Miracle-dz/UniFAR-FTUs huggingface dataset:
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by Chad D. Mano With the expanse of the Internet and the increased reliance on computer networking technology for everyday business, the need to protect electronic data and communication from malicious attack has become increasingly critical. This dissertation addresses the rogue system problem, a significant threat in modern networks. A rogue system is a device installed within a network without the authorization or knowledge of network administrators, which is typically engaged in unauthorized activities. These systems pose a major threat to network data and resources, potentially resulting in the exposure of sensitive information or network performance degradation. This dissertation presents analysis and solutions for rogue system threats within a cooperative distributed network environment and within various types of wireless environments. In addition, a tool is presented which enables high speed network packet logging, for the purpose of rogue system detection, using inexpensive equipment in a scalable distributed storage infrastructure. The major contributions of this dissertation are as follows. The development of a secure communication protocol which protects a distributed network from potential rogue system attacks while enabling the implementation of bandwidth conservation techniques for efficiency. An important enhancement of a standard Chad D. Mano wireless communication protocol for the purpose of preventing both insider and outsider rogue eavesdropping attacks. A novel packet payload slicing technique for the purpose of detecting rogue wireless access points within a corporate network environment. An analysis of the potential of host-based rogue wireless man-inthe-middle attack detection. The development of a tool for high speed traffic analysis to aid in rogue system detection. Rogue system threats will continue to grow as networks become more complex and new attack techniques evolve to better evade detection. The future direction of this work includes applying these techniques to newly identified threats for the purpose of gauging the effectiveness of the proposed methods and to aid in discovering new means of defending against rogue system attacks. In addition, rogue threats in less traditional types of network environments, such as peer-topeer and personal area networks, will be addressed in order to provide protection from all means of electronic rogue system attacks.
Electronic Structure of Twisted Bilayers of Graphene/MoS2 and MoS2/MoS2

Vertically stacked two-dimensional multilayer structures have become a promising prototype for functionalized nanodevices due to their wide range of tunable properties. In this paper we performed first-principles calculations to study the electronic structure of nontwisted and twisted bilayers of hybrid graphene/MoS2 (Gr/MoS2) and MoS2/MoS2. Both twisted bilayers of Gr/MoS2 and MoS2/MoS2 show significant differences in band structures from the nontwisted ones with the appearance of the crossover between direct and indirect band gap and gap variation. More interestingly, the band structures of twisted Gr/MoS2 with different rotation angles are very different from each other, while those of MoS2/MoS2 are very similar. The variation of band structure with rotation angle in Gr/MoS2 is, indeed, originated from the misorientation-induced lattice strain and the sensitive band-strain dependence of MoS2.
Chimie à la surface des grains dans les disques protoplanetaires

La formation des planetes a lieu dans les disques protoplanetaires constitues de gaz et de poussieres. Si ces dernieres ne representent que 1% de la masse totale du disque, elles jouent un role fondamental pour l’evolution chimique des disques en agissant comme catalyseurs pour la formation des molecules. Comprendre cette chimie est essentiel pour remonter aux conditions physiques initiales qui ont permis la naissance des planetes.Au cours de ma these, j’ai etudie la chimie a la surface des grains de poussieres et son impact sur l’evolution chimique du nuage moleculaire, condition initale de la formation du disque, et du disque protoplanetaire. Grâce a des simulations numeriques, a l’aide du code de chimie gaz-grain Nautilus, j’ai pu montrer l’importance des reactions de diffusion et des interactions gaz-grain pour les abondances des especes en phase gazeuse. Les resultats du modele couples aux observations ont egalement mis en evidence les effets de la structure physique (temperature, densite, AV) sur la distribution des molecules dans les disques.
Mobility Before Government Restrictions in the Wake of COVID-19

During the first wave of COVID-19 in Europe, people’s retail and recreation mobility, a high-frequency proxy for consumption, correlated negatively with the increase in the number of COVID-19 daily cases. We propose a new identification strategy to assess how much of the drop in retail mobility was driven by the arrival of bad epidemiological news, represented by the reported daily new cases. In particular, we exploit the time span between the first reported COVID-19 infection in a country and the implementation of a nationwide stay-at-home order. As during this period people were free to move, we argue that this approach allows us to disentangle the role of virus fear from that of restriction in the observed mobility reduction. We find that a 1% increase in daily cases reduces retail mobility by approximately 0.02%, a sizable effect in the context of the first wave of the pandemic. This suggests that the epidemic affects household spending and economic activity, even in the absence of mandatory mobility restrictions.
A Measure of Effectiveness Level of Online Learning Amid COVID-19 Pandemic in the Course of the Project Management Information Systems (Case Study in Stikom Yos Sudarso Purwokerto)

