Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling production using genetic algorithm for elastic knitted fabrics with wide ranges of quantities demanded

In dealing with the production of various products in some industries, many firms need to respond to wide ranges of quantities demanded. High responsiveness to wider ranges of quantities can be a competitive advantage for a firm in modern highly competing environments. To stay ahead of competitors, a firm should be able to schedule production in an efficient way. The purpose of this study is to investigate the effects of wide ranges of quantities demanded on the scheduling. The production scheduling problem of knitted fabrics is used to examine the influences. Genetic algorithm (GA) is employed to be the analytical tool. Optimal parameters including mutation rates and crossover rates that generate good performance are obtained experimentally. Results from this paper show that the makespan in wide ranges of quantities is lower than that in a small range. However, the machine utilization in wide ranges of quantities is lower than that in a small range. To schedule the production with a lower makespan and with reasonable machine utilization, one can divide a big customer order into many manufacturing orders with smaller quantities. In addition, the on-time delivery rate can be increased by adding a penalty factor of completion time of a job.
Stacking prediction for a binary outcome

A large number of supervised classi cation models have been proposed in the literature. In order to avoid any bias induced by the use of one single statistical approach, they are combined through a speci c "stacking" eta-model. To deal with the case of a binary outcome and of categorical predictors, we introduce several improvements to stacking: combining models is done through PLS-DA instead of OLS due to the strong correlation between predictions, and a speci c methodology is developed for the case of a small number of observations, using repeated sub-sampling for variables selection. Five very di erent models (Boosting, Nai ve Bayes, SVM, Sparse PLS-DA and Expert Scoring) are combined through this improved stacking, and applied in the context of the development of alternative strategies for safety evaluation where multiple in vitro, in silico and physico-chemical parameters are used to classify substances in two classes : "Sensitizer" and "No Sensitizer". Results show that stacking meta-models have better performances than each of the five models taken separately, and furthermore, stacking provides a better balance between sensitivity and speci city.
Improving Detection Performance of Duplicate Bug Reports Using Extended Centroid Features

According to recent work, detection on duplicate bug reports has received much attention. One of the reasons is that duplicate bug reports may consume time of bug triagers and software developers. In previous studies, many schemes have been developed for using text mining techniques or using the information retrieval and natural language processing techniques. In this paper, we propose a method to improve centroid characteristics by adjusting centroids with better initial values than based on Class-Feature-Centroid (CFC) (12). With the effectiveness of CFC, the centroid- based approach can obtain further improvements for detection performance. The method includes two steps. First, we extract inter-class and inner-class term indices from the corpus. Second, we enhance centroid calculation based on class features. Moreover, for similarity measure we also adapt the calculation of the traditional cosine similarity by denormalized cosine measure which is also used in (12).
Learning Sensitive Combinations of A/B Test Metrics

Online search evaluation, and A/B testing in particular, is an irreplaceable tool for modern search engines. Typically, online experiments last for several days or weeks and require a considerable portion of the search traffic. This restricts their usefulness and applicability. To alleviate the need for large sample sizes in A/B experiments, several approaches were proposed. Primarily, these approaches are based on increasing the sensitivity (informally, the ability to detect changes with less observations) of the evaluation metrics. Such sensitivity improvements are achieved by applying variance reduction methods, e.g. stratification and control covariates. However, the ability to learn sensitive metric combinations that (a) agree with the ground-truth metric, and (b) are more sensitive, was not explored in the A/B testing scenario. In this work, we aim to close this gap. We formulate the problem of finding a sensitive metric combination as a data-driven machine learning problem and propose two intuitive optimization approaches to address it. Next, we perform an extensive experimental study of our proposed approaches. In our experiments, we use a dataset of 118 A/B tests performed by Yandex and study eight state-of-the-art ground-truth user engagement metrics, including Sessions per User and Absence Time. Our results suggest that a considerable sensitivity improvements over the ground-truth metrics can be achieved by using our proposed approaches.
A unified approach to estimation and control of the False Discovery Rate in Bayesian network skeleton identification

Constraint-based Bayesian network (BN) structure learning algorithms typically control the False Positive Rate (FPR) of their skeleton identification phase. The False Discovery Rate (FDR), however, may be of greater interest and methods for its utilization by these algorithms have been recently devised. We present a unified approach to BN skeleton identification FDR estimation and control and experimentally evaluate the performance of FDR estimators in both tasks over several networks. We demonstrate that estimation is too conservative for most networks and strong control at common FDR thresholds is not achieved with some networks; finally, we identify the possible causes of this situation.
Visual parameters impacting reaction times on smartwatches

