Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALYSIS OF PERFORMANCE MEASUREMENT OF CONSTRUCTION PROJECTS IN MALAYSIA

Performance measurement is used as a systematic way of judging project performance and has become critical to business and organisational success. Past studies have shown that the development of performance indicators is one of the key mechanisms to performance measurement. This paper provides an empirical analysis of the types of measurements used in the development of construction projects in Malaysia and the possibility of measuring project performance by means of performance indicators. A survey was conducted in Malaysia among the four project stakeholders: the Government, private client, consultants, and contractors. In total 93 respondents completed the questionnaire. The findings revealed that the five utmost types of measurement used in Malaysia were measuring contractor performance, followed with measuring project quality, project performance, project manager’s performance, project viability and feasibility, and consultant’s performance. Private sectors in Malaysia were seen to be more receptive, detailed and thorough in measuring project performance compared to the Government. Although the results reported the different types of measurement used, it highlighted that there are no systematic models that appeared to be adopted by the respondents in measuring project performance. As well as the types of measurement used, construction project performance in Malaysia could be measured based on performance indicators, incorporating both the financial and non-financial measures. The data was analysed by means of statistical analysis i.e., ranking variables based on the mean value and analysis of Variance (ANOVA) techniques. By means of a Wilcoxon Signed Rank Test of 2 related samples, 25 out of 28 indicators could be utilised to form a framework for performance measurement of construction projects in Malaysia at the four project phases. It is anticipated that the findings reported in this paper could be important for future strategies and guidelines for measuring project performance in Malaysia.
DBvLEA: A Demand-Based Approach to Virtual Link Mapping for Multi-Service Industrial Applications

Network virtualization is proposed in several research work as a means to overcome the ossification of the Internet. Its application relies on embedding algorithms to instantiate virtual networks on substrate infrastructures. Notably, those considered in the scope of traffic-engineering are developed to focus on efficient resource utilization with the aim of increasing the acceptance ratio of the algorithms. In this paper, a demand-based virtual link embedding approach for multi-service mapping in programmable industrial networks is proposed. The approach aims at increasing the overall acceptance ratio of virtual link embedding algorithms by increasing the acceptance of demand critical requests. The goal is achieved by minimizing the deviation between requested demands and the resources satisfying the demand. The approach, when analyzed against state-of-the-art shortest path approaches under the same simulation conditions, shows good results in terms of utilization of the network resources, acceptance of delay-critical traffic demands and overall acceptance ratio.
Design and Implementation of Automatic Classroom Attendance System based on Face Recognition

Traditional manual sign-up has the problems of generation check-in, time-consuming and laborious, and fingerprint, iris and other bio-check-in technologies are difficult to use and inconvenient to use. In order to solve the above problems, we developed an automatic classroom attendance system. The system is based on face recognition and developed in Visual Studio 2017 integrated development platform, using Emgu CV image library and Entity Framework. Several face recognition algorithms used in the system and Emgu CV image processing library are introduced. The implementation steps and key functions of the system are introduced. The system realizes the functions of multi-scale automatic detection, recognition and attendance in real-time video, improves the efficiency of course attendance, and has high popularization and application value.
Gradual Algebraic Data Types

Algebraic data types are a distinctive feature of statically typed functional programming languages. Existing gradual typing systems support algebraic data types with set-theoretic approaches, e.g., union and intersection types. What would it look like for a gradual typing system to support algebraic data types directly? We describe our early explorations of the design space of gradually typed algebraic data types using AGT [Garcia et al. 2016].
Approximated Bilinear Modules for Temporal Modeling

We consider two less-emphasized temporal properties of video: 1. Temporal cues are fine-grained; 2. Temporal modeling needs reasoning. To tackle both problems at once, we exploit approximated bilinear modules (ABMs) for temporal modeling. There are two main points making the modules effective: two-layer MLPs can be seen as a constraint approximation of bilinear operations, thus can be used to construct deep ABMs in existing CNNs while reusing pretrained parameters; frame features can be divided into static and dynamic parts because of visual repetition in adjacent frames, which enables temporal modeling to be more efficient. Multiple ABM variants and implementations are investigated, from high performance to high efficiency. Specifically, we show how two-layer subnets in CNNs can be converted to temporal bilinear modules by adding an auxiliary-branch. Besides, we introduce snippet sampling and shifting inference to boost sparse-frame video classification performance. Extensive ablation studies are conducted to show the effectiveness of proposed techniques. Our models can outperform most state-of-the-art methods on Something-Something v1 and v2 datasets without Kinetics pretraining, and are also competitive on other YouTube-like action recognition datasets. Our code is available on https://github.com/zhuxinqimac/abm-pytorch.
Relationship between saccadic intrusions and a bimodal aspect of inter-microsaccadic intervals

