Miracle-dz/UniFAR-FTUs huggingface dataset:
A Conceptual Framework for the Adoption of E-Government in Indonesia

The rapid development of e-government across the world has opened the discussion on how governments can improve citizens’ adoption of their online public services. As a result, the adoption of e-government has become a fundamental issue that needs to be adequately addressed. This paper presents a conceptual framework for examining the adoption of e-government from the perspective of citizens. Through semi-structured interviews conducted in Indonesia, the conceptual framework was validated using thematic analysis, which indicates that performance expectancy, effort expectancy and social influence are interrelated critical factors for the adoption of e-government. In terms of academic contribution, this research provides a better understanding of the critical factors for the adoption of e-government from the perspective of citizens. With regards to practical implications, this research provides the Indonesian government and public organisations with relevant suggestions on how the adoption of e-government can be improved.
Supplementary material for Dirichlet Simplex Nest and Geometric Inference

Notation Let λmax(A) and λmin(A) denote the largest and smallest non-zero singular values of the matrix A. We use f(·) to denote the density of Q with respect to Lebesgue measure on the K − 1 dimensional subspace containing the simplex B. Let g(·) be the density of P with respect to the Lebesgue measure on the K − 1 dimensional space containing the eigenvectors of Σtot, where Σ K tot is best K − 1-rank approximation matrix of Σtot := BSB + 0ID and 0ID is a uniform upper bound on Cov[xi | θ]. Let Σ be the population covariance matrix with Σ as the best K − 1 rank approximation. Note that
Remote sensing supporting national forest assessments

This chapter concerns the integration of remote sensing data in national forest inventories. It provides a basic explanation of how remote sensing can be integrated into assessments and highlights key aspects. It presents an overview of different remote sensing systems and data types, including advantages and disadvantages, as well as future developments. Remote sensing data are a longstanding element of forest inventories. The forestry sector was the first, after the military, to understand the potential of remote sensing data in support of inventory tasks. The definition of remote sensing, here, includes all airborne and space-borne instruments for Earth observation, from analogue aerial photography to space-borne digital instruments such as synthetic aperture radar (SAR) and opto-electronic systems. Not included in this definition are satellitepositioning or navigation systems, and terrestrial remote sensing systems such as terrestrial photogrammetry or terrestrial laser scanning. Navigation or positioning systems, as well as terrestrial remote sensing systems, are of increasing importance for sampling and sample-based field measurements, and should not be neglected in discussion of remote sensing for NFAs. However, this is not what most people refer to when talking about remote sensing, and it will not be included in this chapter.
Hate Speech Detection is Not as Easy as You May Think: A Closer Look at Model Validation

Hate speech is an important problem that is seriously affecting the dynamics and usefulness of online social communities. Large scale social platforms are currently investing important resources into automatically detecting and classifying hateful content, without much success. On the other hand, the results reported by state-of-the-art systems indicate that supervised approaches achieve almost perfect performance but only within specific datasets. In this work, we analyze this apparent contradiction between existing literature and actual applications. We study closely the experimental methodology used in prior work and their generalizability to other datasets. Our findings evidence methodological issues, as well as an important dataset bias. As a consequence, performance claims of the current state-of-the-art have become significantly overestimated. The problems that we have found are mostly related to data overfitting and sampling issues. We discuss the implications for current research and re-conduct experiments to give a more accurate picture of the current state-of-the art methods.
Arbitrary Lagrangian-Eulerian Finite Element Formulations Applied to Geotechnical Problems

The Arbitrary Lagrangian Eulerian (ALE) method is an explicit numerical formulation which has become a standard tool to solve large deformation problems in solid mechanics. In this study, a strip footing problem has been modeled to evaluate the competences of ALE in the context of geotechnical engineering. This evaluation is done by applying ALE into a previously analytically solved problem. Moreover, ALE has been investigated in two commercial codes: Abaqus and LS-DYNA. This includes a detailed comparison regarding the ALE remapping method, mesh size optimization and sensitivity analysis, time step size, computation time, accuracy, and stability of results. Results from ALE solution showed a good accuracy of both codes compared with analytical solution. In addition, it was observed that automatic time step determination of the codes is accurate and by decreasing the time step size no significant improvement is observed. ALE approved its efficiency in solving large deformation geotechnical problems.
WAVE: A Decentralized Authorization Framework with Transitive Delegation

Most deployed authorization systems rely on a central trusted service whose compromise can lead to the breach of millions of user accounts and permissions. We present WAVE, an authorization framework offering decentralized trust: no central services can modify or see permissions and any participant can delegate a portion of their permissions autonomously. To achieve this goal, WAVE adopts an expressive authorization model, enforces it cryptographically, protects permissions via a novel encryption protocol while enabling discovery of permissions, and stores them in an untrusted scalable storage solution. WAVE provides competitive performance to traditional authorization systems relying on central trust. It is an open-source artifact and has been used for two years for controlling 800 IoT devices.
Yugong: Geo-Distributed Data and Job Placement at Scale

