Miracle-dz/UniFAR-FTUs huggingface dataset:
Elliptic Curve Method Using Complex Multiplication Method

In 2017, Shirase proposed a variant of Elliptic Curve Method combined with Complex Multiplication method for generating certain special kinds of elliptic curves. His algorithm can efficiently factorize a given composite integer when it has a prime factor p of the form 4p = 1 + Dv2 for some integer v, where −D is an auxiliary input integer called a discriminant. However, there is a disadvantage that the previous method works only for restricted cases where the class polynomial associated to−D has degree atmost two. In this paper, we propose a generalization of the previous algorithm to the cases of class polynomials having arbitrary degrees, which enlarges the class of composite integers factorizable by our algorithm. We also extend the algorithm to more various cases where we have 4p = t2 + Dv2 and p + 1 − t is a smooth integer. key words: integer factorization, elliptic curve method, complex multiplication method, class polynomials
A Study of Continuance Intention to Adopt Cloud Services: The Moderating Effect of Users’ Motivation

With the advancement of mobile and other information-communication technologies, the Cloud application services have gained increasing usage in recent years. The study of adoption factors is a topic worthy of further exploration. This research investigates critical factors, affecting users’ continuance intention to use Cloud services. Our research is based on the model of continuous using information system to explore these relationships. Results were obtained from 313 valid samples, using an online survey test the research model. Empirical results from the SEM conclude that (1) innovation acceptance, perceived safety, confirmation, and perceived usefulness positively affect satisfaction; (2) confirmation positively affect perceived usefulness; (3) knowledge sharing, perceived usefulness, and satisfaction positively affect continuance intention; (4) users’ motivation has a moderating effect between satisfaction and continuance intention. Practical suggestions to the vendors of Cloud application service are provided.
Decoupled Crank–Nicolson/Adams–Bashforth scheme for the Boussinesq equations with smooth initial data

ABSTRACT Based on the mixed finite element method, we consider the decoupled Crank–Nicolson/Adams–Bashforth scheme for the Boussinesq equations with smooth initial data in this paper. The temporal treatment of the spatial discrete Boussinesq equations is based on the implicit Crank–Nicolson scheme for the linear terms and the explicit Adams–Bashforth scheme for the nonlinear terms. Thanks to the decoupled method, the considered problem is split into two subproblems and these subproblems can be solved in parallel. Under some restriction on the time step, we present the stability and convergence results of numerical solutions, Finally, some numerical experiments are provided to test the performance of the developed numerical scheme and verify the established theoretical findings.
A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

17 RESUMO . 19
A Survey of Social Network Analysis Techniques and their Applications to Socially Aware Networking

SUMMARY Socially aware networking is an emerging research ﬁeld that aims to improve the current networking technologies and realize novel network services by applying social network analysis (SNA) techniques. Conducting socially aware networking studies requires knowledge of both SNA and communication networking, but it is not easy for communication networking researchers who are unfamiliar with SNA to obtain comprehensive knowledge of SNA due to its interdisciplinary nature. This paper therefore aims to ﬁll the knowledge gap for networking researchers who are interested in socially aware networking but are not familiar with SNA. This paper surveys three types of important SNA techniques for socially aware networking: identiﬁcation of inﬂuential nodes, link prediction, and community detection. Then, this paper introduces how SNA techniques are used in socially aware networking and discusses research trends in socially aware networking.
A Conceptual Interaction Cycle Between Individual and Group Absorptive Capacity with Social Integration Mechanism and Cohesive Learning Group as Moderating Variables

Many researchers agree that absorptive capacity (ACAP) is a critical multilevel construct (individual to organization) for innovation success. Interestingly, little attention has been paid to this construct independently or the interaction among the levels in a full cycle. This conceptual paper aimed to visualize a proposed research model to fill in the theoretical and empirical gap in the multilevel interaction by detailing some promising moderators from the ACAP conceptual model, that is, cohesive learning groups and social integration mechanisms adopted from organizational learning (OL) 4I theory. In addition, this paper identified feed forward and feedback mechanisms, creating a cycle in the interaction process that had been identified but never tested. Some propositions were conceptualized to address this research line of work, and a research model was proposed.
Learning to Hire? Hiring as a Dynamic Experiential Learning Process in an Online Market for Contract Labor

Can employers learn to hire? This article conceptualizes hiring as a dynamic experiential learning process. Instead of examining hiring as a point in time decision, I investigate whether and how employers’ past hiring experiences affect their future decisions. Drawing on evidence from a global online market for contract labor, I argue that employers revise their beliefs regarding job applicants from a particular social category following a negative hiring experience from that social category. I analyze over 16 Million applications from freelancers worldwide for over 2.2 Million jobs from 557,416 employers. I find that employers who have a negative hiring experience with a freelancer from a particular country are subsequently less likely to hire other freelancers from that country. This effect is stronger on hiring for identical subsequent jobs and weaker for other jobs. Most strikingly, evidence from the actual hiring switches following a negative experience and a simulation using data from the observed distribution of freelancers on the platform demonstrate that employers unnecessarily oversteer away from countries given the narrow distribution of observable ability among freelancers. Switches do not result in hiring from a “better” country.
Evaluation of Recurrent Neural Networks for Hard Disk Drives Failure Prediction

