Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Design of Knowledge Management Systems for School of Information System at XYZ University (A Case Study Approach)

The purpose of this paper is to analyze and to design knowledge management systems in the School of Information Systems at XYZ University, which will improve organization’s performance and deliver innovation to organization as well as provide solutions when it’s needed. Analytical method that used in this study consist of two methods namely data & information collection method and analysis & design information systems method. Data & information collection method was obtained based on literature review study, while analysis & design information systems method was obtained by using object based information systems with the unified processes. The conclusion from this research is knowledge management systems provide solutions to address the problem of managing knowledge in academic and operation area in the School of Information Systems, by designing web based information systems which can make the process of production and distribution knowledge more effective and more efficient.
Treatment of fuzziness in natural language by fuzzy lingual system-FLINS

The Laboratory for International Fuzzy Engineering Research (LIFE) is developing a natural language communication system called Fuzzy Lingual System (FLINS). This paper briefly describes methods used in FLINS to treat fuzziness in natural language, namely an inference method for natural language propositions involving fuzzy quantifiers, an inference method extended the case-based reasoning by fuzzy theory and a learning method using fuzzy analogy. <<ETX>>
Robust Fingerprint Extraction Algorithm for Audio Identification

Now a days there is a lot of advancements in multimedia and computer technologies. Sharing of multimedia files over the internet is very convenient. At the same time the copyright protection of the files is more needed. Audio fingerprinting is an effective way to provide the security of multimedia files. Audio Fingerprint is a representation of an audio signal using the inherent features of the audio. When presented with a query audio the fingerprints of the query and the collection of fingerprints database is matched and the file is retrieved. The existing algorithms does not robust against speed and pitch changes. In this paper fingerprint algorithm based on wavelet transform is proposed. Based on several features of the audio the fingerprints of the audio signal are generated. By viewing its experimental results the proposed algorithm is highly robust against AWGN, Linear speed and pitch changes.
A 2 way VLIW processor architecture for embedded multimedia applications

As the complexity of multimedia applications increases, the need for efficient and compiler-friendly processor architectures also grows. In this paper, a new multimedia processor architecture is proposed. This processor has a 2-issue VLIW architecture with 64-bit SIMD arithmetic functional units to exploit the instruction-level and subword data parallelism found in multimedia applications. Moreover, densely encoded instructions supporting memory operands, DSP-like addressing modes, and SIMD capability boost the performance while keeping the code size and hardware cost small. To maximally utilize this architecture, a software environment including a code converter, a VLIW compiler system, and a compiled simulator has also been developed. The processor core has been synthesized for LSI logic 0.25 /spl mu/m library, which results in the total gate count of 102 K. In spite of the relatively smaller issue rate, the proposed processor shows a comparable or higher performance in terms of both the cycle count and the code size when compared to the 8-issue TMS320C62xx, for DSP benchmark kernels and an H.263 video encoder.
Transport Map sampling with PGD model reduction for fast dynamical Bayesian data assimilation

The motivation of this work is to address real‐time sequential inference of parameters with a full Bayesian formulation. First, the proper generalized decomposition (PGD) is used to reduce the computational evaluation of the posterior density in the online phase. Second, Transport Map sampling is used to build a deterministic coupling between a reference measure and the posterior measure. The determination of the transport maps involves the solution of a minimization problem. As the PGD model is quasi‐analytical and under a variable separation form, the use of gradient and Hessian information speeds up the minimization algorithm. Eventually, uncertainty quantification on outputs of interest of the model can be easily performed due to the global feature of the PGD solution over all coordinate domains. Numerical examples highlight the performance of the method.
State-of-Health Estimation for Lithium-Ion Batteries Based on Wiener Process With Modeling the Relaxation Effect

State-of-Health (SOH) is an intuitive reflection to the capacity during the degradation of lithium-ion batteries. Accurate SOH estimation can not only grasp the battery performance but also achieve a better balance between the safety and economic benefits for lithium-ion battery application system. Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during long rest time. This paper mainly studies the impact of relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel SOH estimation method based on the Wiener process. First, the life cycle of a lithium-ion battery is divided into three parts, i.e., the degradation process that eliminates the relaxation effect, the capacity regeneration process during the rest time, and the degradation process of the regenerated capacity. Next, the degradation model after eliminating the relaxation effect is established based on linear Wiener process, the capacity regenerated model is developed by the normal distribution, and the degradation model of regenerated capacity is established based on a nonlinear Wiener process. Then, for the degradation model based on nonlinear Wiener process, a two-step maximum likelihood estimation (MLE) method for prior parameters is proposed, and the random variables representing the personality features are updated online under the Bayesian framework. For the capacity regenerated model, a parameter estimation method based on MLE is proposed. In addition, a one-step and a multi-step SOH prediction method based on piecewise modeling are developed. Finally, the experiments are carried out based on the degradation data of lithium-ion batteries published by the NASA, and the results show that the method proposed in this paper can effectively improve the accuracy of the SOH estimation.
Calibration of expert systems