Companies like Alibaba operate tens of data centers (DCs) across geographically distributed locations. These DCs collectively provide the storage space and computing power for the company, storing EBs of data and serving millions of batch analytics jobs every day. In Alibaba, as our businesses grow, there are more and more cross-DC dependencies caused by jobs reading data from remote DCs. Consequently, the precious wide area network bandwidth becomes a major bottleneck for operating geo-distributed DCs at scale. In this paper, we present Yugong — a system that manages data placement and job placement in Alibaba’s geo-distributed DCs, with the objective to minimize cross-DC bandwidth usage. Yugong uses three methods, namely project placement, table replication, and job outsourcing, to address the issues of high bandwidth consumption across the DCs. We give the details of Yugong’s de-sign and implementation for the three methods, and describe how it cooperates with other systems (e.g., Alibaba’s big data analytics platform and cluster scheduler) to improve the productivity of the DCs. We also report comprehensive performance evaluation re-sults, which validate the design of Yugong and show that signiﬁcant reduction in cross-DC bandwidth usage has been achieved.
KEPLER: Facilitating Control-flow Hijacking Primitive Evaluation for Linux Kernel Vulnerabilities

Automatic exploit generation is a challenging problem. A challenging part of the task is to connect an identified exploitable state (exploit primitive) to trigger execution of codereuse (e.g., ROP) payload. A control-flow hijacking primitive is one of the most common capabilities for exploitation. However, due to the challenges of widely deployed exploit mitigations, pitfalls along an exploit path, and ill-suited primitives, it is difficult to even manually craft an exploit with a control-flow hijacking primitive for an off-the-shelf modern Linux kernel. We propose KEPLER to facilitate exploit generation by automatically generating a “single-shot” exploitation chain. KEPLER accepts as input a control-flow hijacking primitive and bootstraps any kernel ROP payload by symbolically stitching an exploit chain taking advantage of prevalent kernel coding style and corresponding gadgets. Comparisons with previous automatic exploit generation techniques and previous kernel exploit techniques show KEPLER effectively facilitates evaluation of control-flow hijacking primitives in the Linux kernel.
Layers and the Probability of False Prediction ⋆

In this paper, we consider the L∞ criterion of best fit for identifying linear regression mode ls from data. This criterion has much appeal as it protects agai nst the worst since it selects the regression model that minimizes the largest deviation from observatio ns. Moreover, based on the obtained optimal loss, one can build prediction intervals, as opposed to sing le prediction values, which are guaranteed to contain newly generated output values with high probabilit y. Our main result is the exact quantification of the probability of making a false prediction valid in cond itions of independent and stationary observations, a result that provides an exhaustive charact erization of the confidence of prediction based onL∞ regression models. It turns out that this false prediction p robability is independent of the mechanism through which observations are generated. This r esult bears implications on the possibility of obtaining general and non-conservative evaluations for the probability of false prediction with no a-priori knowledge of the data generation mechanism proper ties.
A SIMPLE METHOD FOR 3 D SHAPE RECONSTRUCTION

To infer 3D descriptors of the scene one can use image(s) captured from camera(s). With respect to the illumination of the scene there are passive and active approaches. The later ones are particularly suitable for objects with no texture. The paper represents 3D reconstruction system with commercially available pair of cameras and video projector, as source of active illumination. Furthermore, algorithms involved are simple and easily implemented. Practical reconstruction results along with systems strengths and weaknesses are presented as well.
Business Lead Generation for Online Real Estate Services : A Case Study

Business leads generation is a crucial and challenging task for online business to attract customers and improve their services. This paper presents a case study of an online real estate service company which provides potential home buyers useful neighborhood information, and accordingly offers them business leads to real estate companies. The company’s current business lead generation is based on a “brute-force” method which requires tedious manual efforts. We developed an automatic business lead generation system which includes data integration as well as data mining tasks of classification and association rule mining. We demonstrated through experiments that this system can empower online real estate service companies to quickly and effectively generate targeted business leads. KeywordsBusiness lead generation; Data modeling; Data integration; Data mining; Business Intelligence.
DEFIBRILLATION OF ARRHYTHMIAS OF INCREASED COMPLEXITY

