Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Service Quality Using Weighted SERVPERF Approach

Service quality is an important aspect that has a direct impact on company. In addition, it is to be one of the consumer’s judgement in purchasing a product or service. Tight business competition and high consumers’ interest in training services encourage companies to enhance their service quality. The aims of this research are (1) to assess the service quality of coffee cupping training using weighted SERVPERF; (2) to analyze consumers’ response to the service quality provided by management; and (3) to analyze the effect of service quality on word of mouth in which customer satisfaction as a moderator variable. This research was conducted in Indonesian Coffee and Cocoa Research Institute. Survey method was applied to collect primary data by using questionnaire. Judgement sampling was used to select 102 respondents. The data were analyzed by descrip- tive method and statistical analysis. Moderator regression analysis was applied to predict the effect of service quality on word of mouth in which customer satis- faction as a moderating variable. The research found that the perceived service quality of coffee cupping training was 3.736 which categorized in relatively good on providing services. There were 3 attributes of service quality that must be immediately improved, among others (i) conformity of materials, visual practices and information provided by instructors to consumers’ needs; (ii) understand- able and up to date training materials; and (iii) duration of the training is matching with the consumers’ demand. The result of moderator regression analysis showed that interaction between service quality and customer satisfaction was not signifi- cant to word of mouth. Thus, customer satisfaction could not act to moderate in providing a better understanding of relationship between service quality and word of mouth than each variable on the model. There are expected external factors beyond the existing models that influences on word of mouth.
Studying Summarization Evaluation Metrics in the Appropriate Scoring Range

In summarization, automatic evaluation metrics are usually compared based on their ability to correlate with human judgments. Unfortunately, the few existing human judgment datasets have been created as by-products of the manual evaluations performed during the DUC/TAC shared tasks. However, modern systems are typically better than the best systems submitted at the time of these shared tasks. We show that, surprisingly, evaluation metrics which behave similarly on these datasets (average-scoring range) strongly disagree in the higher-scoring range in which current systems now operate. It is problematic because metrics disagree yet we can’t decide which one to trust. This is a call for collecting human judgments for high-scoring summaries as this would resolve the debate over which metrics to trust. This would also be greatly beneficial to further improve summarization systems and metrics alike.
Multi-user Detection with Different Linear Equalizers for the Downlink MIMO MC-CDMA Systems over Frequency Selective Fading Channel

Modern wireless communications require an efficient spectrum usage and high channel capacity and throughput. Multiple input and multiple output (MIMO), Linear equalizers, multi-user detection (MUD) and multicarrier code-division multiple access (MCCDMA) are possible solutions to achieve spectral efficiency, high channel capacity, eliminate MAI , eliminate ISI and robustness against frequency selective fading. In this paper, we combine all these techniques and investigate BER performance. We propose a low complexity receiver structure for Single-input Multiple-output (SIMO) downlink MC-CDMA systems. It employs an interference cancellation scheme to suppress the interference caused by the multipath fading channel. Also, the proposed scheme is developed for Multiple-input Multiple-output (MIMO) MC-CDMA system. The performance analysis of Downlink MIMO MC-CDMA systems with V-BLAST over frequency selective fading channel is investigated under various number of transmit and receive antennas. The simulation results show that MIMO MC-CDMA with V-BLAST multi-user detection provides high data rate and the BER significant improvement when MIMO-MC CDMA with multi user detection and different linear equalizers is applied.
Empathy in a Technology-Driven Design Process: Designing for users without a voice of their own

Smart textiles are often developed in sports- oriented contexts through technology-driven processes. In the medical context, practitioners themselves also invent and develop technological aids in response to needs that emerge in practice. In these cases, novel technology may be the first driver for design to secure functionality and reliability, but our study shows that these processes benefit from human-centric and empathic design approaches. The project develops smart textiles for infants with medical adversities, such as preterm birth, neonatal infections, or birth asphyxia, collaboratively with medical researchers. Our pilot research illuminates the need to use the interest group’s empathic understanding as a starting point for design, as the user of the garments does not yet have a voice of their own. In this paper, we develop the argument for empathic design in a technology-driven design process in the medical context.
Biomedical Image Segmentation and Object Detection Using Deep Convolutional Neural Networks