Eye posture exhibits ceaseless involuntary fluctuation during fixation. This fluctuation is referred to as the fixational eye movement (FEM), composed of microsaccades (MS)and drifts-tremor. Saccadic intrusions (SI)are involuntary, conjugate, and horizontal movements, which might be distinguished from MS. Square wave jerks (SWJ), single saccadic pulse (SSP), and double saccadic pulse (DSP)are the common types of SI. SI can be found in both healthy subjects and neurological patients. SI in patients with neurological diseases have been well studied, but not necessarily in healthy subjects. Occurrence of a series of SIs could affect shape of the inter-MS intervals (IMSI) distribution, one of the major characterizations of MS. Despite of the fact that IMSI is usually considered as either monotonically decaying exponential distribution or unimodal ex-Gaussian distribution, we found that it could be a bimodal distribution with a peak at 100–220 ms in healthy subjects, depending on how we discriminated SI from MS. We then characterized types of SIs that contribute to the peak at 100–220 ms.
TangiNet: A Tangible User Interface System for Teaching the Properties of Network Cables

Within many computer science related courses, networking is often included as a core module due to industry needs. An important topic within networking involves teaching students the properties of network cables and many students however find it challenging to learn networking-related topics thus reducing interest and motivation for learning. Through the use of Tangible User Interfaces (TUI), such challenges could be potentially addressed although limited work has been undertaken to create systems that teach networking related topics. In order to address this gap, this paper investigates the application of tangible user interface for teaching the properties of network cables through a proposed system called TangiNet. The proposed system was evaluated to assess four key aspects, namely, learnability, interaction, tangibility and enjoyment. For all the constructs investigated, positive overall results were obtained thus implying that such tool could be adopted in networking related courses for the addressed concepts.
Semi-supervised Discriminative CCA for Estimating Viewed Image Categories from fMRI Data

This paper presents a method that estimates viewed image categories from functional magnetic resonance imaging (fMRI) data via semi-supervised discriminative canonical correlation analysis (Semi-DCCA). We newly derive Semi-DCCA that enables direct comparison of fMRI data and visual features extracted from viewed images while taking into account the class information and additional visual features to avoid overfitting. The proposed method enables estimation of image categories from fMRI data measured when subjects view images by comparing fMRI data with visual features through Semi-DCCA. Experimental results show that Semi-DCCA can improve estimation performance of the viewed image categories.
Electromagnetic Analysis of an Axial Flux Permanent Magnet Generator

The paper presents the electromagnetic analysis of an axial flux permanent magnet generator. The numerical scheme contains the finite element method under the package of Ansys Maxwell 3D. The design has two rotors with a sandwiched stator, having 24 windings. The core structure is new with radial and axial flux orientations. The design has been analyzed within a 2 mm air gap in a three phase operation configuration for the aim of small power scale wind energy. The rated power of 3 kW has been obtained under the air gap magnetic flux of 1.1 T.
EFLightPM: An Efficient and Lightweight Persistent Memory System

Emerging non-volatile memory (also termed as persistent memory, PM) technologies promise persistence, byte addressability and DRAM-like read/write latency. A proliferation of persistent memory systems such as Mnemosyne, NVHeaps, PMDK and HEAPO have been proposed to leverage PM for fast data persistence. However, their performance may suffer from inefficiency issues, mainly caused by kernel/user layer context switches and heavy transaction logging overhead. Concretely, getting a persistent region in Mnemosyne, NV-Heaps and PMDK needs two kernel/user layer context switches since the mmap-like system calls are used, which leads to high latency. To guarantee data consistency, existing systems employ redo or undo logging techniques but they bring non-negligible overhead due to double writes and persistence ordering. In this paper, we develop EFlightPM, an efficient and lightweight persistent memory system to manage data in a fine-grained style. We decouple the data organization for persistent regions by placing large regions in the kernel layer while exposing small regions in the user layer. We also design a lightweight transaction mechanism using hybrid logging with high efficiency by minimizing the writes in the critical path. The experimental results show that compared with state-of-the-art persistent memory systems, EFlightPM manipulates fine-grained persistent data with less persistent region operation overhead and more transaction throughput.
On the Semi-Active Lateral Control of Two Building-Like Structures

The high population density combined with a high seismic activity in Mexico City demands a better way to protect the buildings from vibrations caused by earthquakes. Therefore, a structural modal analysis is required, thus obtaining the natural frequencies that would cause a fatal damage to the structure. By coupling these structures we can protect the buildings from seismic movements attenuating the most representative frequencies in the structure, coupling the structure with a magnetorheological damper, we can reduce the seismic movement by injecting control to the structure, preventing the collapse in response to its different responses to the excitement coming from the ground.
Investigating the Influence of On-Street Parking Guidance Strategies on Urban Mobility

