Miracle-dz/UniFAR-FTUs huggingface dataset:
Low precision storage for deep learning

We train a set of state of the art neural networks, the Maxout networks (Goodfellow et al., 2013a), on three benchmark datasets: the MNIST, CIFAR10 and SVHN, with three distinct storing formats: floating point, fixed point and dynamic fixed point. For each of those datasets and for each of those formats, we assess the impact of the precision of the storage on the final error of the training. We find that very low precision storage is sufficient not just for running trained networks but also for training them. For example, Maxout networks state-of-the-art results are nearly maintained with 10 bits for storing activations and gradients, and 12 bits for storing parameters.
Management of distributed knowledge encapsulated in embedded devices

Embedded electronic devices are now to be found everywhere. In general, they can be used to collect different sorts of data (e.g. on temperature, humidity, illumination and locations). In some specific domains, such as industrial automation, embedded devices are used for process control. The devices may have a programme that can respond immediately to environmental changes perceived through sensors. In the control of large sites, where there are many devices, higher level decisions are made or processed in dedicated computers far away from the sources (devices) where the initial data are collected. This article shows how it is possible to manage portions of distributed knowledge, hosted in embedded devices, making it possible for each embedded device to hold and manage its piece of knowledge. In addition, presented approach allows keeping locus of control at the embedded device level, where the embedded device can make decisions knowing the status of the rest of the world, device contributions and their effects in the overall distributed system knowledge base.
Bottomonia suppression in heavy-ion collisions from anti–de Sitter space/conformal field theory

We compute for the first time the suppression of bottomonia in a strongly coupled QGP and compare the results to those from a weakly coupled QGP. Using imaginary time techniques we numerically determine the real and imaginary parts of the ground state binding energy of the bottomonia in one potential computed from AdS/CFT and another computed from pQCD. We then use these binding energies in a suppression model to determine the $\Upsilon$(1S) nuclear modification factor in $\sqrt{s}_{NN}=2.76$ TeV Pb+Pb collisions. AdS/CFT significantly overpredicts the suppression compared to data, although the predictive power of our calculation is limited by its significant sensitivity to the exact details of the suppression model.
Using the Keyword Method and the Smart Board in vocabulary instruction for students with learning disabilities

Maria Grace Capozzoli Phillips USING THE KEYWORD METHOD AND THE SMART BOARD IN VOCABULARY INSTRUCTION FOR STUDENTS WITH LEARNING DISABILITIES 2012/2013 Joy Xin, Ph.D. Master of Arts in Special Education The purpose of this study was to examine the use of the Keyword Method and the Smart Board presentation in vocabulary instruction for students with Learning Disabilities (LD). A total of five students, ages 8-10 participated in this ten week study. Students were taught using traditional instructional strategies in Reading and Social Studies vocabulary lessons during the baseline. During the intervention, weekly instruction was provided with a Smart Board presentation to demonstrate each vocabulary word with the Keyword Method presented on a Smart Board, such as a mnemonic “catch word”, and a visual picture to demonstrate the meaning followed by practice and review to reinforce their learning. A multiple baseline design with A B phases across the subject areas of Reading and Social Studies was used to compare student performance in word recognition, identification, and application. All students showed an increase of their quiz scores of vocabulary acquisition in both Reading and Social Studies.
Business Process Modeling Notation (BPMN), Version 1.0

The Business Process Modeling Notation (BPMN) specification provides a graphical notation for expressing business processes in a Business Process Diagram (BPD). The objective of BPMN is to support business process management by both technical users and business users by providing a notation that is intuitive to business users yet able to represent complex process semantics. The BPMN specification also provides a mapping between the graphics of the notation to the underlying constructs of execution languages, particularly BPEL4WS. Status of this Document This document is version 1.0 of the BPMN specification submitted by members of the BPMI initiative on May 3, 2004. It supersedes any previous version. It has been produced based on the work of the members of the BPMI Notation Working Group. Comments on this document and discussions of this document should be sent to BPMN-PublicReview@bpmi.org. It is expected that as experience is gained with BPMN there will be feedback about this relatively young specification, particularly the mapping from the notation to BPEL4WS. Thus, this document may be updated, replaced, or made obsolete by other documents at any time.
Approximate Dynamic Programming in Tracking Control of a Robotic Manipulator

