Miracle-dz/UniFAR-FTUs huggingface dataset:
“ INFORMATION LITERACY ” PUSTAKAWAN (Studi Deskriptif Tentang “ Information Literacy “ Pustakawan Perpustakaan Perguruan Tinggi Negeri Di Surabaya Menurut Model SCONUL The Seven Pillars of Information Literacy )

Penelitian ini membahas mengenai information literacy pustakawan perguruan tinggi negeri di Surabaya. Information literacy dalam penelitian ini mengambil dari model SCONUL the seven pillars of information literacy. Dalam SCONUL membahas tujuh kemampuan yakni kemampuan dalam merekognisi kebutuhan informasi, kemampuan membedakan cara mengatasi kesenjangan, kemampuan membangun strategi untuk menentukan lokasi informasi, kemampuan menentukan lokasi dan akses informasi, kemampuan mengkombi nasikan dan mengevaluasi informasi dari berbagai sumber, kemampuan menampilkan dan mengkomunikasikan informasi, kemampuan menciptakan pengetahuan baru. Selain mengetahui kemampuan pustakawan, penelitian ini juga membahas faktor –faktor yang mempengaruhi information literacy. Penelitian ini menggunakan pendekatan kuantitatif dengan tipe deskriptif. Pengambilan datanya menggunakan metode sensus. Pengambilan sampel sebanyak 61 pustakawan perpustakaan perguruan tinggi negeri meliputi Unair, ITS dan Unesa. Analisisnya menggunakan tabel frekuensi dengan menggunakan SPSS 16. Dan diperkuat dengan adanya probing dan teori yang mendukung. Hasil analisis menunjukan kemampuan pustakawan dalam hal merekognisi kebutuhan informasi cukup mampu khusunya dalam mengidentifik asi informasi dari berbagai sumber. Sedangkan kemampuan membedakan cara mengatasi kesenjangan yakni dengan menentukan sumber informasi yang relevan baik tercetak maupun elektonik. Sebagian besar pustakawan memilih sumber informasi elektronik seperti internet. Dalam hal menentukan lokasi dan akses informasi pustakawan kurang menguasainya seperti menggunakan teknik penelusuran boolean logic, serta menggunakan bookmark, untuk penggunaan email dan browsing ke internet pustakawan mampu, sedangkan untuk kemampuan menampilkan dan mengkomunikasikan informasi sebagaian ada yang mampu dan ada yang kurang mampu. Sedangkan kemampuan menciptakan pengetahuan baru rata -rata pustakawan kurang menguasainya. Di balik ketidak mampuan pustakawan ada beberapa factor yang mempengaruhinya antara lain kurang menguasai bahasa inggris.
The Prognostic Value of Serum Levels of Heart-Type Fatty Acid Binding Protein and High Sensitivity C-Reactive Protein in Patients With Increased Levels of Amino-Terminal Pro-B Type Natriuretic Peptide

Background Amino-terminal pro-B type natriuretic peptide (NT-proBNP) is a well-established prognostic factor in heart failure (HF). However, numerous causes may lead to elevations in NT-proBNP, and thus, an increased NT-proBNP level alone is not sufficient to predict outcome. The aim of this study was to evaluate the utility of two acute response markers, high sensitivity C-reactive protein (hsCRP) and heart-type fatty acid binding protein (H-FABP), in patients with an increased NT-proBNP level. Methods The 278 patients were classified into three groups by etiology: 1) acute coronary syndrome (ACS) (n=62), 2) non-ACS cardiac disease (n=156), and 3) infectious disease (n=60). Survival was determined on day 1, 7, 14, 21, 28, 60, 90, 120, and 150 after enrollment. Results H-FABP (P<0.001), NT-proBNP (P=0.006), hsCRP (P<0.001) levels, and survival (P<0.001) were significantly different in the three disease groups. Patients were divided into three classes by using receiver operating characteristic curves for NT-proBNP, H-FABP, and hsCRP. Patients with elevated NT-proBNP (≥3,856 pg/mL) and H-FABP (≥8.8 ng/mL) levels were associated with higher hazard ratio for mortality (5.15 in NT-proBNP and 3.25 in H-FABP). Area under the receiver operating characteristic curve analysis showed H-FABP was a better predictor of 60-day mortality than NT-proBNP. Conclusions The combined measurement of H-FABP with NT-proBNP provides a highly reliable means of short-term mortality prediction for patients hospitalized for ACS, non-ACS cardiac disease, or infectious disease.
Managing Downside Risk

