Miracle-dz/UniFAR-FTUs huggingface dataset:
Disk Backup Through Algebraic Signatures in Scalable Distributed Data Structures

A Scalable Distributed Data Structure (SDDS) allows to store a large scalable file over a distributed RAM. The file scales up transparently for the application over the nodes of a multicomputer, e.g., a local network of PCs. The prototype system termed SDDS 2000, designed by CERIA experiments with this technology for Wintel multicomputers. The application may manipulate data much faster than on local disks. We present the functions we have added to SDDS-2000 to backup the RAM on each storage node onto the local disk. Our goal is to write only the data that has changed since the last backup. We experiment for this purpose with the algebraic signatures. We present our architecture and design choices. Performance measures validate our implementation. It is now available for the noncommercial use as part of new version of our prototype termed SDDS-2002. 1. Introduction A Scalable Distributed Data Structure (SDDS) allows to store a large scalable file over a distributed RAM. The file scales up transparently for the application over the storage nodes of a multicomputer, e.g., a local network of PCs and WSs. The storage nodes are called the (SDDS) server nodes. The application manipulates through the SDDS interface at its node, called the (SDDS) client node. The client node performs the (scalable) address calculus and handles the application requests accordingly. Various SDDS schemes have been investigated, [C]. Probably the most studied were the linear hash partitioning variants of the LH* scheme, and the range
PENGARUH KOMBINASI TERAPI MUSIK DENGAN DEEP BREATHINGEXERCISE TERHADAP KECEMASAN DAN PARAMETER FISIOLOGISPADA KLIEN DENGAN VENTILASI MEKANIK

Pendahuluan : Lingkungan perawatan ruang intensif care memberikan kecemasan tersendiri pada klien, suara monitor, pemakaian ventilator dapat menjadi salah satu stresor terutama klien dengan tingkat kesadaran composmentis. Tujuan dari penelitian ini untuk menganalisis pengaruh kombinasi terapi musik dengan deep breathing exercise terhadap kecemasan dan parameter fisiologis pada klien dengan ventilasi mekanik. Metode : desain penelitian true eksperimental study dengan pendekatan pretest-postest with control group design. Sampel penelitian 35 klien kelompok perlakuan, 35 klien kelompok kontrol, total sampel 70 klien. Sampel diambil dengan metode consecutive sampling. Intervensi yang dilakukan yaitu memberikan kombinasi terapi musik dengan deep breathing exercise, pelaksanaan selama 30 menit sebanyak 2 kali sehari selama 5 hari pada jam 09.00 dan jam 16.00 wib. Pada klien yang dirawat menggunakan ventilasi mekanik dengan setting mode : PSIMV, ASV, PSV atau CPAP. Instrumen untuk mengukur tingkat kecemasan menggunakan kuesioner State Trait Anxiety Inventory (STAI) form-Y, dan intrumen parameter fisiologis menggunakan ukuran tekanan darah, MAP, nadi, respirasi rate (RR), saturasi oksigen (SaO2). Analisis data menggunakan uji Paired T-Test, uji Wilxocon, uji Manova, dan Mann-Whitney U dengan nilai signifikan p < 0,05. Hasil dan analisis : ada perbedaan antara dua kelompok pada tingkat kecemasan (p=0,000), denyut nadi (p=0,000), frekuensi pernafasan (p=0,001). Tidak ada perbedaan antara dua kelompok pada tekanan darah systole (p=0,459), tekanan darah diastole (p=0,901), MAP (p=0,461), dan SaO2 (p=0,717). Kesimpulan : berdasarkan hasil penelitian didapatkan bahwa kombinasi terapi musik dengan deep breathing exercise merupakan teknik relaksasi yang dapat mempengaruhi kecemasan dan parameter fisiologis klien yang memiliki resiko minimal sehingga aman dilakukan pada klien dengan ventilasi mekanik.
Generating hierarchical system descriptions for software error localization

The purpose of this study is to quantify ratios of coupling and cohesion and use them in the generation of hierarchical system descriptions. The ability of the hierarchical descriptions to localize errors by identifying error-prone system structure is evaluated using actual error data. Measures of data interaction called data bindings, are used as the basis for calculating software coupling and cohesion. A 135000 source line system from a production environment has been selected for empirical analysis. Software error data were collected from high-level system design through system test and from field operation of the system. A set of five tools is applied to calculate the data bindings automatically, and cluster analysis is used to determine a hierarchical description of each of the system's 77 subsystems. An analysis-of-variance model is used to characterize subsystems and individual routines that had either many/few errors or high/low error correction effort. <<ETX>>
Detecting breakdown in ultra-thin dielectrics using a fast voltage ramp