This article focuses on the implementation of an approximate dynamic programming algorithm in the discrete tracking control system of the three-degrees of freedom Scorbot-ER 4pc robotic manipulator. The controlled system is included in an articulated robots group which uses rotary joints to access their work space. The main part of the control system is a dual heuristic dynamic programming algorithm that consists of two structures designed in the form of neural networks: an actor and a critic. The actor generates the suboptimal control law while the critic approximates the difference of the value function from Bellman's equation with respect to the state. The residual elements of the control system are the PD controller, the supervisory term and an additional control signal. The structure of the supervisory term derives from the stability analysis performed using the Lyapunov stability theorem. The control system works online, the neural networks' weights-adaptation procedure is performed in every iteration step, and the neural networks' preliminary learning process is not required. The performance of the control system was verified by a series of computer simulations and experiments performed using the Scorbot-ER 4pc robotic manipulator.
A new motion energy based unequal error protection over wireless video transmission system

In order to deliver high quality video compressed by H.264/AVC standard with flexible macroblock ordering (FMO) applied over wireless environment, new motion energy based unequal error protection technique (UEP) is proposed. Motion energy (ME) of video frames is computed to determine the importance of frames. According to two different methods in calculating threshold value of motion energy, video frames can be scaled in three different levels and two different levels respectively. Appropriate forward error correction (FEC) codes with various coding rates are applied to different frames according to their importance. In two-level UEP method, low coding rate will be applied for high important frames while low important frames will be protected by the same code with high coding rate. While in three-level UEP method, one more coding rate named as medium coding rate is introduced to operate on middle-important frames. The proposed techniques will be verified by simulations.
Joint Training of Deep Auto-Encoders

Traditionally, when generative models of data are developed via deep architectures, greedy layer-wise pre-training is employed. In a well-trained model, the lower layer of the architecture models the data distribution conditional upon the hidden variables, while the higher layers model the hidden distribution prior. But due to the greedy scheme of the layerwise training technique, the parameters of the bottom layers are fixed after training. This makes it extremely challenging for the model to learn the hidden distribution prior, which in turn leads to a suboptimal model for the data distribution. We therefore propose a novel method for training deep autoencoders, where the architecture is viewed as one stack of two or more single-layer autoencoders. A single global reconstruction objective is proposed and jointly optimized, such that the objective for the single autoencoders at each layer acts as a local, layer-level regularizer. We empirically evaluate the performance of this joint training scheme and observe that it not only learns a better data model, but also learns better higher layer representations, which highlights its potential for unsupervised feature learning. In addition, we find that the usage of regularizations in the joint training scheme is crucial in achieving good performance. Our proposed work can thus provide a platform for investigating more efficient usage of different types of regularizers, especially in light of the growing volumes of available unlabeled data.
Metrics and algorithms for processing multiple continuous queries

Data streams processing is an emerging research area that is driven by the growing need for monitoring applications. A monitoring application continuously processes streams of data for interesting, significant, or anomalous events. Such applications include tracking the stock market, real-time detection of disease outbreaks, and environmental monitoring via sensor networks. Efficient employment of those monitoring applications requires advanced data processing techniques that can support the continuous processing of unbounded rapid data streams. Such techniques go beyond the capabilities of the traditional store-then-query Data Base Management Systems. This need has led to a new data processing paradigm and created a new generation of data processing systems, supporting continuous queries (CQ) on data streams. Primary emphasis in the development of first generation Data Stream Management Systems (DSMSs) was given to basic functionality. However, in order to support large-scale heterogeneous applications that are envisioned for subsequent generations of DSMSs, greater attention will have to be paid to performance issues. Towards this, this thesis introduces new algorithms and metrics to the current design of DSMSs. This thesis identifies a collection of quality of service (QoS) and quality of data (QoD) metrics that are suitable for a wide range of monitoring applications. The establishment of well-defined metrics aids in the development of novel algorithms that are optimal with respect to a particular metric. Our proposed algorithms exploit the valuable chances for optimization that arise in the presence of multiple applications. Additionally, they aim to balance the trade-off between the DSMS’s overall performance and the performance perceived by individual applications. Furthermore, we provide efficient implementations of the proposed algorithms and we also extend them to exploit sharing in optimized multi-query plans and multi-stream CQs. Finally, we experimentally show that our algorithms consistently outperform the current state of the art.
An ARL-unbiased Approach to Setting Control Limits of CCC-r Chart for High-Yield Processes