Expert systems provide automated vehicles for providing advice, and enacting decisions which are commonly associated with human 'experts'. This research investigates calibration of expert systems to bring them in line with end user expectations. Calibration is typically involved in initial learning and setup of the knowledge base. Little research exists to provide insight into how much calibration to perform, or effective approaches to calibration. The research develops a short run comparative static analysis of expert systems development which focuses on expertise. The potential user of an expert system will demand a certain level of expertise and the potential provider of an expert system will be able to provide a certain level of expertise, depending on the sophistication of the expert system provided. The level of expertise provided depends, in part, on how well it is calibrated to the needs of the end user. Since calibration may require significant additional expenditures, as well as require a change in the life cycle approach used to develop expert systems, the decision to calibrate may incur significant additional costs in expert systems implementation. The research finds that only in certain situations is calibration justified. <<ETX>>
Replacing competition with cooperation to achieve scalable lock-free FIFO queues

In order to guarantee that each method of a data structure updates the logical state exactly once, al-most all non-blocking implementations employ Compare-And-Swap (CAS) based synchronization. For FIFO queue implementations this translates into concurrent enqueue or dequeue methods competing among themselves to update the same variable, the tail or the head, respectively, leading to high contention and poor scalability. Recent non-blocking queue implementations try to alleviate high contentionby increasing the number of contention points, all the while using CAS-based synchronization. Furthermore, obtaining a wait-free implementation with competition is achieved by additional synchronization which leads to further degradation of performance.In this paper we formalize the notion of competitiveness of a synchronizing statement which can beused as a measure for the scalability of concurrent implementations. We present a new queue implementation, the Speculative Pairing (SP) queue, which, as we show, decreases competitiveness by using Fetch-And-Increment (FAI) instead of CAS. We prove that the SP queue is linearizable and lock-free.We also show that replacing CAS with FAI leads to wait-freedom for dequeue methods without an adverse effect on performance. In fact, our experiments suggest that the SP queue can perform and scale better than the state-of-the-art queue implementations.
A Biometrics Invited Paper. Factor Analysis: An Introduction to Essentials I. The Purpose and Underlying Models

Factor analysis aims to explain observed relations among numerous variables in terms of simpler relations. The simplification may consist of producing a set of classificatory categories, or creating a smaller number of hypothetical variables. Such resolution is so central to all scientific work that we must pause in a moment to ask about the relation of factor analysis to investigatory methods in general. However, it may be said forthwith that its most valuable functions lie in the biological and behavioral sciences, where a great array of phenomena are multiply determined and where the conceptual independent variables are not easily located and agreed upon. For example, in the behavioral sciences, a student of delinquency or mental disorder may have doubts whether there is such a single thing as delinquency or mental disorder and must have an open mind as to where the important causal influences lie in the bewildering array of possibilities. In such a situation he may take a set of variables, each semantically entitled to be called a form of delinquency, and measure each on members of a population and then seek for the functional unities by computing correlation coefficients for each pair of variables over the list of people concerned. Into this correlation matrix-as we designate the square matrix of coefficients shown in Table 1 below-he may also introduce measures of putative causal influences, seeking in the subsequent factor analysis to find those underlying 'hypothetical variables' or factors among them which are of -theoretical importance. Or again a biologist concerned with nutrition might correlate-over many animals, each fed in a different wayvarious signs of health and disease and various ingredients in the food. The vitamins, for example, might historically have been located-perhaps more economically of research time than they were-as factors each with a certain pattern of deficiency signs and of relative presence in various foods.
An Efficient Job Scheduling for MapReduce Clusters

The job scheduling for Map Reduce clusters has received significant attention in recent years, because it plays an important role on Map Reduce clusters. Traditional job scheduling performs poorly in assigning a task to appropriate nodes, and can not predict the resource utilization of the unexecuted tasks. To address the problems, an efficient job scheduling for Map Reduce clusters is proposed in this paper. The job scheduling introduces dynamic priority scheduling and real-time prediction model. Dynamic priority scheduling introduces the minimum cost data locality algorithm with a weight to deal with different size jobs, and real-time prediction model can predict the resource utilization of unexecuted tasks by calculating the running tasks. The resource utilization contains CPU, memory, and network. Experimental results prove that the proposed job scheduling is able to perform well in Map Reduce clusters.
Considerations for targets in 3D pointing experiments