Managing Downside Risk, edited by Frank Sortino and Stephen Satchell, 2001, Oxford, UK: Butterworth-Heinemann, Quantitative Finance Series, 267 pages The main topic of this volume, covering both theoretical and practical profiles, is of great interest: downside, "asymmetric" risk in financial markets. This is because of the implications that these predominant new modeling paradigms have in terms of financial risk identification, measurement, and management. The book is divided into two separate sections: part 1 ("Applications of Downside Risk") and part 2 ("Underlying Theory"). The comprehensive (although heterogeneous) series of contributions-from professionals, practitioners, and academics-on the concept of downside risk offers a survey of the ways that such risk can be defined and dealt with, based on a half century of theoretical developments. Frank Sortino, one of the editors of the book, authors the first chapter ("From Alpha to Omega"), which is mostly devoted to providing a historical excursus of the debate leading to the "schism" between the mean-variance framework-whose definitive importance is firmly acknowledged-and competing approaches. Asymmetry of uncertainty is emphasized and minimum acceptable return (MAR) is treated in the chapter, together with the importance of the stochastic dominance rules. This is in order to reveal some of the faults coming from basing decisions only on the first and second moments of the probability distributions, as in the modern portfolio theory (MFT). Sortino's contribution serves as a useful introduction to the book, for it anticipates some of the matters treated by the following authors, and some of their conclusions. We first mention Hal Forsey (chapter 4, "The Mathematician View: Modelling Uncertainty with the Three Parameter Lognormal"), who is the author, with Sortino, of the software that comes with the book (the "Forsey-Sortino model"). Forsey describes the Pension Research Institute model designed to estimate downside risk by providing some measurements based on the MAR. Joseph Messina should be mentioned as well (chapter 6, "An Evaluation of Value at Risk and the Information Ratio (for Investors Concerned with Downside Risk")), for his discussion of the adequacy and usefulness of VAR and IR measures, when investor goals are defined in terms of MAR. Other contributions included in the second part of the book are closely linked to Sortino's paper, e.g., the one by Leslie Balzer (chapter 8, "Investment Risk: A Unified Approach to Upside and Downside Returns"), a magnificent synopsis of criticalities concerned with traditional (commonly used) and innovative risk measures, or the one by Auke Plantinga and Sebastian de Groot (chapter 10, "Preference Functions and Risk Adjusted Performance Measures"), evaluating R-adjusted performance measures and preference functions, as alternative approaches to rank investment opportunities for individuals. …
Exploiting Detachability : A Non-Silicon Approach to Polymorphism

General purpose processing elements are designed to attain high overall performance across a wide range of applications with different workload characteristics. This greatly compromises the performance of the execution of individual applications. Moreover, with the commercial advent of chip-multiprocessors, the designspace of processing elements has greatly increased, rendering the performance of fixed architectures in the execution of arbitrary workloads more suboptimal than ever. The gist of this paper is the argument that the performance enhancement attainable through employing processing elements that are custom-designed for different workloads is greater than that ever attainable through conventional forms of implementing architectural polymorphism. Thus the full potential of customization will only be attainable if it were made possible to physically remove the processing element actively employed in the computer system and replace it with one that is customized to the workload characteristics of the application at hand. Through exploitation of the physical detachability of processing elements the architectural diversity of available configurations can scale unboundedly without degrading chip yield or the performance of individual configurations. These are critical features that are absent in previously proposed approaches to customization. We qualitatively argue that through this approach, a significant form of performance enhancement is achievable that will otherwise remain untapped.
Postictal behavioural impairments are due to a severe prolonged hypoperfusion/hypoxia event that is COX-2 dependent