The cumulative count of conforming (CCC) chart is a new type of statistical process control technique for monitoring high-yield processes. Rather than counting the number of nonconforming items in a fixed sample size, a CCC chart monitors the cumulative number of items inspected until observing one nonconforming item. The CCC chart has shown to be superior to the traditional p chart in monitoring the fraction nonconforming of a high-yield process. The CCC-r chart is an improvement of the CCC chart. It monitors the cumulative number of items inspected until the r(superscript th) nonconforming item is observed based on negative binomial distribution. Due to the skewness of negative binomial distribution, the CCC-r chart usually shows an ARL-biased performance. In this paper, we introduce an ARL-unbiased design of the CCC-r chart. The ARL-unbiased design involves the determination of an adjustment factor of the existing control limits. Extensive numerical work shows that our approach can produce ARL-unbiased or nearly ARL-unbiased performance. In addition, the proposed approach shows more superiority in detecting process deteriorations, which is the major concern in high-yield processes. For simplification, a regression model is derived in this research. An adjustment factor can be calculated by specifying a predetermined false alarm rate α and the in-control nonconforming rate p0 to the regression equation. Then, the optimal control limits of an ARL-unbiased CCC-r chart can be easily obtained. An illustrative example is also presented to illustrate the proposed design procedure.
Model selection for SVM using mutative scale chaos optimization algorithm

This paper proposes a new search sWategy using mutative scale chaos optimization algorithm (MSCO) for model selection of support vector machine (SVM). It searches the parameter space of SVM with a very high efficiency and finds the optimum parameter setting for a practical classification problem with very low time cost. To demonstrate the performance of the proposed method it is applied to model selection of STM in ultrasonic flaw classification and compared with grid search for model selection. Experimental results show that MSCO is a very powerful tool for model selection of SVM, and outperforms grid search in search speed and precision in ultrasonic flaw classification.
Analysis of Expectations of Electronic Grocery Shopping for Potential Customer Segments

The special nature of groceries, especially perishables, challenges the possibilities of digital channels i.e. computers and networks to improve customer service. We report the findings of theme interviews on the potential of electronic commerce (EC) of groceries with 33 experts of grocery industry and EC, most of them senior executives, in 23 organisations in Finland during the Autumn of 1997. The specific areas of interest in the study are the predicted volume of EC in grocery shopping, the most potential customer segments, the anticipated benefits of EC to customers, and supplier strategies and infrastructure alternatives. Benefits of ECS (electronic grocery shopping) are analysed for three selected potential customer groups in terms of phases in consumer buying process as well as two different infrastructure solutions. It seems that an EGS build on top of current grocery industry infrastructure can only satisfy one major consumer group. In order to gain momentum EGS needs a dedicated infrastructure within which logistic efficiency can be increased.
Myers-Briggs Attitude Typology: The Influence of Birth Order with Other Family Variables

This study investigated the influence of birth order with the family variables of parenting style, age spacing, gender, and socioeconomic status on the Myers-Briggs attitude scales of Extraversion/Introversion and Judging/Perceiving. Results indicated that age spacing interacted with birth order to influence Extraversion and Introversion scores. Parenting style and gender interacted with birth order to influence Judging and Perceiving scores.
Pengaruh Penerapan Coi Framework pada Pembelajaran Online Terhadap Peningkatan Pemahaman (Subkategori Cognitive Presence) Mahasiswa

This study aims to discuss some of considerations in education to achieve good quality in learning instruction. The indicator is student’s comprehension increasing through the use of technological advances such as electronic learning (E-Learning) on social media and using Community of Inquiry (CoI) model as a comparative study to improve the quality of E-Learning program. The data used in this study were obtained through survey to intended students and literature study. Final conclusion of this study is a proposal to teachers on an interactive learning and rich of knowledge that will improve the learner’s comprehension.
Exploring Users' Attitudes and Intentions toward the Adoption of Cloud Computing in Saudi Arabia: an Empirical Investigation