Quick and accurate segmentation and object detection of the biomedical image is the starting point of most disease analysis and understanding of biological processes in medical research. It will enhance drug development and advance medical treatment, especially in cancer-related diseases. However, identifying the objects in the CT or MRI images and labeling them usually takes time even for an experienced person. Currently, there is no automatic detection technique for nucleus identification, pneumonia detection, and fetus brain segmentation. Fortunately, as the successful application of artificial intelligence (AI) in image processing, many challenging tasks are easily solved with deep convolutional neural networks. In light of this, in this thesis, the deep learning based object detection and segmentation methods were implemented to perform the nucleus segmentation, lung segmentation, pneumonia detection, and fetus brain segmentation. The semantic segmentation is achieved by the customized U-Net model, and the instance localization is achieved by Faster R-CNN. The reason we choose U-Net is that such a network can be trained end-to-end, which means the architecture of this network is very simple, straightforward and fast to train. Besides, for this project, the availability of the dataset is limited, which makes U-Net a more suitable choice. We also implemented the Faster R-CNN to achieve the object localization. Finally, we evaluated the performance of the two models and further compared the pros and cons of them. The preliminary results show that deep learning based technique outperforms all existing traditional segmentation algorithms.
Informal Introduction to Algol 68

Make more knowledge even in less time every day. You may not always spend your time and money to go abroad and get the experience and knowledge by yourself. Reading is a good alternative to do in getting this desirable knowledge and experience. You may gain many things from experiencing directly, but of course it will spend much money. So here, by reading informal introduction to algol 68, you can take more advantages with limited budget.
Integration modes, global networks, and knowledge diffusion in overseas M&As by emerging market firms

Purpose This paper aims to examine how integration modes impact the acquirer knowledge diffusion capacity of overseas mergers and acquisitions (M&As) effected by emerging market firms and the role played by the global innovation network position of the acquiring firms in affecting this relationship. Design/methodology/approach Through the use of structural equation modelling and bootstrap testing, the hypotheses are tested by drawing upon a sample of 102 overseas M&As effected by listed Chinese manufacturing companies. Findings The results show that acquirers from emerging countries are unable to increase the knowledge diffusion capacity unless they choose the right post-merger integration mode. This paper also finds that the relationship between integration mode and knowledge diffusion is channelled through the centrality and structural holes of acquirers in the global innovation networks. When considering the combinations of different resource similarities and complementarities of the acquired firms, differences emerge in the integration model and network embedded path of acquirers in emerging countries. Practical implications Emerging market multinational enterprises should consider post-merger integration as a crucial facilitator to the crafting of global innovation network positions that promote knowledge diffusion. The choices of integration mode and brand management autonomy should be matched with the resource similarities and complementarities that exist between the acquirer and target firms. Originality/value Based on the resource orchestration theory and by focussing on network centrality and structural hole as the crucial links, this study provides a nuanced understanding of the relationship between post-merger integration and knowledge diffusion and sheds light on latecomer firms from emerging countries.
A novel dynamic localisation system for indoor and outdoor tracking

ABSTRACT Hybrid indoor and outdoor localisation system has generated substantial interest from researchers due to its potential use in many fields. Many research papers on hybrid localisation were focused on localisation accuracy and computation time instead of the process for seamless switching during the subject’s indoor-outdoor movement. Seamless and stable switching can improve the usability and practicality of a system in an ever-changing scenario. This paper presents a hybrid localisation system that can adapt to the changes in the environment. Two hybrid systems – Enhanced Fusion Hybrid (EFH) and Enhanced Unified Hybrid (EUH), and novel signal selection and switching algorithms suitable for dynamic environment where signal fluctuations and transient condition frequently occur, i.e. the Clean Radio Signal Label (CRSL) classification algorithm and the Adaptive Signal Thresholding (AST) algorithm are proposed. From the experimental results of EFH and EUH methods, the GPS and Wi-Fi combination produces the best results as compared to the combinations of GPS and GSM or GSM and Wi-Fi. The accuracy of estimates was improved to 2.0 m to 5.0 m. Further work shown that an accuracy of 2.0 m or less could be achieved with an increase in Wi-Fi nodes from three to four for the system.
Inpatient Trial of an Artificial Pancreas Based on Multiple Model Probabilistic Predictive Control with Repeated Large Unannounced Meals

Abstract Background: Closed-loop control of blood glucose levels in people with type 1 diabetes offers the potential to reduce the incidence of diabetes complications and reduce the patients' burden, particularly if meals do not need to be announced. We therefore tested a closed-loop algorithm that does not require meal announcement. Materials and Methods: A multiple model probabilistic predictive controller (MMPPC) was assessed on four patients, revised to improve performance, and then assessed on six additional patients. Each inpatient admission lasted for 32 h with five unannounced meals containing approximately 1 g/kg of carbohydrate per admission. The system used an Abbott Diabetes Care (Alameda, CA) Navigator® continuous glucose monitor (CGM) and Insulet (Bedford, MA) Omnipod® insulin pump, with the MMPPC implemented through the artificial pancreas system platform. The controller was initialized only with the patient's total daily dose and daily basal pattern. Results: On a 24-h basis, the first coh.
Internal Promotion Optimization