Seizures are often followed by sensory, cognitive or motor impairments during the postictal phase that show striking similarity to transient hypoxic/ischemic attacks. Here we show that seizures result in a severe hypoxic attack confined to the postictal period. We measured brain oxygenation in localized areas from freely-moving rodents and discovered a severe hypoxic event (pO2 < 10 mmHg) after the termination of seizures. This event lasted over an hour, is mediated by hypoperfusion, generalizes to people with epilepsy, and is attenuated by inhibiting cyclooxygenase-2 or L-type calcium channels. Using inhibitors of these targets we separated the seizure from the resulting severe hypoxia and show that structure specific postictal memory and behavioral impairments are the consequence of this severe hypoperfusion/hypoxic event. Thus, epilepsy is much more than a disease hallmarked by seizures, since the occurrence of postictal hypoperfusion/hypoxia results in a separate set of neurological consequences that are currently not being treated and are preventable. DOI: http://dx.doi. org/10.7554/eLife.19352. 001
Digital Forensic Model Based On Malaysian Investigation Process

Summary With the proliferation of the digital crime around the world, numerous digital forensic investigation models already being develop . In fact many of the digital forensic investigation model focus on technical implementation of the investigation process as most of it develop by traditional forensic expert and technologist. As an outcome of this problem most of the digital forensic practitioners focus on technical aspect and forget the core concept of digital forensic investigation model . In this paper we are introducing a new digital forensic model which will be capture a full scope of an investigation process based on Malaysia Cyber Law . The proposed model is also compared with the existing model which currently available and being apply in the investigation process.
An Empirical Investigation of the Sensitivity of Inductive Decision Trees

Data mining (DM) techniques have gained a lot of attention lately as they may provide decision-makers with institutional knowledge required for making strategic decisions. Such techniques are able to handle and extract useful information from large data sets. Many commercial tools are now available for the novices to facilitate the data mining process. However, treating these tools as a black box can lead to misleading and unpredictable results. Even when comparing among tools that are based on the same data mining technique, results may vary according to the implementation process and may be affected by the user selection of parameters prior to the mining phase. In this paper, we present an empirical comparison among some of the tools that generate decision trees, and illustrate the difference in their results using the case of loan application processing.
Modeling of Artificial Neural Network for the Prediction of the Multi-Joint Stiffness in Dynamic Condition

Unlike robotic systems, humans excel in various tasks by taking advantage of their intrinsic compliance, force sensation, and tactile contact clues. By examining human strategy in arm impedance control, we may be able to teach robotic manipulators human's superior motor skills in contact tasks. This paper develops a novel method for estimating and predicting the human joint impedance using the electromyogram (EMG) signals and limb position measurements. An artificial neural network (ANN) model was developed to relate the EMG and joint motion to joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. Experimental and simulation results confirmed the feasibility of the developed ANN model.
Convolutional neural network with gradient information for image super-resolution

Image super-resolution (SR) can help people obtain a high-resolution (HR) image through a series of low-resolution (LR) images. Deep convolutional neural network (CNN) is one of deep learning methods, and has been applied in the field of machine vision successfully. Recently, people developed a CNN model, which learns an end-to-end mapping between LR/HR images directly. However, this method only considers the local gray information of images in the training step, and we find that the experimental results produce local edge blurring. In this paper, we consider a combination of local gray information and local gradient information to reduce the local edge blurring. We calculate the 1st (first) and 2nd (second) order for each pixel to represent image edge information by gradient algorithm. Then, we combine our training samples and the gradient information together to train the SRCNN model. Finally, the experimental result shows that our method is effective by comparing with the state-of-the-art SR methods.
Centre for EMEA Banking, Finance and Economics Working paper series Structural Breaks and Convergence in the European Retail Banking Sector