Energy requirements for successful defibrillation shocks are arrhythmia specific. However, it remains unclear why probability of shock success decreases with increase in arrhythmia complexity. We hypothesize that this is due to differences in transmural postshock behavior. To test this hypothesis, we assess shock efficacy in a bidomain defibrillation model of a 4mm thick slice of canine ventricles. Shocks are applied between an RV cathode and a distant anode to terminate one of two types of arrhythmias: VT (single scroll wave) or VF (multiple scroll waves). The ED50 shock strength for VT is found to be 13% less than that for VF. The results demonstrate that an isoelectric window in the range of 34 to 43ms exists following both failed shocks and those that succeed in Type B defibrillation for both VT and VF. Further, shocks given in VT result in longer isoelectric window durations as compared to shocks in VF. Analysis of the results demonstrates that the shock induces strong virtual electrode polarization on the slice surfaces that rapidly diminishes in tissue depth. We find that transmural excitable pathways allowing wavefront propagation leading to breakthrough on the surface are formed earlier following shocks given in VF as compared to VT.
Linear Stochastic Bandits with Heavy-Tailed Payoffs

of thesis entitled: Linear Stochastic Bandits with Heavy-Tailed Payoffs Submitted by SHAO, Han for the degree of Master of Philosophy at The Chinese University of Hong Kong in March 2019 In this thesis, we center around bandit models, especially linear stochastic bandits. Bandit models can tackle numerous problems of sequential learning with feedback of instantaneous payoffs. With such an ability, bandits have been applied into many applications, such as clinical trials, online recommendations and portfolio managements. The main problem solved in this thesis is linear stochastic bandits with heavy-tailed payoffs. This problem is motivated by the wide application of linear stochastic bandits and the phenomenon of heavy-tailed distributions in various scenarios, e.g., network routing and financial markets. However, the problem has not been studied well in the previous work. In linear stochastic bandits, it is commonly assumed that payoffs are with sub-Gaussian noises. In this thesis, we study the problem of linear stochastic bandits with non-sub-Gaussian payoffs. We assume that the distributions of the payoffs have finite moments of order p, with p ∈ (1, 2]. First, we analyze the regret lower bound of Ω(T 1 p ), where T denotes the number of rounds. This provides us with two hints: one is that the prior algorithms are far from optimal and the other is that finite variances (which refer to finite moments of order 2) lead to
WP / 17 / 019 The Impact of Terrorism on Governance in African Countries Forthcoming in : World Development

This study investigates how terrorism affects governance in 53 African countries for the period 1998-2012. Four terrorism indicators are used namely: domestic, transnational, unclear and total terrorism. Ten bundled and unbundled governance indicators are also employed namely: political governance (consisting of political stability and voice and accountability), economic governance (encompassing government effectiveness and regulation quality); institutional governance (entailing corruption-control and the rule of law) and general governance. The governance indicators are bundled by means of principal component analysis. The empirical evidence is based on Generalized Method of Moments. Three key findings are established. First, all selected terrorism dynamics negatively affect political governance and its constituents. Second, evidence of a negative relationship is sparingly apparent in economic governance and its components. Third, no proof was confirmed in relation to the impact of terrorism and institutional governance with its elements. Fourth, compared with domestic terrorism, transnational terrorism more negatively and significantly affects political, economic and general governances. Policy implications are discussed. JEL Classification: C52; D74; F42; O38 ; P37
Towards mitigating conflicts of interest : An empirical study of typology of corporate governance systems including values

Conflicts of interest are still a major issue in corporate governance systems which concerned with the relationships between owners and top management. Despite the fact, there has been a considerable amount of attention towards a better solution to mitigate conflicts of interest, but the issue is still dubious due to the substantial corporate crisis are outlined. By carrying forward the previous research, typology of corporate governance systems including values, this study identified the research problem that is: “Lacking the justifiability of constructed typology of corporate governance systems including values towards mitigating conflicts of interest. ” There were two sets of research data was analysed during this study. One is Doing business data: The World Bank group and the second set of data is Schwartz’s value survey to characterise multiple countries by their culture. The result of the study comes with an empirically justified the best possible corporate governance type including values towards mitigating conflicts of interest.
CKIP at the NTCIR-14 STC-3 Task

We describe how we build the system for NTCIR-14 Short Text Conversation (STC-3) Chinese Emotional Conversation Generation (CECG) subtask. In our approach, we first build an emotion keyword list, which contains emotion vocabularies under 5 different emotion classes. After that, responses are generated through a RNN-based inference mechanism, aimed at using emotion words from previously built keyword list to express emotion. Although our system doesn’t reach top performance, we still find that there are some interesting findings in our results worth discussing.
NEURINF : Neuroinspired Informatics Special session at Cognitive 2017 , Athens

