (International Telegraphy and Telephony Consultative Committee) has standardized a new synchronous interface named SDH (synchronous digital hierarchy). A number of SDH-based digital radio systems have been developed. Design criteria for such SDH radio systems are described. Technical details and an illustration of some applications are presented. The 11/15 G-150 M (8 PSK) and 4/11 G-50 M (4 PSK) systems are presented in detail. <<ETX>>
Tolerating latency through software-controlled data prefetching

The large latency of memory accesses in modern computer systems is a key obstacle to achieving high processor utilization. Furthermore, the technology trends indicate that this gap between processor and memory speeds is likely to increase in the future. While increased latency affects all computer systems, the problem is magnified in large-scale shared-memory multiprocessors, where physical dimensions cause latency to be an inherent problem. To cope with the memory latency problem, the basic solution that nearly all computer systems rely on is their cache hierarchy. While caches are useful, they are not a panacea. Software-controlled prefetching is a technique for tolerating memory latency by explicitly executing prefetch instructions to move data close to the processor before it is actually needed. This technique is attractive because it can hide both read and write latency within a single thread of execution while requiring relatively little hardware support. Software-controlled prefetching, however, presents two major challenges. First, some sophistication is required on the part of either the programmer, runtime system, or (preferably) the compiler to insert prefetches into the code. Second, care must be taken that the overheads of prefetching, which include additional instructions and increased memory queueing delays, do not outweigh the benefits. This dissertation proposes and evaluates a new compiler algorithm for inserting prefetches into code. The proposed algorithm attempts to minimize overheads by only issuing prefetches for references that are predicted to suffer cache misses. The algorithm can prefetch both dense-matrix and sparse-matrix codes, thus covering a large fraction of scientific applications. It also works for both uniprocessor and large-scale shared-memory multiprocessor architectures. We have implemented our algorithm in the SUIF (Stanford University Intermediate Form) optimizing compiler. The results of our detailed architectural simulations demonstrate that the speed of some applications can be improved by as much as a factor of two, both on uniprocessor and multiprocessor systems. This dissertation also compares software-controlled prefetching with other latency-hiding techniques (e.g., locality optimizations, relaxed consistency models, and multithreading), and investigates the architectural support necessary to make prefetching effective.
The Geography of the International System: The CShapes Dataset

We describe CShapes, a new dataset that provides historical maps of state boundaries and capitals in the post-World War II period. The dataset is coded according to both the Correlates of War and the Gleditsch and Ward (1999) state lists, and is therefore compatible with a great number of existing databases in the discipline. Provided in a geographic data format, CShapes can be used directly with standard GIS software, allowing a wide range of spatial computations. In addition, we supply a CShapes package for the R statistical toolkit. This package enables researchers without GIS skills to perform various useful operations on the GIS maps. The paper introduces the CShapes dataset and structure and gives three examples of how to use CShapes in political science research. First, we show how results from quantitative analysis can be depicted intuitively as a map. The second application gives an example of computing indicators on the CShapes maps, which can then be used in statistical tests. Third, we illustrate the use of CShapes for generating different weights matrices in spatial statistical applications. All the examples can be replicated using the freely available R package and do not require specialized GIS skills. The dataset is available for download from the CShapes website (http://nils.weidmann.ws/projects/cshapes).
Epidemic Modelling: An Introduction

Preface 1. Some history 2. Deterministic models 3. Stochastic models in continuous time 4. Stochastic models in discrete time 5. Rumours: modelling spread and its cessation 6. Fitting epidemic data 7. The control of epidemics References and author index Subject index.
Matching and integrated display of brain images from multiple modalities

This article concerns the integration of multimodal volumetric brain images. Integration consists of two steps: matching or registration, where the images are brought into spatial agreement, and fusion or simultaneous display where the registered multimodal image information is presented in an integrated fashion. Approaches to register multiple images are divided into extrinsic methods based on artificial markers, and intrinsic matching methods based solely on the patient related image data. The various methods are compared by a number of characteristics, which leads to a clear preference for one class of intrinsic methods, viz. voxel based matching. Furthermore, two- and three-dimensional techniques to display multimodality image information are outlined.
Development of Daily Peak Power Demand Forecasting Algorithm using ELM