Parking Guidance and Information (PGI) solutions are a type of Intelligent Transportation System aimed at helping drivers by suggesting routes leading to facilities with higher parking availability. Current PGIs are mainly limited to multi-storey car parks, where this availability information can be easily collected. On the other hand, monitoring on-street parking availability is a challenge, requiring very expensive sensor deployments. The actual benefits of such investments to provide up-to-date on-street parking availability data for PGIs has been barely studied.To fill this gap, in this paper, we present the results of an investigation on the influence of three different types of on-street parking information on urban mobility. Based on real on-street parking data from San Francisco (USA), we investigated the scenario where a PGI has to support a driver who has not found an on-street parking space at his/her destination, and has to decide on the next road to go. We compared four scenarios for the PGI guidance, based on: (I) actual parking availability information, collected from stationary sensors, (II) static information about the parking capacity of a road segment and (temporary) parking limitations, (III) static information about only parking limitations, and (IV) no information at all. Clearly these solutions have strong implications in terms of deployment and operational costs. Results show that there is a significant reduction of parking search with more informed strategies, but also that the use of real-time information makes sense only presence of limited parking availabilities. Indeed, whenever the parking dynamics are not very competitive, real-time data offers only a limited improvement over static one.
Modelling Hybrid Train Speed Controller using Proof and Refinement

The modern radio-based railway signalling systems aim to increase network's capacity by enabling trains to run closer to each other. At the core of such systems is train's on-board computer (discrete) responsible for computing and controlling the speed (continuous) of the train. Such systems are best captured by hybrid models, which capture discrete and continuous system's aspects. Hybrid models are notoriously difficult to model and verify, in our research we address this problem by applying hybrid systems' modelling patterns and stepwise refinement for developing hybrid train speed controller model.
BEXHI: A Mechanical Structure for Prototyping Bendable and EXpandable Handheld Interfaces

In this paper, we present BEXHI, a new mechanical structure for prototyping expandable and bendable handheld devices. Many research projects have pushed bendable surfaces from prototypes to commercially viable devices. In the meantime, expandable devices have become a topic of interest letting one foresee that such devices are on the horizon. With BEXHI, we provide a structure to explore the combined capabilities of these devices. The structure consists of multiple interweaved units allowing non-porous expandable surfaces to bend. Through an instanciation, we illustrate and discuss that the BEXHI structure allows for the exploration of the combination of both bend and expansion interaction spaces.
Target Localization in Multipath Propagation Environment Using Dictionary-Based Sparse Representation

This paper addresses the target localization problem in complex multipath propagation environment for three-dimensional (3-D) radar systems. Firstly, an approach based on the singular value decomposition (SVD) technique is developed to reduce the data dimension and formulate the joint multiple snapshot sparse representation problem in the signal subspace domain. Subsequently, a novel sparse representation based DOA estimation algorithm, combined with alternatingly iterative and dictionary refinement techniques, is proposed. The Cramér-Rao bounds (CRB) for the target DOA and attenuation coefficient estimations of multipath model are derived in closed forms. Experimental results based on both simulated data and measured data indicate that the target localization accuracy can be effectively enhanced by utilizing the proposed algorithm in complex terrain and/or limited snapshot scenarios.
Water and Heat-sink Cooling System for Increasing the Solar Cell Performances

Solar panel comprises some solar cells in series and parallel arrangements converting sunlight into electrical current in usable level. However, the cell arrangement causes large surface area exposed to sunlight that tends to be heat absorbent. Existing study shows that the efficiency of solar cell decreases about 0.5% for each 1°C-surface temperature increment. Various cooling systems for solar cells have been offered by many researchers. This paper proposes a passive cooling system that combines water-filled aluminium blocks and heat-sinks. The water-filled aluminium blocks absorb heat from the back of solar cells and the heat-sinks release this heat to the air. The experimental assessment showed that the proposed method is able to reduce surface temperatures about 11.91% lower than the non-cooled solar panel. Compared to the existing methods, the proposed coolant achieved 5.08% and 7.37% lower surface temperatures than water and heat-sink coolant subsequently. The proposed cooling system also exerted at least 6.28% higher power than the other methods.
Real-Time Battery Monitoring and Fault Identification Application on Electric Scooter