The capability of predicting failures in Hard Disk Drives is a major objective of many data storage service providers. Such capability may avoid severe data losses, increasing the quality and reducing the costs of the service provided by such companies. Considering several recent works, Recurrent Neural Networks are shown to be the most promising methods for failure prediction on HDDs. Based on that results, in this work, we aim to evaluate the performance of several recurrent neural models in the HDDs failure prediction task. We conducted a comparative study between shallow and deep architectures and verified that deep models had the best results. Additionally, we evaluated the performance of all models when using three initialization procedures.
Traffic identification in IP networks

XII RESUMO . XIV CHAPTER 1 – INTRODUCTION . 1 1.1. MOTIVATION . 2 1.2. OBJECTIVES . 4 1.3. ORGANIZATION OF THE THESIS . 4 CHAPTER 2 – STATE OF THE ART . 6 2.1. TRAFFIC MEASUREMENT . 6 2.1.1. Active vs. Passive Measurements . 7 2.1.2. Packet-Level Passive Measurements . 8 2.1.3. Flow-Level Passive Measurements . 10 2.1. 4. Flow analysis . 13 2.1. 4.1. Classification: Volume, Duration, Rate and Burstiness . 14 2.1. 4.2. Traffic Characterization . 16 2.2. INTERNET TRAFFIC IDENTIFICATION . 18 2.2.1. Inference Quality Metrics . 19 2.2.2. Identification Baseline . 23 2.2.3. Port-Based Application Deduction. 25 2.2. 4. Payload Identification . 25 2.2. 5. Statistical Identification . 26 2.2. 6. Behavioral Identification . 27 2.2. 7. Machine Learning . 28 2.2. 8. Combination of Traffic Identification Algorithms . 29 2.3. DISCUSSION . 32 CHAPTER 3 – INTERNET TRAFFIC IDENTIFICATION CASE STUDY . 33 3.1. ACTIVE MEASUREMENT . 35 3.2. LABORATORY MEASUREMENT . 39 3.3. ACADEMIC NETWORK MEASUREMENT . 43 3.4. COMMERCIAL NETWORK MEASUREMENT . 45 3.5. UNIDIRECTIONAL ACADEMIC NETWORK EVALUATION . 47
BERKELEY CARDINALS AND THE STRUCTURE OF L(Vδ+1)

Abstract We explore the structural properties of the inner model L(Vδ+1) under the assumption that δ is a singular limit of Berkeley cardinals each of which is itself limit of extendible cardinals, lifting some of the main results of the theory of the axiom I0 to the level of Berkeley cardinals, the strongest known large cardinal axioms. Berkeley cardinals have been recently introduced in [1] and contradict the Axiom of Choice.1 In fact, our background theory will be ZF.
Setting the Bar: The Evaluative and Allocative Roles of Organizational Aspirations

This study explores the determinants of organizational aspirations, proposing that aspirations play dual roles that create important tension for managers. On one hand, aspirations serve an evaluative role as a benchmark for assessing performance. On the other, they have an allocative role in influencing the acquisition of limited resources. Our theory suggests that managers strategically adapt organizational aspirations to balance the tension between the two concerns. They set more aggressive aspirations when facing increased pressure to acquire resources, but set more conservative targets when the costs of missing performance targets are higher. In the context of annual management forecasts, which allow us to directly observe performance targets and their deviation from traditional aspiration measures, we find that external factors influencing the intensity of resource pressure and the cost of missing performance targets determine the aggressiveness of organizational aspirations. This study highlights a novel antecedent of aspirations that complements existing explanations, linking agency and governance research with behavioral theory.
Coping with the Aftermath of Terror-Resilience of ZAKA Body Handlers

Abstract This study assessed the psychological consequences of body handling in the aftermath of terror attacks on 87 ZAKA volunteers and the implications of coping in attenuating the detrimental effects of prolonged exposure to terror. Subjects reported a low sense of danger and considerable self-efficacy. Only two participants (2.3 percent) met symptom criteria for PTSD, and 16 (18.4 percent) met criteria for sub-clinical posttraumatic disorder. Several possible explanations for the resilience of subjects are altruistic and religious rewards, respect and admiration from society and a tendency for sensation seeking.
Understanding Adoption of Big Data Analytics in China: From Organizational Users Perspective