As a new generation of smartwatches enters the market, one common use is for displaying information such as notifications. While some content might warrant immediately interrupting a user, there is also information that might be important to display yet less urgent. It would be useful to show this content on the watch but not immediately draw the user's attention away from their primary task. In this paper, we investigate how fast three visual parameters draw a user's attention. In particular, we present data from a smartwatch user study where we examine the size, frequency, and color of a visual prompt and the associated impact on reaction time. We find statistically significant differences for size and frequency where smaller and slower result in the less immediate reactions. We also present reaction time distributions that a designer can use to tailor expected notification response times to match their content.
Autocorrection of arabic common errors for large text corpus

Automatic correction of misspelled words means offering a single proposal to correct a mistake, for example, switching two letters, omitting letter or a key press. In Arabic, there are some typical common errors based on letter errors, such as confusing in the form of Hamza ةزمھ, confusion between Daad داض and Za ءاظ, and the omission dots with Yeh ءای and Teh ءات . So we propose in this paper a system description of a mechanism for automatic correction of common errors in Arabic based on rules, by using two methods, a list of words and regular expressions.
A novel cardholder behavior model for detecting credit card fraud

Since credit card fraud costs the banking sector billions of dollars every year, decreasing the losses incurring from credit card fraud is an important driver for the sector and end-users. Rule-based fraud detection tools have been widely used as a part of credit card systems. Rules of such tools are determined by human fraud experts. However, experts mostly ignore cardholder-specific spending behavior. In this paper, we focus on analyzing the cardholder spending behavior and propose a novel cardholder behavior model for detecting credit card fraud. The model is named Cardholder Behavior Model (CBM).
Health and Economic Behaviour: a Critical Survey of the Literature

The purpose of this paper is to give a brief overview of the relevant literature on health economics. Theoretical and empirical researches are reviewed to examine the traditional perception of health and pro- vide some general intuition of why the study of the individual eco- nomic behaviour is important in the ¯eld of health. Health, wealth and lifestyles are intimately linked through a complex bidirectional re- lationship. Economists have contributed much at its understanding and have overcome the limits of the medical and epidemiological approaches. This survey concentrates on the main estimation problems found in the health production function approach. Econometric methods that take account of reverse causation, unobservable heterogeneity and endogene- ity are needed to recover the genuine causal e®ect of socio-economic factors and health-related behaviours on health. Finally, this survey looks at inequality in health as an urgent public interest issue and sug- gests to disentangle the causes of health inequalities from an economic perspective.
A PSO FPGA based architecture to optimize constrained functions

The search for optimal solutions in Science and Technology is one of the greatest challenges, because performance optimization, especially in complex systems, is often ruled with antagonistic variables such as budget constraints, security requirements and real-time responses. In light of this, many system optimization techniques are constantly being developed and presented to the scientific community. This paper proposes a parallels hardware architecture with FPGA (Field-Programmable Gate Arrays) that can make use of the parallel features that the Swarm Intelligence algorithm PSO (Particle Swarm Optimization) offers to mitigate the engineering Design Pressure Vessel (DPV) problem under constraints. We believe our proposed hardware architecture can be used as an alternative to the PSO software solution because its results are very promising when compared to those obtained by others papers which have relied on software as a solution platform.
Video based localization for Bertha

In August 2013, the modified Mercedes-Benz SClass S500 Intelligent Drive (“Bertha”) completed the historic Bertha-Benz-Memorial-Route fully autonomously. The self-driving 103 km journey passed through urban and rural areas. The system used detailed geometric maps to supplement its online perception systems. A map based approach is only feasible if a precise, map relative localization is provided. The purpose of this paper is to give a survey on this corner stone of the system architecture. Two supplementary vision based localization methods have been developed. One of them is based on the detection of lane markings and similar road elements, the other exploits descriptors for point shaped features. A final filter step combines both estimates while handling out-of-sequence measurements correctly.
Predicting Graduate-level Performance from Undergraduate Achievements

One of the principal concerns in graduate admissions is how future performance in graduate studies may be predicted from a candidate’s undergraduate achievements. In this study, we examined the statistical relationship between B.Sc. and M.Sc. achievements using a dataset that is not subject to an admission-induced selection bias. Our analysis yielded three insights. First, we were able to explain 55% of the variance in M.Sc. performance using a small set of highly discriminative B.Sc. achievements alone. Second, we found the final B.Sc. year to be most informative for future M.Sc. performance. Third, when a failed exam was repeated, the crucial piece of information was the grade achieved in the final attempt. We envisage that results such as the ones described in this study may increasingly improve the design of future admission procedures.
Randomly Colouring Sparse Random Graphs using a Constant number of colours.