The aim of this paper is to investigate the convergence process in the European retail banking sector by analysing monthly deposit and lending data sets for the household and non-financial corporations sectors, the two sectors of retail banking, for the period 1991 to 2008. One of the main contributions of this paper is the application of the stochastic multiple structural break model developed by Bai and Perron (1998). This methodology is chosen in order to verify whether the interest rate data have been subject to structural change and whether the timings in the break dates coincide with significant events in the history of European banking. The second contribution of this paper is to test for convergence in the interest rate data by employing the Pesaran (2007) panel unit root tests while allowing for structural breaks. The findings show that the retail interest rate data have between two to four breaks which tend to be clustered around specific events such as new EU legislation. In addition, it is revealed that the presence of structural breaks materially affect the convergence results.
Learning and Visualizing User Preferences Over Sets

In previous work, we introduced a representation language, DD - PREF , that balances preferences for particular items with preferences about the properties of the set. Speciﬁcally, DD - PREF permits the expression of preferences for depth (i.e., preferences for speciﬁc attribute values over others) and diversity of sets (i.e., preferences for broad vs. narrow distributions of attribute values). We have also shown how preferences represented in DD - PREF can be learned from training data. In this paper, we present a new experimental methodology, and give results in a Mars rover image domain. We also provide new visualizations of the learned preferences in this domain. Finally, we describe a Chinese restaurant menu domain for which we are currently gathering user data. 1
Can a Mobile Game Teach Computer Users to Thwart Phishing Attacks?

Phishing is an online fraudulent technique, which aims to steal sensitive information such as usernames, passwords and online banking details from its victims. To prevent this, anti-phishing education needs to be considered. This research focuses on examining the effectiveness of mobile game based learning compared to traditional online learning to thwart phishing threats. Therefore, a mobile game prototype was developed based on the design introduced by Arachchilage and Cole [3]. The game design aimed to enhance avoidance behaviour through motivation to thwart phishing threats. A website developed by Anti-Phishing Work Group (APWG) for the public Anti-phishing education initiative was used as a traditional web based learning source. A think-aloud experiment along with a pre- and post-test was conducted through a user study. The study findings revealed that the participants who played the mobile game were better able to identify fraudulent web sites compared to the participants who read the website without any training.
On-line Fault diagnosis of Arbitrary Connected Networks

This paper proposes an on-line two phase fault diagnosis algorithm for arbitrary connected networks. The algorithm addresses a realistic fault model considering crash and value faults in the nodes. Fault diagnosis is achieved by comparing the heartbeat message generated by neighboring nodes and dissemination of decision made at each node. Theoretical analysis shows that time and message complexity of the diagnosis scheme is O(n) for a n-node network. The message and time complexity are comparable to the existing state of art approaches and thus well suited for design of different fault tolerant wireless communication networks.
SHOWFACE MPEG-4 COMPATIBLE FACE ANIMATION FRAMEWORK

This paper proposes ShowFace, a streaming structure for personalized face animation. This structure extends (and complies with) MPEG-4 face animation by defining image transformations required for certain facial movements, and also by providing a higher level Face Modeling Language (FML) for modeling and control purposes. The proposed framework consists of components for parsing the input script, generating and splitting the audio and video “behaviors”, creating the required images and sounds, and eventually displaying or writing the data to files. This component-based design and scripted behavior also make the framework suitable for web-based applications.
A Robust Multi-Camera Approach to Object Tracking and Position Determination using a Self-Organising Map Initialised through Cross-Ratio Generated Virtual Points

This paper presents a new method for the tracking of an object, and the determination of its absolute position using the image coordinates provided from multiple cameras. Existing methods used in multi-camera tracking systems require the calibration of all camera parameters, with re-calibration needed when cameras are moved. The proposed method obtains the image coordinates of an object at known locations and generates "virtual points" that are used as initial codebook vectors in a Self-Organising Map. This is achieved through a novel use of the cross-ratio invariance. The use of the Self-Organising Map also allows for camera movement, addition and removal without the need for re-calibration. Experimental results are presented demonstrating the tracking capability of the system.
Mapping nodal power injections to branch flows in connected LTI electrical networks