As a result of COVID-19, the government is attempting and implementing a national education system that must guarantee equal opportunity and improve the quality of education, in the globalization 4.0 era through online (network system). From the formulation of prior problems, based on the policy "Online learning from Home," is it effective? This study aims to determine how to measure the effectiveness level of online learning amid COVID-19 pandemic, with a case study in STIKOM Yos Sudarso Purwokerto. The research method used Action Research, with the Criterion-Referenced Evaluation (Penilaian Acuan Patokan/PAP) approach and varied online learning models. In this case study, learning tools used Google Classroom, Zoom, Google Meet, Youtube, Trello.com, WhatsApp, and email. Results of the research teaching and learning process using online showed that it was proven to be effective based on PAP reference with respondents consisting of 55 students passed the test of 120 questions, with the results of grades A (3.6%,) B (27.3%), C (65.5%), D (3, 6%), and E (0). From the Evaluation Results of Learning Activities of the Project Management Information Systems course before and after using the online system through pre-test and post-test, hypothesis testing using a Paired sample T-test in the teaching and learning process was more effective. With a mean score of a pre-test of 73.90 and post-test after using online learning with 80.20. Hence, it was proven that H1 is accepted by assuming the result of the study using the Paired sample T-test was more effective. Implementation test results through ISO 9126 obtained a total percentage of 88.25%, consisting of Usability (89.73), Learnability (87.55), Effective (89.75), and Acceptability (85.98). Thus, it can be determined that online learning amid the COVID-19 pandemic for project management in the Information system course (case study in STIKOM Yos Sudarso Purwokerto) was very effective with the highest number of scores (89.75) and was beneficial.
Impulse-Based Computation of Policy Counterfactuals

We propose an efficient procedure to solve for policy counterfactuals in linear models with occasionally binding constraints. The procedure does not require knowledge of the structural or reduced-form equations of the model, its state variables, or its shock processes. Forecasts of the variables entering the policy problem, and impulse response functions of these variables to anticipated policy shocks under an arbitrary policy, constitute sufficient information to construct valid counterfactuals. We show how to compute solutions for instrument rules and optimal discretionary and commitment policies with multiple policy instruments, and discuss various extensions, including imperfect information, asymmetric objectives, and limited commitment. Our procedure facilitates the comparison of the effects of policy regimes across models. As an application, we compute counterfactual paths of the U.S. economy around 2015 for several monetary policy regimes.
The Long-Run Spillover Effects of Pollution: How Exposure to Lead Affects Everyone in the Classroom

Evidence shows that lead-exposed children are more disruptive and have lower achievement. However, we know less about how lead-exposed children affect the learning environment of their classroom peers. We estimate these spillover effects using new data on children's blood lead levels (BLLs) matched to all education data in North Carolina. We compare siblings who attend the same school, but whose school-grade cohorts differ in the proportion of children with elevated BLLs. We find that having more lead-exposed peers is associated with lower test scores and graduation rates, increased suspensions and dropping out of school, and a decrease in college intentions.
Exploring the Entry Point for Growth Steps: A Real-time Case Study of the Platform Development Process

Recent advances in information and communication technology have led to the rapid growth of platform businesses that provide a place for two or more sides (i.e., groups of participants), such as sellers and buyers, to interact and transact. This business has structural characteristics that are different from conventional businesses, and its development process is also considered unique. However, previous studies have mainly focused on established and successful platforms, and have not paid enough attention to the early stages of the process. As a result, scholars assumed that platforms would be built according to a blueprint that clearly stated which side and segment to approach. However, identifying and exploiting entrepreneurial opportunities is not a linear process and involves learning under uncertainty and trial and error. In light of the above, this study conducted a longitudinal case study to examine the process of defining the sides and segments approached by Company V, a startup that provides a platform to connect manga (cartoon) artists with readers and companies. Consequently, this study demonstrates that it is essential for platform startups to attract participants, and identify the optimal entry point for the company’s growth steps. In particular, when entering an industry such as the manga industry, where information technology is not widely used, it is possible to build a platform by leveraging technology in challenge areas, based on a deep understanding of the market structure and other companies’ trends. The study also suggests that it is effective to use the existing networks as a foothold, to identify on which side and segment to focus.
SIGSOFT Outstanding Doctoral Dissertation Award

As software becomes more important and ubiquitous, high quality software also becomes crucial. We depend on software developers who write the software to also maintain and improve its quality. When developers make changes to software, they rely on continuous integration [6] and regression testing [15] to check that changes do not break existing functionality. Continuous integration (CI) automates the process of building and testing software after every change. The process of running tests on the code after every change is known as regression testing. The goal of regression testing is to allow developers to detect and fix faults early on, ideally the moment the faults are introduced. Regression testing is widely used in both industry and open source, but regression testing suffers from two main challenges: (1) regression testing is costly, and (2) regression test suites often contain flaky tests.
Version 2.0