In this work we present an eﬃcient algorithm which, with high probability, provides an almost uniform sample from the set of proper χ -colourings of an instance of sparse random graphs G n,d/n , where χ = χ ( d ) is a suﬃciently large constant. This work improves, asymptotically, the result of Dyer, Flaxman Frieze and Vigoda in [4] where the algorithm proposed there needs at least Θ( loglog n logloglog n ) colours.
Pt monolayer electrocatalyst for oxygen reduction reaction on Pd-Cu alloy: First-principles investigation

First principles approach is used to examine geometric and electronic structure of the catalyst concept aimed to improve activity and utilization of precious Pt metal for oxygen reduction reaction in fuel cells. The Pt monolayers on Pd skin and Pd1-xCux inner core for various compositions x were examined by building the appropriate models starting from Pd-Cu solid solution. We provided a detailed description of changes in the descriptors of catalytic behavior, d-band energy and binding energies of reaction intermediates, giving an insight into the underlying mechanism of catalytic activity enhancement based on the first principles density functional theory (DFT) calculations. Structural properties of the Pd-Cu bimetallic were determined for bulk and surfaces, including the segregation profile of Cu under different environment on the surface.
Semi- and Weakly- Supervised Semantic Segmentation with Deep Convolutional Neural Networks

Successful semantic segmentation methods typically rely on the training datasets containing a large number of pixel-wise labeled images. To alleviate the dependence on such a fully annotated training dataset, in this paper, we propose a semi- and weakly-supervised learning framework by exploring images most only with image-level labels and very few with pixel-level labels, in which two stages of Convolutional Neural Network (CNN) training are included. First, a pixel-level supervised CNN is trained on very few fully annotated images. Second, given a large number of images with only image-level labels available, a collaborative-supervised CNN is designed to jointly perform the pixel-level and image-level classification tasks, while the pixel-level labels are predicted by the fully-supervised network in the first stage. The collaborative-supervised network can remain the discriminative ability of the fully-supervised model learned with fully labeled images, and further enhance the performance by importing more weakly labeled data. Our experiments on two challenging datasets, i.e, PASCAL VOC 2007 and LabelMe LMO, demonstrate the satisfactory performance of our approach, nearly matching the results achieved when all training images have pixel-level labels.
Voice quality: Not only about “you” but also about “your interlocutor”

This paper investigates the effect of voice quality in commutative speech. Voice quality is often considered as the characteristic auditory colouring of an individual speaker's voice, but in our study, we find that voice quality can also reveal information about the interlocutor in everyday social interactions. In the correlation analysis between acoustic measures and interlocutors, the effect caused by the linguistic content was reduced by focusing on the corpus of the commonly-used Japanese word “yes”. The distributions of voice quality features, e.g., Normalized Amplitude Quotient (NAQ), jitter and shimmer, showed a clear difference among different interlocutors, e.g., friend or business partner. Automatic classification of interlocutors was conducted using random forest method with voice quality, prosodic and spectral features. The best classification accuracy was 76.9% over four interlocutors' data.
Finding the Shortest Path in Dynamic Network using Labeling Algorithm

This study concerns the problem of finding shortest paths from one node to all other nodes in networks for which arc costs can vary with time, each arc has a transit time and parking with a corresponding time-varying cost is allowed at the nodes. it show that this problem is equivalent to a classical shortest path problem in a timeexpanded network. The label correcting algorithm is used for finding shortest paths.
Eecient Real-time Index Updates in Text Retrieval Systems