Over the past few years, cloud computing has evolved as one of the major advances in the field of Information Technology (IT) utilizing third-party services. Therefore, trust in cloud vendors as well as the determination of potential risks, such as privacy and security issues, are crucial for ensuring the successful adoption of an appropriate cloud. Prior research have addressed the technical aspects of cloud-based environments, such as cloud virtualization, scalability and security. Nevertheless, it is argued that the biggest obstacle of cloud computing is not technological, rather it is perceptual or attitudinal. The adoption of cloud computing has been central to several scholarly research areas, particularly user acceptance. This study presents an extended Technology Acceptance Model (TAM), which integrates Trust (TR), Anxiety (ANX) and Perceived Risk (PR), to investigate users' attitudes and intentions toward the adoption of cloud computing. The proposed model was empirically examined using the Structural Equation Model (SEM) to analyze data gathered by a survey of both IT professionals and end users. The results herein suggested that trust, anxiety and PR can be successfully integrated within the TAMs. Trust has demonstrated to have a strong positive influence on Perceived Ease of Use (PEOU), but it had no significant effects on Perceived Usefulness (PU). Both anxiety and PR were found to have significant negative effects on PEOU and PU. In addition, Behavioral Intention (BI) to use the cloud can be predicted by trust, attitudes and PU, as PR was shown to have no significant effect on BI. The proposed model, including PR, trust and anxiety, has been demonstrated to be a true predictor of user intentions, toward the use of cloud computing, within the context of Saudi Arabia.
Efficient numerical method for solution of l2 optimal mass transport problem

Optimal Mass Transport (OMT) is an important problem with numerous applications in a wide range of fields such as econometrics, fluid dynamics, automatic control, transportation, statistical physics, shape optimization, expert systems and meteorology. More recently, it has been shown to also have application in image processing such as for non-rigid registration and morphing alongside other differential and variational methods based on fluid dynamics. In this thesis, we present a novel and efficient numerical method for the computation of the L2 optimal mass transport mapping in two and three dimensions. Our method uses a direct variational approach. We have formulated a new projection to the constraint technique that can yield a good starting point for the method as well as a second order accurate discretization to the problem. The numerical experiments demonstrate that our algorithm yields accurate results in a relatively small number of iterations that are mesh independent. In the first part of the thesis, we develop the theory and implementation details of our proposed method. These include the reformulation of the Monge-Kantorovich problem using a variational approach and then using a consistent discretization in conjunction with the “discretize-then-optimize” approach to solve the resulting discrete system of differential equations. We also develop advanced numerical methods such as multigrid and adaptive mesh refinement to solve the linear systems in practical time for even 3D applications. In the second part we show the methods efficacy via application to various image processing tasks. These include image registration and morphing. We present application of (OMT) to registration in the context of medical imaging and in particular image guided therapy where registration is used to align multiple data sets with each other and with the patient. We believe that an elastic warping methodology based on the notion of mass transport is quite natural for several medical imaging applications where density can be a key measure of similarity between different data sets e.g. proton density based imagery provided by MR. We also present an application of two dimensional optimal mass transport algorithm to compute diffeomorphic correspondence maps between curves for geometric interpolation in an active contour based visual tracking application.
Investigating Factors Influencing Adoption of Broadband in the Household

This research paper reports an empirical investigation of the attitudinal and control factors influencing broadband adoption in private residences. The aim of this research was to identify the challenges in terms of demand that Internet Service Providers (ISPs) face when deploying broadband. The study was conducted using a mail survey on a total of 172 household consumers from the London borough of Hillingdon. The survey was based upon the Theory of Planned Behaviour, the Decomposed Theory of Planned Behaviour and the Model of Adoption of Technology in the Household. This research indicates that broadband adoption in the household is driven by relative advantages such as faster access, utility outcomes, such as the uses of broadband for work purposes, and hedonic outcomes such as to use of broadband for entertainment. The main factors for non-adopters were high costs and lack of needs. The eventual adoption of broadband by consumers is not only an industry concern, but a government aim as well. Therefore this research should offer a substantial contribution to all interested stakeholders including ISPs and government.
Data mining using /spl Mscr//spl Lscr//spl Cscr/++ a machine learning library in C++

Data mining algorithms including machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper, we focus on classification algorithms and review the need for multiple classification algorithms. We describe a system called /spl Mscr//spl Lscr//spl Cscr/++ which was designed to help choose the appropriate classification algorithm for a given dataset by making it easy to compare the utility of different algorithms on a specific dataset of interest. /spl Mscr//spl Lscr//spl Cscr/++ not only provides a work-bench for such comparisons, but also provides a library of C++ classes to aid in the development of new algorithms, especially hybrid algorithms and multi-strategy algorithms. Such algorithms are generally hard to code from scratch. We discuss design issues, interfaces to other programs, and visualization of the resulting classifiers.