We identify various tradeoffs in the design of 3D pointing experiments based on Fitts' law and the ISO9241-9 methodology. The advantages and disadvantages of several approaches for such experiments are analyzed and compared against each other. Results of an experiment that investigates various visual aids are presented as evidence. We conclude with recommendations for 3D pointing experiments and avenues of future work.
Cloud Model-based Outlier Detection Algorithm for Categorical Data

Most of the existing outlier detection methods aim at numerical data, but there will be a large number of categorical data in real life. Some outlier detection algorithms have been designed for categorical data. There are two main problems of outlier detection for categorical data, which are the similarity measure between categorical data objects and the detection efficiency problem. A cloud model-based outlier detection algorithm for categorical data is proposed in this paper. The algorithm is based on data driven idea and does not require the user to specify parameters. We utilize the synthetic data set and real data set to verify, compare our algorithm with the existing outlier detection algorithms for categorical data, and the experimental result demonstrates that our proposed algorithm has a higher detection rate and lower false alarm rate, while the time complexity is also more competitive.
Reliable Multi-path Routing Protocol in Wireless Sensor Networks

There are often some faults happening in the wireless sensor networks due to node failure or energy exhaustion. It is because the nodes in sensor networks are energy constrained and usually deployed in harsh environments. These factors strongly influence reliability of data transmission and decrease the lifetime of networks. An important issue in designing wireless sensor network is the reliable routing protocols which provide as much reliability of data transmission as possible. Another important factor is to make the best use of the limited energy presented by nodes of wireless sensor networks. This paper proposes a new routing protocol called REEM(Reliable and Energy-Efficient Multi-path routing protocol) to settle above problems. Simulation results show that our protocol surpasses the MSR, AOMDV and ARAMA protocols in terms of reliability and network lifetime.
L'impiego dei farmaci antidepressivi nel puerperio

: The post-partum period, as well as pregnancy, is associated with an increased risk of anxiety and/or affective disorders. Postnatal depression, frequently in co-morbidity with anxiety symptoms, is recognised as the most frequent form of maternal morbidity after delivery, with a prevalence rate estimated between 5% to 15%. Among antidepressant drugs, the SSRIs are considered the drugs of choice in the treatment of post-partum affective disorders, particularly in the major depression. It is, thus, crucial from a clinical standpoint to establish, in the newborn whose mother needs to be treated with an SSRI, the safety profile of these drugs during breastfeeding. The benefits of breastfeeding, on the other hand, both for the nursing mother and the infant, are in fact very well documented. Unfortunately, all antidepressant drugs, including SSRIs, cross into breast milk and the milk-to-plasma ratio, a measure proposed to establish the amount of drug transferred to maternal milk, does not seem to be a reliable parameter to predict the safety of these drugs. From the available literature, however, it seems that among SSRIs, paroxetina and sertralina offer the best safety profile, as these drugs has never been associated with unsafe reports in suckling infants. Despite these reassuring but preliminary data, more studies are needed to better assess the safety of the antidepressant drugs in the infants exposed during breastfeeding. As general rule, it is important to recommend if the mother wishes to breastfeed her infant while taking an antidepressant, that the baby should be closely monitored in order to detect, as soon as possible, any unwanted drug-related side effect.
The Transcultural Diabetes Nutrition Algorithm Toolkit: Survey and Content Validation in the United States, Mexico, and Taiwan

Abstract Background: Evidence demonstrates that medical nutrition therapy (MNT) in prediabetes and type 2 diabetes (T2D) improves glycemic control and reduces diabetes risks and complications. Consequently, MNT is included in current clinical practice guidelines. Guideline recommendations, however, are frequently limited by their complexity, contradictions, personal and cultural rigidity, and compromised portability. The transcultural Diabetes Nutrition Algorithm (tDNA) was developed to overcome these limitations. To facilitate tDNA uptake and usage, an instructional Patient Algorithm Therapy (PATh) toolkit was created. Content validation of tDNA-PATh is needed before widespread implementation. Subjects and Methods: Healthcare providers (n=837) in Mexico (n=261), Taiwan (n=250), and the United States (n=326) were questioned about challenges implementing MNT in clinical practice and the projected utilization and impact of tDNA-PATh. To assess the international portability and applicability of tDNA-PATh, th.
When Negotiations Become Routine