As information retrieval (IR) systems emerge as the mainstream information nding tool within commercial enterprises due to the enormous popularity of World Wide Web (WWW) technology in the intranet environments, the ability to incorporate new and/or updated documents into the database in real time becomes an essential requirement. However, conventional IR systems are optimized for read queries by employing aggressive indexing and/or query result caching. The cost of maintaining the consistency between the indexes and the underlying database is expensive but assumed to be tolerable since update processing is performed in a batch fashion. This paper describes the design and implementation of a real-time index/cache consistency maintenance technique in Codir, a text retrieval system that features an integrated compression and and indexing scheme. The main idea behind Codir's real-time index consistency maintenance technique is to build transient index for new document updates, and to process read queries using both permanent and transient index. To minimize the performance overhead associated with document database updates, Codir integrates transient index with permanent index lazily either by piggybacking the integration task with read query processing or by periodic batch processing. Finally, unlike previous IR systems, Codir supports document deletion, based on a lazy invalidation approach.
E-CTP: An Energy-balanced Collection Tree Protocol for Power Constrained Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), as the network consists of low-cost sensor nodes with finite battery power, power efficient strategies must be applied for data transmission in order to prolong the lifetime of the network. It is important to minimize the total energy consumption in each round so that the network lifetime is maximized. In this study, we propose a new energy efficient protocol E-CTP based on the Collection Tree Protocol (CTP). Taking into account the power available to the whole network and single node, the Expected Transmission Value (ETX) was updated according to the proposed protocol. The related experiments were carried on TelosB, the results were compared in terms of lifetime and efficiency. We conclude that the new protocol has a longer lifetime and higher efficiency.
Computer simulation based parameter selection for resistance exercise

In contrast to most scientific disciplines, sports science research has been characterized by comparatively little effort investment in the development of relevant phenomenological models. Scarcer yet is the application of said models in practice. We present a framework which allows resistance training practitioners to employ a recently proposed neuromuscular model in actual training program design. The first novelty concerns the monitoring aspect of coaching. A method for extracting training performance characteristics from loosely constrained video sequences, effortlessly and with minimal human input, using computer vision is described. The extracted data is subsequently used to fit the underlying neuromuscular model. This is achieved by solving an inverse dynamics problem corresponding to a particular exercise. Lastly, a computer simulation of hypothetical training bouts, using athlete-specific capability parameters, is used to predict the effected adaptation and changes in performance. The software described here allows the practitioner to manipulate hypothetical training parameters and immediately see their effect on predicted adaptation for a specific athlete. Thus, this work presents a holistic view of the monitoring-assessment-adjustment loop.
Notice of RetractionThe relationship of leadership challenges, corporate strategies, knowledge management and information technology investment among Institutions of higher learning in Malaysia

Despite an interest among public and private institutions of higher learning (IHL) in Malaysia in managing information technology investment agenda, there has been very little research that highlights the significant role of strategies and leadership challenges in higher education field which emphasize appropriate IT investment linked to knowledge management issues. In this paper, the researcher integrated theories of knowledge-based view, a resource-based view of the firm and a strategy to develop a suitable framework and model for information technology research; and empirically tested the research model within the Tallon's information technology investment dimension. In particular, the influence of knowledge management issues among academic leaders which have been neglected in Malaysian previous studies were examined. Data from the survey were examined using multiple regression analysis. Results indicate that strategy and information technology investment explain 38% of variance in predicting the effort of IHLs to develop appropriate knowledge management system. An important management implication of this paper is that it confirms that the greater usage of both strategy and information technology investment and knowledge management system in higher education would enhance the operational and strategic direction of IHLs. This paper further suggests that a greater utilization of specific information technology investment in crafting the corporate strategy both in the aspect of operational effectiveness (internally) and strategic positioning (externally) would help leaders of IHLs to pinpoint areas within the universities where knowledge management issues are creating value.
A conceptual study of systems architecture for decentralised topology inference of distribution networks

Power system operation and control relies heavily on models for decision making. Determining the topology of electrical distribution networks is a critical part of producing these models and maintaining up-to-date topologies is a resource consuming and challenging task. This paper provides a conceptual study of a methodology and supporting system architecture for real time inference of electrical topology using process and model data from IEC 61850 compliant substation automation devices . The conceptual study aims to provide a discussion of the performance and scalability of such systems based on an analysis scenario. The decentralized topology inference system concept forms the basis for future work in operation and management of active distribution networks accommodating larger integration of distributed energy sources of intermittent nature.
The Jews of the United States, 1654 to 2000

Acknowledgments Introduction PART 1. THE EARLIEST JEWISH COMMUNITIES 1. American Jewish Origins: 1654--1776 2. Becoming American: 1776--1820 PART 2. THE PIVOTAL CENTURY 3. A Century of Migration: 1820--1924 4. A Century of Jewish Life in America: 1820--1924 5. A Century of Jewish Politics: 1829--1920 PART 3. TWENTIETH-CENTURY JOURNEYS 6. At Home and Beyond: 1924--1948 7. A Golden Age? 1948--1967 8. In Search of Continuity: 1967--2000 Notes Bibliography Index