Due to the increase of power consumption, it is difficult to construct an accurate prediction model for daily peak power demand. It is very important work to know power demand in next day to manage and control power system. In this research, we develop a daily peak power demand prediction method based on Extreme Learning Machine(ELM) with fast learning procedure. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.
Exploring cognitive modelling in engineering usability design

Engineering practice moves towards user-centred product design. Without effective computer-aided design tools, relying only on subjective guidelines in design and user testing in prototyping has made usability design expensive and time-consuming. This paper explores how modern cognitive modelling (CogM) can systematically address the challenges facing engineering usability design. A CogM-aided design paradigm is investigated for the different requirements of conceptual and detailed design stages, its procedural framework, and its special training and design modes. As the proof of concept, cognitive models are used in conjunction with empirical experiments to evaluate a set of graphical user interfaces (GUIs) designs. A group of human subjects test different settings of three design variables for several information-retrieval tasks with these GUIs. Then cognitive models, using the adaptive control of thought–rational/perceptual–motor cognitive architecture and built with distinct visual search strategies commonly employed by users, simulate the same operations. It is shown that CogM has the potential to assist practical decision-making in engineering design. And cognitive models can pinpoint possible cognitive challenges when human users deal with product interfaces in order to seek better usability. This study is the first step towards integrating computational CogM tools for practical usability design.
Design of a system-on-a-chip for pattern recognition

Addresses the design and specification of a heterogeneous HW/SW architecture of a SOC (system-on-a-chip) for pattern recognition. In this design, a RISC processor is used along with specialized optimized coprocessors all articulated around a system bus (PI-bus). Once the algorithms involved are presented, we investigate the hardware/software codesign methodology, relying on the use of a system level simulator and extensive reutilization of arithmetic and signal processing operator library. We then present the system architecture and finally the VLSI physical integration. We conclude by giving results on the performance of the system and its VLSI characteristics.
External Joint Torque-Based Estimation of Contact Information

In the paper, a method for the estimation of the contact force and contact location is proposed. Contact can occur at any point on the body of the robot manipulator. The contact force estimation algorithm is based on the measurement of the part of the joint torques caused by the external force. As this is a complex nonlinear problem, the proposed method is based on nonlinear constrained optimization. Using certain assumptions, the complexity of the problem is reduced. In this paper, only robots with cylindrically-shaped links are considered. The simulation and experimental results show that the proposed approach allows the estimation of the contact forces by using only the joint torque sensors, without any additional external sensory systems for the detection of contacts along the robot body-structure.
Simple printed array for microwave power rectenna

A simple printed Yagi-Uda antenna (YUDE) operating at 2.45 GHz is proposed in this paper for wireless microwave power transmission. The antenna has an electromagnetic coupling feed, through a printed power splitter/combiner. The antenna is compared with two corresponding single structures having coaxial (YUSC) and electromagnetic coupling feed (YUSE), respectively. All antennas are well-matched at the corresponding frequency of operation with -10 dB reflection bandwidth of less than 2%. The antennas exhibit directional far-field E-plane radiation pattern. YUDE has the smallest 3 dB beamwidth indicating the highest directivity. The relative gain of the YUSE was found to be 7 dB better than YUSC. However, YUDE showed the least relative gain. This could be due to the imperfect feed location of the power splitter/combiner which was rather difficult to be achieved.
A 150-mW, 8-bit video-frequency A/D converter

The authors describe an 8-bit, extremely low-power, flash A/D converter LSI for video-frequency image signal processing. This converter uses a shallow-groove-isolated bipolar VLSI technology. It consumes only 150 mW, which is half the amount of the lowest power consumption so far reported. This low level of power consumption is achieved by the use of a comparator circuit, which is newly designed. This converter can digitize video signals of up to 10 MHz at a conversion rate of 30 MHz. A differential gain (DG) error of 1% and a differential phase (DP) error of less than 0.5/spl deg/ were observed.