We propose a voltage ramp technique which detects ultra-thin oxide breakdown when other techniques (such as noise) fail. This technique provides a straightforward method of extending conventional ramp breakdown techniques to ultra-thin dielectrics (<3.5 nm). This technique monitors the use-condition current (or current below stress current) after each stress-step in the ramp. We apply this method to a wide range of oxide areas and gate oxide thicknesses. We show for the first time that the post-stress leakage current is independent of oxide area over 7 orders of magnitude and for 5 oxide thickness from 20 nm to 2.3 nm. In addition, we show that the voltage ramp Weibull distribution statistics scale with area and are consistent with constant voltage stress tests on 2 nm gate oxides. We model the post-breakdown I-V characteristics and show a space-charge limited behavior. This observation is used to explain why a modified voltage ramp technique is needed for ultra-thin oxides and defect-detecting large area structures. Finally, we demonstrate that new noise techniques may be necessary to detect breakdown during constant voltage stress in ultra-thin gate oxides.
The development of a novel biomechatronic hand-ongoing research and preliminary results

An ideal upper limb prosthesis should be perceived as part of the natural body by the amputee and should replicate sensory-motor capabilities of the amputated limb. However, such an ideal cybernetic prosthesis is still far from reality: current prosthetic hands are simple grippers with one or two degrees of freedom, which barely restore the capability of the thumb-index pinch. The paper describes the design and fabrication of a prosthetic hand based on a biomechatronic and cybernetic approach. Our approach is aimed at providing natural sensory-motor coordination to the amputee, by integrating biomimetic mechanisms, sensors, actuators and control, and by interfacing the hand with the peripheral nervous system.
Gender classification by processing emotional speech

Gender information improves emotion classification accura cy. Accordingly, gender classification for emotional speech is an interesting research t opic. A pool of 1418 features is created, 619 of which are tested for the first time in gender cl assification. This is the largest feature set to the best of the authors’ knowledge. The fe atures are related to statistics of pitch, formant, and energy contours, as well as autocorrelation, MPEG-7 descriptors, Fujisaki’s model parameters, jitter, and shimmer. A featur e selection algorithm derives 15 features, which are input to a nearest neighbor classifier, radial-basis function neural network, probabilistic neural network, support vector machines, discriminant analysis-based classifier, classification tree, self organizing map, and ne ural gas network. Two databases are employed: the Berlin database of Emotional Speech and the Danish Emotional Speech database. A perfect classification accuracy is obtained. A s ystematic comparative study of the performance gains among the classifiers and the variants of each particular classifier is undertaken. Furthermore, the databases are assessed concerning gender classification accuracy. Results advance the state-of-the-art.
What Information Do Firms Hide in Confidential SEC Filings

Publicly traded companies in the United States can request confidential treatment from the Securities and Exchange Commission (SEC) to withhold information from investors that management asserts would cause competitive harm if publicly disclosed. This study examines the nature and materiality of the information granted confidential treatment by the SEC. Using data obtained under the Freedom of Information Act, we find that redacted material contracts withhold net favorable versus unfavorable news in approximately equal proportions. Redacted favorable versus unfavorable information is predictably associated with insider purchases and some types of unfavorable redactions are associated with significantly negative long-window stock returns. These findings suggest that at least some information the SEC approves for confidential treatment is material in the aggregate. However, combining net favorable and unfavorable material supply chain contracts reflects “no news” on average. Thus, diversified investors are unlikely to be impacted by managers’ decisions to redact information.
An ontology-based topic evaluation method for enhancing information filtering

Topic Modelling has been applied in many successful applications in data mining, text mining, machine learning and information filtering. The limitation is that the quality of topics generated from the input corpus is not always good because many topics contain intrusive and ambiguous words. Hence, topic evaluation to assess and to rank the topics is really important for good quality topics before applying those topics to text based applications. In this study, we proposed an ontology-based topic evaluation method for enhancing information filtering, named as TRbTCM. This new model assesses the quality of topics based on matching topic models with subject headings in Library Congress Subject Heading (LCSH) ontology.
Job Shifting Prediction and Analysis Using Machine Learning

In today’s volatile employmentstructure,the employees tend to shift the job in an unexpected manner. In that case the company may face issues regarding scarcity of the workforce and find problem to reemploy quickly. Thus to overcome this problem we have designed a predictive model to anticipate the chances of an employee leaving the job. In this project the train and the test datasets are taken from Analytics Vidhya site where in the algorithm used to do the prediction are Random Forest, XGBoost, CatBoost, LightGBM out of which CatBoost has performed the best and ended up giving the most accurate prediction.The datasets provided by Analytics Vidhyawere structured in nature but incomplete in observance thus to full fill that the missing values imputation procedure had to be performed and then the data was fed to the algorithm for prediction. Knowing the employees approach towards job shift prior would actually help the company to plan out the workforce efficiently.CatBoost is a gradient boosting technique on decision trees library made available as open source by Yandex.It is universally applied across a wide range of areas and to a variety of problems. Considering accuracy, robustness, usability, extensibility catboost as an upper hand over the other models.
Scheduling of a limited communication channel for optimal control