Le projet DEBIAN (http://www.debian.org) est un projet bénévole d'ampleur internationale visant à la création et à la production de systèmes d'exploitation libres. Les MEMBRES DU PROJET DEBIAN sont des personnes physiques disposant du droit de vote selon la constitution Debian (http://www.debian.org/devel/constitution) et d'une clé cryptographique PGP/GPG personnelle qui figure dans la liste officielle des clés cryptographiques du Projet Debian, telle que publiée sur http://keyring.debian. org.
Technical report on scaffolding learning by kinesthetic teaching and incremental learning

In this deliverable we present three novel learning approaches and movement representations which can benefit from kinesthetic support and imitation learning. These different approaches were applied to motor skill learning tasks that can benefit from human demonstrations. In particular EPFL-B has investigated how incremental learning can be used to adapt a learned forward model of a real robot arm to a new task. The model was initially learned via kinesthetic teaching using a dynamical system based approach. Furthermore EPFL-B has demonstrated which learning mechanisms are involved in teaching a robot how to catch fast inflight objects. Also this challenging task benefits from human demonstrations. UniBi demonstrated how kinesthetic teaching facilitates motor skill learning in three different scenarios which emphasize that kinesthetic teaching transfers knowledge about tasks but also task constraints: first a inverse kinematics model of a KUKA light-weight robot is learned using recurrent neuronal networks. In a second experiment the iCub robot was taught via kinesthetic teaching to learn pointing movements without depth calculation of camera calibration. Finally, UniBi analysed the trade-offs in a learning and control architecture between representation and generalization on iCub which was used to learn and generalize multiple movement primitives that in turn were acquired via kinesthetic teaching. At TUG an alternative movement primitive representation based on probabilistic inference in learned graphical models was developed. This approach has interesting and new features, i.e. it allows for an intuitive representation of complex motor skills based on learned cost functions. The cost function is in the most simple case specified by learned via-points. They allow for an easy integration of task specific prior knowledge. This prior knowledge might be observed from human demonstrations, which is currently investigated on a real robot in cooperation with UGent.
The Factors Affecting Entrepreneurial Intentions of University Students in Malaysia

The main purpose of this paper is to corroborate the factors affecting entrepreneurial intentions of university students in Malaysia constructed on empirical reviews. In this study, general searches were conducted to accumulate empirical literatures by the name of Entrepreneurship Development, Entrepreneurship Education and Theory of Planned Behaviour (TPB) in different online database sources such as Google Scholars, Springer Link, Wiley, Science Direct, JSTOR, Emerald full text, Scopus, and EBSCO HOST etc. This study found that innovation, entrepreneurship training & education, family background, government support program, social entrepreneurship, women participation, individual entrepreneurial characteristics, participation of micro, small & medium enterprises, youth empowerment, collaboration of governmentuniversity-industry is the key tool for entrepreneurship development. Stimulating employment will eventually help alleviating poverty. This study also found that there is a strong relation among students entrepreneurial attitude, subjective norms, perceived behavioural control. Moreover, the entrepreneurship teaching methodology has moderating effects on every relationship.
Shifted Power Transformations

Diagnostic displays for outlying and influential observations are reviewed. In some examples apparent outliers vanish after a power transformation of the data. Interpretation of the score statistic for transformations as regression on a constructed variable makes diagnostic methods available for detection of the influence of individual observations on the transformation, Methods for the power transformation are exemplified and extended to the power transformation after a shift in location, for which there are two constructed variables. The emphasis is on plots as diagnostic tools.
Direction-dependent Corrections in Polarimetric Radio Imaging. III. A-to-Z Solver—Modeling the Full Jones Antenna Aperture Illumination Pattern

In this third paper of a series describing direction-dependent corrections for polarimetric radio imaging, we present the the A-to-Z solver methodology to model the full Jones antenna aperture illumination pattern (AIP) using Zernike polynomials. In order to achieve accurate, thermal noise-limited imaging with modern radio interferometers, it is necessary to correct for the instrumental effects of the antenna primary beam (PB) as a function of time, frequency, and polarization. The algorithm employs the orthonormal, circular Zernike polynomial basis to model the full Jones AIP response, which is obtained by a Fourier transform of corresponding antenna holography measurements. These full Jones models are then used to reconstruct the full Mueller AIP response of an antenna, in principle accounting for all the off-axis frequency-dependent leakage effects of the PB. The A-to-Z solver is general enough to accommodate any interferometer for which holographic measurements exist, and we have successfully modeled the AIP of the VLA, MeerKAT, and ALMA as a demonstration of its versatility. We show that our models capture the PB morphology to high accuracy within the first two side lobes, and show the viability of full Mueller gridding and deconvolution for any telescope given high-quality holographic measurements.
Black-box Probe for Unsupervised Domain Adaptation without Model Transferring