Since the initial model of the perceptron proposed in 1957, artificial neural networks have ignited great interest in the scientific community as an alternative to the classical Von Neumann’s model. Fifty five years later, Deep Neural Networks (DNNs) have become the state-of-theart method in many supervised or reinforcement learning tasks. Neuroinspired models obtain the best performance on challenges that are efficiently solved by the human brain. On the other hand, the biological plausibility of DNNs is debatable. Finding neural networks architectures that are both biologically inspired and efficient at solving challenging problems is a very active field of research, and the motivation for this special session.
N ov 2 00 6 A Contour Method on Cayley tree

We consider a finite range lattice models on Cayley tree with two basic properties: the existence of only a finite number of ground states and with Peierls type condition. We define notion of a contour for the model on the Cayley tree. By a contour argument we show the existence of s different (where s is the number of ground states) Gibbs measures.
Analysis of urine culture isolates from microbiology laboratory of a tertiary care Hospital

The study of urine culture isolates was conducted on samples received to microbiology laboratory of GMERS Medical College attached General Hospital, Gandhinagar during the period of December 2015 to November 2016. Only isolates from urine samples of non catheterized patients having colony count of >105 CFU/mL were included for analysis. Analysis of the species distribution and antibiotic sensitivity pattern was done with the help of WHONET 5.6 software. Out of 160 isolates identified from 801 urine samples (positivity rate of 19.98%), most common were coliforms (71.88%) followed by pseudomonas spp. (8.75%), candida spp. (8.75%) etc. Female patients (22.81%) had higher rate of urinary tract infection as compared to males (17.45%). Enterobacteriaceae group was most resistant among all isolates. Amikacin (85%) and imipenem (82%) were most sensitive antibiotics in Enterobacteriaceae group while polymyxin-B (100%) and aztreonam (93%) were most sensitive in pseudomonas spp. Antimicrobial susceptibility testing and analysis was done for gram positive cocci also. Enterobacteriaceae are the leading cause of urinary tract infection worldwide and also most resistant among all isolates. Cautious usage of antimicrobial agents is required to reduce the burden of increasing drug resistance in UTI. Keywords: Urinary tract infection, Antimicrobial sensitivity, Coliform, Gender difference.
A distance saving approach to the K-means problem for massive data

In spite of its dependency on the initial settings and the large number of distance computations that it may require, the K-means algorithm remains one of the most popular clustering methods. In this work, we propose an approximation to the solution of the K-means problem that controls the trade-off between the number of distances computed and the quality of the obtained solution. Our approach recursively applies a weighted version of the K-means algorithm over a local representation of the dataset: At each iteration, a thinner partition of the dataset is constructed, by dividing the subsets located at the boundaries of the clustering obtained in the previous iteration. Since all the computations are performed over a small number of representatives, this approach can drastically reduce the number of distance computations especially for problems with a massive amount of data points. Experimental results indicate that our method outperforms well-known approaches, such as the K-means++ and the minibatch K-means, in terms of the relation between number of computations and the quality of the approximation. Introduction Clustering techniques have been widely used in different areas, such as artificial intelligence and machine learning. This process determines the intrinsic grouping of an unlabeled dataset by dividing it into a predefined number of disjoint subsets, called clusters. Even when there exist several clustering methods [11], the K-means algorithm remains one of the most popular and widely studied approach [18]. Given a set of data points D = {x1, . , xn} in R and an integer K, the Kmeans problem is to determine the set of K centroids C = {c1, . , cK} in R that minimizes the squared-error distortion: E(C) = K ∑
Improved salient objects detection based on salient points

In this paper, we propose an effective framework for detecting salient regions of an image via salient points and color contrast. First, the boosting Harris is used to detect highlight points as the salient points, and the convex hull is constructed to separate the foreground regions and the background regions. Due to the fact that superpixel is perceptually more meaningful than pixel, and which can reduce the complexity of image processing. So we utilize the superpixel algorithm to reprocess original images, and compute the contrast between superpixels in the foreground regions and the background regions to get the salient map. After that, the fusion is used to improve the detection results. Finally, the proposed method is evaluated on the MRSA(Microsoft Research Asia) dataset, and can produce promising results compared with 7 state-of-the-art salient object detection approaches.
atient participation in discharge planning decisions in the frame of rimary Nursing approach : A conversation analytic study

Primary Nursing (PN) is a model of care delivery which is described to favor patient participation, as a Primary Nurse is responsible for coordinating all aspects of care including discharge planning. The purpose of this paper is to explore patient-nurse interactions in a rehabilitation clinic in which PN is used. Twentyfive interactions of video-recorded data involving 12 patients with their primary nurse were included in this paper and analyzed using conversation analysis, an inductive data-driven approach. Our findings ecision making ischarge planning ehabilitation