The aim of this paper is to apply the insights of recent research on routine in the context of repeated negotiations. To demonstrate the link between both concepts, we introduce an analytical framework, based on which we identify different negotiation situations in which routine can develop. Following various definitions, we distinguish two dimensions of the negotiation process: a problem solving dimension and a communication dimension which imply and determine the dimensions of our framework, namely: the degree of substance standardization in negotiated deals and variability of negotiation partners respectively. We introduce the concept of a critical skill set and argue that in repeated negotiations, it can be reduced to the part which is relevant for coping with the dimension that remains unstable or non-standard throughout repetitions. Moreover, we demonstrate that our framework is inherently dynamic and we illustrate this with simplified business examples.
A wearable robotic arm with high force-reflection capability

Many exoskeletal robotic arms have been developed for teleoperation having force reflection. They can measure an operator's arm motion and apply reactive force to the operator as well. Previous research has emphasized control performance of motion tracking and force reflection mainly but ignored how comfortable an operator feels, that is, issues of human wearability. Most robotic arms are heavy and give an operator excess fatigue during the teleoperation. We propose a robotic arm that satisfies high wearability and high force-reflection simultaneously. In order to accomplish these two objectives, the proposed arm has a parallel mechanism, one for a forearm and the other for a brachium. The parallel mechanism has an analogy to human muscular structure in which many extensors and flexors interact with each other and generate torque. The prismatic joints in the parallel mechanism act as virtual human muscles and determine joint torque by contraction and relaxation. The configuration of the prismatic joints enhances human wearability because all the joints are placed on the circumference of cylinders. Consequently, this circular configuration distributes internal force over an arm and thus contributes to reduce human fatigue during operation. In addition, the robotic arm does not make any singularity in its kinematics. The kinematic parameters in the robotic arm are selected to maximize the operator's workspace while considering performance of force reflection.
Towards information-economical classification and ranking

The standard formulations of classification and ranking are rather strict in terms of information usage and may incur high information costs. Standard classification assumes that we need all the attributes to classify every test example. This is neither cheap nor necessary because the attributes can be expensive to obtain and quite often we can predict the label of a test example by looking at a small subset of the attributes. A more economical approach is to acquire the attributes of test examples sequentially and weigh the benefit and cost of acquiring more attributes at every step. To do this, we need to learn a sequence of decision rules that together minimizes the penalty costs due to misclassification and information acquisition costs. We focus on rejecting negative examples as early as possible and present the catenary support vector machine (catSVM). Standard classification learns a prediction function from a set of labeled examples. However, it is often expensive (and sometimes impossible) to obtain labels of individual examples. One economical alternative is to learn from aggregate label information which are easily available and cheap to obtain. We propose an SVM method to learn classification from group label proportions and provide a theoretical bound on its generalization error. The idea of learning from aggregate label information is also useful for ranking. Standard ranking relies on the relative preferences between individual examples for training which can be expensive or difficult to obtain. We propose a probabilistic model for the relative preferences among groups of individual examples and present several estimation methods.
Fading and carrier frequency offset robustness for different pulse shaping filters in OFDM

A carrier frequency offset (CFO) or a time varying (fading/Doppler spread) channel in an orthogonal frequency division multiplexed (OFDM) system will create interbin interference (IBI). IBI causes decreased SNR and increased symbol error rate (SER). The amount of CFO or Doppler spread which creates a given performance degradation depends on the number of channels, the SNR and the pulse shaping. The common OFDM scheme, with M-QAM and square pulses, are compared with an OFDM scheme with M-offset QAM (M-OQAM) and pulses of finite duration. The comparison is made both for stationary and flat fading channels. For flat fading channels, analytical methods have been developed for calculation of the SER caused both by IBI due to CFO and by IBI due to Doppler spread. The models show a good agreement with simulations. Comparison of the two different OFDM systems show that for stationary channels the system with pulse shaping allows a higher CFO than the system using square pulses. For a flat fading channel the two systems have similar Doppler spread and CFO robustness.
Some trouble-making aspects of load flow analysis in distribution systems and their solutions

Conventional load flow methods for transmission networks are not so efficient for distribution networks because of the special characteristics of the latter networks. The conventional transmission load flow methods such as Newton-Raphson or Fast-Decoupled might not converge in the case of distribution networks. Therefore, it is essential to utilise special load flow methods for distribution systems. This paper presents the methods introduced for solving distribution load flow problem. The inefficiency of the conventional methods is presented and the necessarily modification are discussed for distribution applications. A brief history of developed methods is then presented.