Peoples have shifted from fossil fuel-powered vehicles to electric vehicles these days. It is environmentally friendly and also saves more energy. Electric scooter has two main components which are BLDC motor as a prime mover and battery as energy storage. Therefore, design of monitoring and diagnosing instruments are needed to maintain battery performance. Design of the instrument necessarily arranged to optimize the battery usage, monitor and identify an interruption or damage on battery used in electric scooter. Parameters used in design are voltage, current, temperature, and battery capacity. Furthermore, in optimizing the battery usage, all of the parameters are processed by a Controller Area Network (CAN) protocol. The parameters will be displayed on android smartphone to provide the scooter performance and give an alert if the interruption or damage is occurred such as over temperature and unbalanced cell. Based on the experiment results, it is shown that maximum error for current testing is 2.17% and for voltage testing is 0.09%. Maximum unbalance cell testing value reaches 12 % with an average value over 1 minute is 8.4% while safe limit of unbalance cell is 10%.
Stochastic Description of Vibrational Energy Relaxation in Fluids

Vibrational energy relaxation of a diatomic solute in a liquid solvent is investigated by means of Langevin equation for the vibrator energy with the stochastic power source characterized by an exponential time correlation function. The memory effects included via the stochastic power source allow one to explain the behavior of the mean square energy fluctuations on short and long time scales that was obtained from molecular dynamics simulations.
Data protection in fog computing through monitoring and adaptation*

Fog computing has enormous potential to offer increased computational capacity with acceptable latency near the network edge. However, fog computing also introduces many risks relating to the protection of sensitive data, which threaten the practical adoption of the fog computing paradigm. In this paper, the main challenges of data protection in fog computing and potential mitigation techniques are briefly reviewed. We argue that, given the highly dynamic nature of fog computing systems and the negative side-effects of existing data protection techniques, such techniques should be used adaptively, always in accordance with the relevant data protection risks. We sketch an approach to monitor a fog system and activate data protection techniques adaptively, followed by a research agenda to elaborate the details of the proposed approach.
In situ impedance spectroscopy of the intestinal mucosa in an ischemia-reperfusion model

The disruption of the intestinal mucosa plays a key role in the evolution of shock and is the "motor of multiple organ failure". In this study, a novel technique is proposed and evaluated for assessing tissue damage to prevent MOF. Complex impedance spectroscopy measurements in a frequency range of 10/sup 2/-10/sup 6/ Hz were performed on ischemic intestinal tissues of pigs using a minimally invasive gastrointestinal catheter. The experimental protocol relied on an intestinal ischemia model produced by total occlusion of the superior mesenteric artery (SMA) in thirteen anesthetized pigs (15-25 kg) assigned to four different ischemia-reperfusion groups. Complex impedance spectra were recorded with simultaneous measurements of tonometric pHi in the ileum and SMA blood flow for 4 hours. Impedance spectra were reproducible each time within each group, and those of tissue under prolonged ischemia were significantly differentiable (p<0.05) from those of normally perfused tissue. Spectral changes over time were characterized and compared to measurements of ischemia (pHi) and perfusion (SMA blood flow). It is concluded that impedance spectroscopy does indeed measure changes in tissue injury, and could be a very useful tool to monitor end-organ status and guide therapy of patients in shock.
NATURAL RESOURCES SYSTEMS PROGRAMME PROJECT REPORT DFID Project Number R7856 Project Title Strengthening social capital for improving policies and decision-making in natural resources management Project Leader Dr. Pascal Sanginga Organisation International Centre for Tropical Agriculture- African Hi

Despite the recognition that policy processes are important for sustainable natural resources management (NRM), there is concern that agricultural research and technology development have not been reflected in policy change, nor have they affected decision-making processes of wider communities. Most policy research focuses on policy analysis, often at the macro, national level, ignoring the much more difficult and rather murkier part on how to get policies implemented and adopted by users and how to get the intended beneficiaries small-scale resource poor farmers, to influence policies in NRM. This paper reports results of a participatory policy action research process that aimed at strengthening local-level processes and capacity for developing, implementing and enforcing local policies and byelaws to improve the adoption of NRM technologies in Kabale, Uganda. The action research was built around six key components: (i) community visioning and planning; (ii) participatory policy analysis; (iii) policy dialogue linking bottom-up processes to higher level policy processes; (iv) policy learning events; (v) policy process management, and (vi) supporting policy action at different levels. As a result of this process, the pilot communities have formulated and implemented a number of integrative byelaws on soil erosion control, tree planting, animal grazing, wetlands management, bush burning and food security. Results suggest that recent decentralization reforms in Uganda provide significant opportunities for research to influence and support the process of policy change in NRM. To influence policy change in NRM, the paper suggests a five “INs” approach: (i) strengthening local institutions; (ii) providing information; (iii) linking byelaws to NRM innovations; (iv) promoting incentives, and (v) building network of influence. Influencing policy is, however, a long process that needs perseverance and commitment, and a sustained programme of interventions by multiple stakeholders.