In recent years, researchers have been paying increasing attention to the threats brought by deep learning models to data security and privacy, especially in the ﬁeld of domain adaptation. Existing unsupervised domain adaptation (UDA) methods can achieve promising performance without transferring data from source domain to target domain. However, UDA with representation alignment or self-supervised pseudo-labeling relies on the transferred source models. In many data-critical scenarios, methods based on model transferring may suffer from membership inference attacks and expose private data. In this paper, we aim to overcome a challenging new setting where the source models are only queryable but cannot be transferred to the target domain. We propose Black-box Probe Domain Adaptation (BPDA), which adopts query mechanism to probe and reﬁne information from source model using third-party dataset. In order to gain more informative query results, we further propose Distributionally Adversarial Training (DAT) to align the distribution of third-party data with that of target data. BPDA uses public third-party dataset and adversarial examples based on DAT as the information carrier between source and target domains, dispensing with transferring source data or model. Experimental results on benchmarks of Digit-Five, Ofﬁce-Caltech, Ofﬁce-31, Ofﬁce-Home, and DomainNet demonstrate the feasibility of BPDA without model transferring.
Decidability of Liveness on the TSO Memory Model

An important property of concurrent objects is whether they support progress -a special case of liveness-guarantees, which ensure the termination of individual method calls under system fairness assumptions. Liveness properties have been proposed for concurrent objects. Typical liveness properties includelock-freedom,wait-freedom,deadlock-freedom,starvation-freedom and obstruction-freedom. It is known that the five liveness properties above are decidable on the Sequential Consistency (SC) memory model for a bounded number of processes. However, the problem of decidability of liveness for finite state concurrent programs running on relaxed memory models remains open. In this paper we address this problem for the Total Store Order (TSO) memory model,as found in the x86 architecture. We prove that lock-freedom, wait-freedom,deadlock-freedom and starvation-freedom are undecidable on TSO for a bounded number of processes, while obstruction-freedom is decidable.
Faster Matchings via Learned Duals

A recent line of research investigates how algorithms can be augmented with machine-learned predictions to overcome worst case lower bounds. This area has revealed interesting algorithmic insights into problems, with particular success in the design of competitive online algorithms. However, the question of improving algorithm running times with predictions has largely been unexplored. We take a first step in this direction by combining the idea of machine-learned predictions with the idea of"warm-starting"primal-dual algorithms. We consider one of the most important primitives in combinatorial optimization: weighted bipartite matching and its generalization to $b$-matching. We identify three key challenges when using learned dual variables in a primal-dual algorithm. First, predicted duals may be infeasible, so we give an algorithm that efficiently maps predicted infeasible duals to nearby feasible solutions. Second, once the duals are feasible, they may not be optimal, so we show that they can be used to quickly find an optimal solution. Finally, such predictions are useful only if they can be learned, so we show that the problem of learning duals for matching has low sample complexity. We validate our theoretical findings through experiments on both real and synthetic data. As a result we give a rigorous, practical, and empirically effective method to compute bipartite matchings.
Loschmidt echo approach to Krylov-subspace approximation error estimation

The Krylov subspace method is a standard approach to approximate quantum evolution, allowing to treat systems with large Hilbert spaces. Although its application is general, and suitable for many-body systems, estimation of the committed error is involved. This makes it difficult to automate its use. In this paper, we solve this problem by realizing that such error can be regarded as a Loschmidt echo in a tight-binding Hamiltonian. We show that the different time-regimes of the approximation can be understood using simple physical ideas. More importantly, we obtain computationally cheap error bounds that describe with high precision the actual error in the approximation.
Hygiea: A secure, smart, privacy-preserving and interoperable Blockchain solution for the Covid-19 pandemic

In this article we present hygiea, an end-to-end blockchain-based solution for the Covid-19 pandemic. hygiea has two main objectives. The first is to allow governments to issue Covid-19 related certificates to citizens that can be verified by designated verifiers to ensure safer workplaces. The second is to provide the necessary tools to experts and decision makers to better understand the impact of the pandemic through statistical models built on top of the data collected by the platform. This work covers all steps of the certificate issuance, verification and revocation cycles with well-defined roles for all stakeholders. We also propose a governance model that is implemented via smart contracts ensuring security, transparency and auditability. Finally, we propose techniques for deriving statistical models that can be used by decision makers.