We outline a method for optimal offline scheduling of a limited bandwidth communication channel that is used for control purposes. We study LQ-control of parallel control processes and describe how the theory for periodic control can be used for defining a cost functional that measures the performances of sampled data systems in relation to desired continuous time performances. This formulation results in a complex combinatorial optimization problem which is solved by exhaustive search and the solution can be viewed as an optimal allocation of the limited communication resources. The resulting optimal sequences are typically such that the sampling is nonuniform, but the control law is time varying and computed to take this nonuniform sampling into account. In an example, two inverted pendulums with different closed loop performances that share a common communication resource are studied. The resulting optimal sequences are in agreement with the intuitive idea that the sampling resources should be focused to where they are needed the most, that is, to the controller with the fastest closed loop characteristics.
A new estimation algorithm for AR signals measured in noise

Albeit several least-squares (LS) based methods have been developed for noisy autoregressive (AR) signal identification, none is "in closed form", in that an iterative procedure is needed for estimating the AR parameters and the measurement noise variance alternately. A new formulation with respect to the measurement noise variance is presented, leading to the development of a new estimation algorithm for noisy AR signals. In addition to the eminent algorithmic difference from its predecessors, the developed algorithm achieves a better estimation accuracy while requiring an almost identical amount of computation.
Bayesian model averaging employing fixed and flexible priors: The BMS package for R

This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian model averaging for linear regression models. The package excels in allowing for a variety of prior structures, among them the "binomial-beta" prior on the model space and the so-called "hyper-g" specifications for Zellner's g prior. Furthermore, the BMS package allows the user to specify her own model priors and offers a possibility of subjective inference by setting "prior inclusion probabilities" according to the researcher's beliefs. Furthermore, graphical analysis of results is provided by numerous built-in plot functions of posterior densities, predictive densities and graphical illustrations to compare results under different prior settings. Finally, the package provides full enumeration of the model space for small scale problems as well as two efficient MCMC (Markov chain Monte Carlo) samplers that sort through the model space when the number of potential covariates is large.
Méthodes MCMC pour la Décomposition en Paquets d'Ondelettes de Signaux Transitoires

Wavelet packets provide a natural framework to answer the problem of finding a “best representation" of stochastic signals. In this paper, we present a Bayesian approach to this problem when dealing with noisy transient signals by considering the basis itself as a model parameter. Prior non-homogeneous models for the underlying signal and noise coefficients are introduced and make it compelling to resort to recently introduced reversible jump Markov chain Monte Carlo algorithms. Various nonlinear signal estimates, which may depend on several representations, are subsequently obtained, and presented through several simulations.
How Informative Are Fraud and Non-Fraud Firms' Earnings?

The quality of earnings affects information asymmetry and so, the optimal functioning of the capital markets (e.g., Bhattacharya et al., 2003). Givoly et al. (2010) evaluate earnings management at private and public companies and find that managers of public companies manage earnings more than their private company counterparts, affirming the importance of high earnings quality for publicly traded companies. Higher quality of earnings improves financial statement comparability by reducing the cost of acquiring information as measured by analysts’ forecast errors and analysts’ following (De Franco et al., 2011). The importance of the quality of earnings is accentuated by the fact that even sophisticated market participants like analysts have been documented to consistently misjudge the quality of earnings. For example, analysts have been found to exhibit an optimistic bias in their earnings forecasts. That is, actual earnings minus analysts’ forecasted earnings tends to be negative (Richardson et al., 2003), and misperceive the properties of accruals (Sloan, 1996).
Application-Aware NoC Design for Efficient

In systems-on-chip (SoCs), a microprocessor de- mands guaranteed synchronous dynamic random access memory (SDRAM) latency whereas most of the other cores are served as a best-effort packet. However, a priority service for the guaranteed latency causes the SDRAM utilization and latency of an overall system to be degraded critically. In addition, the data size of SDRAM requested by various cores is not matched with an SDRAM access granularity such that the SDRAM utilization and latency are further deteriorated. In this paper, we propose an application-aware networks-on-chip (NoCs) design for an efficient SDRAM access, which can consider memory latency demands and memory access granularities in various applications. In order to provide short latency for priority memory requests with few penalties, memory request packets are scheduled by our guaranteed SDRAM service router that includes a hybrid flow controller of priority-first and priority-equal algorithms. In addition, our SDRAM access granularity matching NoC design further improves the memory performance by splitting a memory request packet to several short memory request packets and then controlling the short memory request packets with a partially open-page mode and an auto-precharge operation in a memory subsystem. Experimental results show that our cost-effective application-aware NoC design significantly improves, on average, memory latency for latency-sensitive cores up to 32.8%, overall memory latency up to 7.8%, and memory utilization up to 3.4%, compared to the state-of-the-art SDRAM-aware NoC design (4). Index Terms—Access granularity, flow control, memory, networks-on-chip, quality-of-service, router.
APA Style Referencing: A Web-Based Module