This paper presents analytical closed-form expressions that map the contributions of nodal active- and reactive-power injections to the branch active- and reactive-power flows in an AC electrical network that is operating in sinusoidal steady state. We term these as the power divider laws, since they are derived leveraging, and their form and functionality are similar to, the ubiquitous current divider law. Distinct from the current divider law that only depends on the topology and constitution of the electrical network, the power divider laws are a function of the topology as well as the sinusoidal-steady-state voltage profile of the network.
The surface morphology and bonding properties of free standing PMMA/TiO2 nanocomposite films

PMMA/TiO2 nanocomposite films in free standing form were prepared at different amount of TiO2 nanoparticles by using sonication method. The PMMA powder and TiO2 nanopowder were dissolved into the toluene solution and sonicated for 1 h to confirm the homogeneity of the mixture. The FESEM micrographs reveal that PMMA/TiO2 nanocomposite contain 4 wt% TiO2 nanoparticles can produced the homogenous mixture with only little crack observed in the sample compared to other samples. Even the agglomerations were observed in other samples, all the TiO2 nanoparticles were fully dispersed in the PMMA matrix. The EDX results reveal the existence of the titanium and oxygen element in the nanocomposite films and FTIR results reveal the bonding properties between these two materials. Raman spectra of the PMMA/TiO2 nanocomposite films also were discussed in this paper.
Regulation of Neurite Growth by Inorganic Pyrophosphatase 1 via JNK Dephosphorylation

Neural cell differentiation during development is controlled by multiple signaling pathways, in which protein phosphorylation and dephosphorylation play an important role. In this study, we examined the role of pyrophosphatase1 (PPA1) in neuronal differentiation using the loss and gain of function analysis. Neuronal differentiation induced by external factors was studied using a mouse neuroblastoma cell line (N1E115). The neuronal like differentiation in N1E115 cells was determined by morphological analysis based on neurite growth length. In order to analyze the loss of the PPA1 function in N1E115, si-RNA specifically targeting PPA1 was generated. To study the effect of PPA1 overexpression, an adenoviral gene vector containing the PPA1 gene was utilized to infect N1E115 cells. To address the need for pyrophosphatase activity in PPA1, D117A PPA1, which has inactive pyrophosphatase, was overexpressed in N1E115 cells. We used valproic acid (VPA) as a neuronal differentiator to examine the effect of PPA1 in actively differentiated N1E115 cells. Si-PPA1 treatment reduced the PPA1 protein level and led to enhanced neurite growth in N1E115 cells. In contrast, PPA1 overexpression suppressed neurite growth in N1E115 cells treated with VPA, whereas this effect was abolished in D117A PPA1. PPA1 knockdown enhanced the JNK phosphorylation level, and PPA1 overexpression suppressed it in N1E115 cells. It seems that recombinant PPA1 can dephosphorylate JNK while no alteration of JNK phosphorylation level was seen after treatment with recombinant PPA1 D117A. Enhanced neurite growth by PPA1 knockdown was also observed in rat cortical neurons. Thus, PPA1 may play a role in neuronal differentiation via JNK dephosphorylation.
LO BAL CHANGE Interactive Chemistry and Climate Models in Global Change Studies

Continually increasing atmospheric concentrations of radiatively important chemical species such as CO2, CH4, N2O, tropospheric O3, and certain halocarbons most likely will cause future climate changes, which could in turn impact chemical reaction rates and thus lifetimes of many important chemical species. Complicated interactions between climate dynamics and atmospheric chemistry strongly suggest that a fully interactive, comprehensive chemistry-climate modeling system is needed to study the issue. This article reviews recent work in the new and challenging field of interactive chemistry-climate modeling, describing major efforts in model development and summarizing in detail applications of and results from these models.