Most large Internet companies run internal promotions to cross-promote their different products and/or to educate members on how to obtain additional value from the products that they already use. This in turn drives engagement and/or revenue for the company. However, since these internal promotions can distract a member away from the product or page where these are shown, there is a non-zero cannibalization loss incurred for showing these internal promotions. This loss has to be carefully weighed against the gain from showing internal promotions. This can be a complex problem if different internal promotions optimize for different objectives. In that case, it is difficult to compare not just the gain from a conversion through an internal promotion against the loss incurred for showing that internal promotion, but also the gains from conversions through different internal promotions. Hence, we need a principled approach for deciding which internal promotion (if any) to serve to a member in each opportunity to serve an internal promotion. This approach should optimize not just for the net gain to the company, but also for the member's experience. In this paper, we discuss our approach for optimization of internal promotions at LinkedIn. In particular, we present a cost-benefit analysis of showing internal promotions, our formulation of internal promotion optimization as a constrained optimization problem, the architecture of the system for solving the optimization problem and serving internal promotions in real-time, and experimental results from online A/B tests.
UML Based Integrated Multilevel Checkpointing Algorithms for Cloud Computing Environment

Main objective of this research work is to improve the checkpoint efficiency for integrated multilevel checkpointing algorithms and prevent checkpointing from becoming the bottleneck of cloud data centers. In order to find an efficient checkpoint interval, checkpointing overheads has also considered in this paper. Traditional checkpointing methods stores persistently snapshots of the present job state and use them for resuming the execution at a later time. The attention of this research is strategies for deciding when and whether a checkpoint should be taken and evaluating them in regard to minimizing the induced monetary costs. By varying rerun time of checkpoints performance comparisons are which will be used to evaluate optimal checkpoint interval. The purposed fail-over strategy will work on application layer and provide highly availability for Platform as a Service (PaaS) feature of cloud computing.
Gaussian fitted estimation of point spread function and remote sensing image restoration

In order to reduce or eliminate the image blur caused by the degradation in imaging process of a space remote sensing camera,the acquired images were restored to enhance the characteristic objects of images.First,a frequency-domain notch filter was adopted to remove noises in the remote sensing images.Then,the degradation function,namely,Point Spread Function(PSF) of the imaging system was estimated by ground characters with the knife-edge shape,and the estimated PSF was corrected with Gaussian fitting.Finally,the images were restored by using adaptive Wiener filtering with the fitted PSF.Experimental results show that almost all strip noises in the image are eliminated.After the denoised image is restored,its variance and gray mean gradient increase by 4.395 and 1.799,respectively,and also Laplacian gradient increases by 10.014. Experiments demonstrate that the restoration with Gaussian fitted PSF is beneficial to interpreting and analyzing the remote sensing images.After restoration,the blur phenomenon of the images is reduced,the characters are highlighted,and the visual effects of the images are clearer.
Real-time event detection using recurrent neural network in social sensors

We proposed an approach for temporal event detection using deep learning and multi-embedding on a set of text data from social media. First, a convolutional neural network augmented with multiple word-embedding architectures is used as a text classifier for the pre-processing of the input textual data. Second, an event detection model using a recurrent neural network is employed to learn time series data features by extracting temporal information. Recently, convolutional neural networks have been used in natural language processing problems and have obtained excellent results as performing on available embedding vector. In this article, word-embedding features at the embedding layer are combined and fed to convolutional neural network. The proposed method shows no size limitation, supplementation of more embeddings than standard multichannel based approaches, and obtained similar performance (accuracy score) on some benchmark data sets, especially in an imbalanced data set. For event detection, a long short-term memory network is used as a predictor that learns higher level temporal features so as to predict future values. An error distribution estimation model is built to calculate the anomaly score of observation. Events are detected using a window-based method on the anomaly scores.
The effects of different types of virtual reality display on perception of a virtual presenter

Virtual reality (VR) displays present an immersive 3D virtual world that convey a sense of presence. These displays are rapidly increasing in fidelity, from traditional 2d desk monitors to head-mounted displays (HMD’s) like the Oculus Rift. Research must investigate the relationship between this growing level of fidelity in displays and the virtual experiences they produce, especially as high-fidelity devices become more widespread. Evidence shows that the fidelity of VR displays can affect a variety of factors such as spatial awareness, communication, engagement, arousal, enjoyment, and presence. The current study investigated the potential relationship between the fidelity of VR displays and user’s perceptions of others. Participants were randomly assigned to view a virtual avatar deliver a speech on either a high-fidelity HMD VR display or on low-fidelity desktop VR display (traditional 2D monitor). Self-reported perceptions of the presenter’s credibility, motivation ability, social and task attractiveness, social presence, and dominance were then compared between the two groups to determine any affects. Fidelity did not impact perceptual evaluations in any significant way. Implications with respect to minimal cues and presence are discussed, as well as limitations and suggestions for future research.
Fuzzy AHP in a Knowledge-Based Framework for Early Flood Warning