Big data is a recent technology employed by companies to achieve competitive advantage. The investment of big data technologies in USA was estimated at more than 30 billion USD in 2016. However, the investment of big data technologies in China was relatively small in 2016. Grounded in the Technology-Organization-Environment (TOE) framework, this study analyzes the factors that can affect Big Data Readiness, which further affect attitudes and intention of adopting big data. The industries can make use of the results as reference to utilize big data for a more productive business.
Discrete rotation for directional orthogonal wavelet packets

We propose a simple and new way of defining the directional wavelet packets basis. This new decomposition is based on a discrete rotation. To avoid information loss or modification (in order to keep the orthogonality property) we use a new one-to-one discrete rotation. This rotation is based on three discrete shear transformations. This new oriented basis does not divide the 2D Fourier plane into square regions. Instead of horizontal and vertical favourite directions, our directional scheme treats different arbitrary directions.
Time-Dependent Influence Measurement in Citation Networks

In every scientific discipline, researchers face two common dilemmas: where to find bleeding-edge papers and where to publish their own articles. We propose to answer these questions by looking at the influence between communities, e.g. conferences or journals. The influential conferences are those which papers are heavily cited by other conferences, i.e. they are visible, significant and inspiring. For the task of finding such influential places-to-publish, we introduce a Running Influence model that aims to discover pairwise influence between communities and evaluate the overall influence of each considered community. We have taken into consideration time aspects such as intensity of papers citations over time and difference of conferences starting years. The community influence analysis is tested on real-world data of Computer Science conferences.
A Note on Optimal Experimentation Under Risk Aversion

In a standard two-armed bandit setup, this paper shows – counterintuitively – that a more risk-averse decision maker might be more willing to take risky actions. The reason relates to the fact that pulling the risky arm in bandit models produces information on the environment – thereby reducing the risk that a decision maker will face in the future. This finding gives reason for caution when inferring risk preferences from observed actions: in a bandit setup, observing a greater appetite for risky actions can actually be indicative of more risk aversion, not less.
Hybrid Fault-Tolerant Consensus in Asynchronous and Wireless Embedded Systems

Byzantine fault-tolerant (BFT) consensus in an asynchronous system can only tolerate up to floor[(n-1)/3] faulty processes in a group of n processes. This is quite a strict limit in certain application scenarios, for example a group consisting of only 3 processes. In order to break through this limit, we can leverage a hybrid fault model, in which a subset of the system is enhanced and cannot be arbitrarily faulty except for crashing. Based on this model, we propose a randomized binary consensus algorithm that executes in complete asynchrony, rather than in partial synchrony required by deterministic algorithms. It can tolerate up to floor[(n-1)/2] Byzantine faulty processes as long as the trusted subsystem in each process is not compromised, and terminates with a probability of one. The algorithm is resilient against a strong adversary, i. e. the adversary is able to inspect the state of the whole system, manipulate the delay of every message and process, and then adjust its faulty behaviour during execution. From a practical point of view, the algorithm is lightweight and has little dependency on lower level protocols or communication primitives. We evaluate the algorithm and the results show that it performs promisingly in a testbed consisting of up to 10 embedded devices connected via an ad hoc wireless network.
eSports, skins and loot boxes: Participants, practices and problematic behaviour associated with emergent forms of gambling

Twenty years since the Internet transformed gambling products and services, the convergence of online games and gambling has initiated a new means of consuming Internet-based media. Gambling specifically connected to eSports is a significant development, not only offering a new avenue for existing gambling products to be inserted into gaming media but also affording several novel experiences (e.g. skins and loot boxes). This study assesses participation rates and demographic characteristics of eSports spectators who gamble via an international online survey (N = 582). The sample highlighted the prevalence of young, often under-age, males in eSports-related gambling activities. Participation in gambling, and gambling-like activities, was found to be 67%, with rates of problematic and potentially problematic gambling in the sample being 50.34%. Finally, increased gambling is associated with increased spectating of eSports. Although the results are not generalisable to the wider population, they suggest a need for increased attention, from academia and regulators, regarding newly emergent gambling behaviours in contemporary digital culture.
Visualization Techniques Applied to Image-to-Image Translation

Convolutional Neural Networks became a state-of-the-art approach for many different problems of computer vision, pattern recognition, and image processing. However, due to the large number of parameters of these architectures, researchers may find difficult to explain what the networks are using as discriminative patterns. An alternative to better understand the behavior of the learned convolutional kernels is the use of visualization techniques. Currently, visualization techniques are more frequently applied to classification tasks. In this paper, we address the visualization of image-to-image translation. One of the contributions of our work is the possibility to modify a network based on the kernel visualization and achieve superior results.
Improving Modularization in Industry by Introducing a New Model for Module Classification