This web-based instructional module on APA style referencing allowed students easy access to learning a skill needed for academic success. It took into account multimedia learning theories and known problems such as cognitive overload, working memory capacity, modality principle, and individual differences. Individual differences in prior knowledge, content preference, and working memory capacity were considered during the design of the module which was created using an instructional design model. Findings from the study show that APA instruction via hypermedia was effective in increasing posttest scores from pretest scores. Also, survey results showed students found the module engaging, clear, and relevant. Research findings from this project will be useful to the academic community in selecting instructional design strategies, instructional design models, and types of media when creating instruction so that it will be more effective for learners.
Extreme multi-label learning : A large scale classification approach in machine learning

Abstract In digital world, the amount of data is growing exponentially in day to day life. It is difficult to analyze and extract knowledge from large amount of data with millions of categories in Big Data environment. Therefore, it is challenging problem to develop model that classify large volume of documents available on Internet. However, Multi-Label Classification approach is used to classify data with multiple categories or labels but it is inefficient way to deal with millions of categories. Hence Extreme Multi-Label Classification approach is used to overcome this limitation by selecting subset of labels for the new instance from millions of labels. Recently Extreme Multi-Label Classification has attracted research attention in different application areas like document categorization in Wikipedia, people identification in social networking, gene prediction in bio-informatics etc. Extreme Multi-Label Classification is also opened up new challenge to reformulate existing machine learning problems like ranking, tagging and recommendation. This survey paper focuses on approaches and reviewing current research challenges on eXtreme Multi Label Classification. Also discussed state-of-the-art algorithms to handle eXtreme Multi-Label Classification Problem.
Theory and applications of power system blockset, a MATLAB/Simulink-based simulation tool for power systems

This paper presents a unified framework for power system simulation using a Simulink environment. The modeling and simulation of power systems under Simulink are described and discussed. Application examples for power systems including power networks, power electronics and electric machines are presented and compared with two popular simulation packages (EMTP, PSpice).
Automatic Construction of Financial Semantic Orientation Lexicon from Large-Scale Chinese News Corpus

Sentiment analysis is a popular research topic in computational linguistics, and its applications in social science and business have attracted much attention. Semantic orientation lexicon of positive and negative words is indispensable for sentiment analysis. However, many lexicons are manually created by a small number of human subjects, which are susceptible to high cost and bias. In this paper, we propose a novel idea to construct a financial semantic orientation lexicon from large-scale Chinese news corpus automatically. Because stock price movements reflect human collective judgments on news, we rely on the stock market to label news, expand seed words, and optimize lexicon. Specifically, without invoking human annotations, we classify stock news into positive or negative according to the direction of returns. Based on the labeled data, we learn the important features as seed words, which are expanded by a linguistic model that assigns a sentiment score to each candidate term. Then, the economic significance of lexicon terms is considered to select good candidates. Finally, we iteratively optimize the lexicon to achieve a wide return dispersion. In the test, we examine the performance of our lexicon on outof-sample datasets and obtain high accuracy for identifying sentiment from news. Our research has shown the efficacy of relying the market to construct financial semantic orientation lexicon automatically. Essentially, our baseline methodology is language-independent, and can be easily extended to foreign languages.
Building and Validation of 3D Carbohydrate Structures for Simulation

Molecular modelling can provide answers to important questions of chemical structure and interaction, which can place crucial roles in disease. However, molecular modelling software is often hard to use. One of the key reasons of this is the molecular structure. In the absence of an experimentally-determined structure for a molecule, it is very difficult to build a reasonable structure from which to start simulations. Carbohydrates as a class of molecule are particularly difficult, because very few structures of these flexible molecules have been solved. Our CARBLOALDER application aims to combine the current approaches to this problem of carbohydrate structure building into one useful tool to make the building and validation of carbohydrates easier for both experts and novices. The package consists of three main components: the Builder, Validator and a web interface. The role of the Builder part of the application is converting a 2D primary structure description of carbohydrate into a valid 3D structure. In addition to supporting the building of both simple and branched 3D structures (functionality supported by most current carbohydrate building tools), the Builder application described in this report also allows for input of repeated 2D sequence and derived carbohydrate molecules, which no other building applications currently supports. It also allows the user to fine tune the orientation of the 3D structure. The Builder component was validated by analysing the deviation between the produced model and the simulated model. In addition, visual correctness and usability were also tested by biochemistry experts. Our results show that the application produces valid 3D output for both simple and complex 2D primary structures.