Knowledge is essential for early flood warning as it can save life and property. This paper presents a novel knowledge-based framework based on rainfall, river water level, sediment, cloud distance and cloud strength that contributes to flood in Malaysia as the criteria in the AHP for Multiple Criteria Decision Analysis (MCDM). AHP caters complex decisions during flood events in uncertainty condition and provides fast decision making. The proposed framework is applied to the Bernam River Basin dataset located in Selangor, Malaysia. The framework is expected to produce early flood warning to the public.
A handheld computer-aided diagnosis system and simulated analysis

This paper describes a Computer Aided Diagnosis (CAD) system based on cellphone and distributed cluster. One of the bottlenecks in building a CAD system for clinical practice is the storage and process of mass pathology samples freely among different devices, and normal pattern matching algorithm on large scale image set is very time consuming. Distributed computation on cluster has demonstrated the ability to relieve this bottleneck. We develop a system enabling the user to compare the mass image to a dataset with feature table by sending datasets to Generic Data Handler Module in Hadoop, where the pattern recognition is undertaken for the detection of skin diseases. A single and combination retrieval algorithm to data pipeline base on Map Reduce framework is used in our system in order to make optimal choice between recognition accuracy and system cost. The profile of lesion area is drawn by doctors manually on the screen, and then uploads this pattern to the server. In our evaluation experiment, an accuracy of 75% diagnosis hit rate is obtained by testing 100 patients with skin illness. Our system has the potential help in building a novel medical image dataset by collecting large amounts of gold standard during medical diagnosis. Once the project is online, the participants are free to join and eventually an abundant sample dataset will soon be gathered enough for learning. These results demonstrate our technology is very promising and expected to be used in clinical practice.
Bit-error probability of uncoded QPSK transmitted over a 2-ray frequency selective Rayleigh fading channel

The authors study the error performance of uncoded quadrature phase shift keyed (QPSK) signals transmitted over a two-ray frequency selective Rayleigh fading channel. A Viterbi receiver with perfect channel state information is assumed. For a rectangular baseband pulse, it was found that the relative delay between the two arrival rays introduces a diversity effect which helps to improve the error performance over that of a one-ray, frequency non-selective fading channel. <<ETX>>
Detecting Holes in Point Set Surfaces

Models of non-trivial objects resulting from a 3d data acquisition process (e.g. Laser Range Scanning) often contain holes due to occlusion, reflectance or transparency. As point set surfaces are unstructured surface representations with no adjacency or connectivity information, defining and detecting holes is a non-trivial task. In this paper we investigate properties of point sets to derive criteria for automatic hole detection. For each point, we combine several criteria into an integrated boundary probability. A final boundary loop extraction step uses this probability and exploits additional coherence properties of the boundary to derive a robust and automatic hole detection algorithm.
Design and implementation of tool-chain framework to support OpenMP single source compilation on cell platform

The well-known performance and power bottlenecks in traditional architecture design, in conjunction with the sustained demand for high performance in real world applications, stimulated the creation of new designs that utilize multi-cores in one processor. There are two approaches in multi-core design: homogeneous and heterogeneous. The homogeneous design is based on the replication of simple cores. It is easier for developers to port existing applications to this kind of platform. However, great diversity exists among applications and a homogeneous multi-core chip cannot be optimal for heterogeneous workloads. Therefore, more and more multi-core designs tend to utilize heterogeneous cores and specialized accelerators. The Cell Broadband Engine (CBE) [1, 2] is a representative. Every Cell processor integrates one Power Processing Engine(PPE) core and eight Synergistic Processing Engines (SPE). A PPE core has a traditional memory and cache hierarchy and it accesses memory via caching mechanism. On the other hand, each SPE core only has 256K local storage and accesses its own local storage directly. All data exchange between the SPUs and shared system memory is via high-latency DMA operation. Therefore, this architecture presents great challenges to programmers who want to utilize parallelism: (1) Threads running on PPE are far different from the ones on SPEs, both in capability and ISAs. The users have to take care of programming threads in each ISA, as well as their cooperation and synchronization. (2) The SPE local storage is so limited that SPE code or data may have to be partitioned into overlay sections. Given the explicit memory hierarchy, it is necessary for the programmers to issue DMA instructions at the appropriate time and transfer