Modules are often considered the basis of product platforms by enabling a variety of product variants based on interchangeable modules. In this way, modules enable efficient utilization of resources through economies of scale. The purpose of this work is to improve the product realization process by introducing a new model for module classification that enable companies to structure their assets and formalizing them in the development system. The modules developed following this methodology contains both physical resources and non-physical resources that can be reused in a structured way, thereby improving the efficiency of the development process.
A Simplified Musculoskeletal Hip Model for Replicating the Natural Human Walking Behavior

Musculoskeletal models provide an alternative way to explore the function of muscles. The purpose of this study is to develop a simplified musculoskeletal hip model with lumped muscles to investigate the biomechanics and energetics of biological muscle groups during human walking. Our hip model is actuated by four active Hill-type muscle-tendon units (MTUs) and a passive torsional spring representing elastic tissues, the lumped muscle parameters of the model are derived from initial muscles within a group and optimized to make it output human-Iike profile of the joint moment. The coefficient of determination and root mean squared error (RMSE) are both used to validate the hip model. Our results show the coefficient and normalized RMSE related to body weight are both in the statistical acceptance range, which validate the ability of our model replicating human behaviors at the hip joint-level. The calculated muscle activation and total metabolic energy consumption of the hip MTUs also match the experimental data well, which further validate the performance of the model at a deeper muscle-level.
Effect of Coffee Intake on Heat Rate Variability and Driving Performance in Sleep-deprived Condition

This driving simulation study investigates the effect of coffee intake as a countermeasure on sleepiness and driving performance decrement in sleep-deprived condition. Twelve university students (mean age of $21.2 \pm 0.2$ years) drove following an urban route after 24 h sleep deprivation without drinking coffee or with a cup coffee containing 89.4 mg caffeine in two separate days and random order. Sleepiness level was assessed employing a subjective sleepiness scale and heart rate variability. The results show that consuming a cup of coffee before driving in sleep-deprived condition may enhance sympathetic nerve activities and improve driving performance. From these findings, it suggests that consuming coffee may be useful as sleepiness countermeasure in a sleep-deprived condition.
A Concept-Based ILP Approach for Multi-document Summarization Exploring Centrality and Position

Multi-document summarization systems aim to generate a brief text containing the most relevant information from a collection of related documents. The fast and continually growing volume of text data has increasingly drawn the attention from users and researchers to such systems. Aspects such as sentence centrality and position have been extensively studied in multi-document summarization as indicators of content relevancy. Very few works have investigated their efficient integration using global-based optimization approaches, however. This paper proposes a concept-based integer linear programming approach for multi-document summarization of news articles that integrates centrality and position features to filter out the less relevant sentences and measure the importance of concepts (textual fragments) in composing the output summary. The presented approach relies on a centrality-based strategy to perform the sentence clustering process and also to support the sentence ordering step. The benchmarks conducted with four datasets of the Document Understanding Conferences from 2001 to 2004 demonstrate that the proposed approach presents competitive performance compared with other state-of-the-art methods.
Organization/fuzzy Approach to the CTO Problem

In the CTO problem a group of moving observers should monitor a group of moving targets in order to maximize the average number of observed targets (ANOT). This work proposes and evaluates a centralized strategy based on FCM (Fuzzy C-means) to determine the movement of the observers in a setting in which the targets move randomly. The observer group is modeled as an agent organization specified in an FSB (Function-Structure-Behavior) standard. Compared with similar works, the test results are competitive in addition to avoiding known drawbacks reported in the literature.
Rapid Thermal Simulation of Powder Bed Additive Manufacturing

Controlling thermal history is a key factor for the success of an additive manufacturing operation. Thermal history depends on manufacturing strategies i.e., beam path and energy input. Energy input and beam path, are determined by either very simple models at the machine site or complex and time-consuming finite element models. This paper proposes a new thermal simulation method based on abacus to determine energy input along the trajectories. The paper presents first the method. Then, the method is validated comparing its results to finite element simulation and to built part quality.
The Effect of Subscription Video-on-Demand on Piracy: Evidence from a Household-Level Randomized Experiment

We partner with a major multinational telecommunications provider to analyze the effect of subscription video-on-demand (SVoD) services on digital piracy. For a period of 45 consecutive days, a group of randomly selected households who used BitTorrent in the past were gifted with a bundle of TV channels with movies and TV shows that could be streamed as in SVoD. We find that, on average, households that received the gift increased overall TV consumption by 4.6% and reduced Internet downloads and uploads by 4.2% and 4.5%, respectively. However, and also on average, treated households did not change their likelihood of using BitTorrent during the experiment. Our findings are heterogeneous across households and are mediated by the fit between the preferences of households in our sample for movies and the content available as part of the gifted channels. Households with preferences aligned with the gifted content reduced their probability of using BitTorrent during the experiment by 18% and decreased their am.