Miracle-dz/UniFAR-FTUs huggingface dataset:
The Millennial Generation

This article puts to the test the notion that younger generations, most notably the Millennials, value work less than older generations do. The analysis, deploying a linear probability model, is based on Statistics Finland’s Quality of Work Life Surveys, 1984 to 2013. Focusing on labour market entrants aged 15 to 29, we address two main themes: the value given to work, leisure and family life, and work commitment. Regardless of age, the value given to work has remained consistently high for the past three decades. At the same time, leisure and family life have gained increasing importance, not only among the Millennials but also among older generations. The Millennials are more prepared to change to a different occupational field than older employees, but this is not a new tendency, and therefore the generational gap remains unaffected. The evidence does not support the argument that the Millennials are less work-oriented than older generations.
D'Avolio A;Simiele M;Calcagno A;Siccardi M;Larovere G;Agati S;Baietto L;Cusato J;Tettoni M;Sciandra M;Trentini L;Di Perri G;Bonora S. Intracellular accumulation of ritonavir combined with different protease inhibitors and correlations between concentrations in plasma and peripheral blood mononuclear

Objectives Ritonavir, used at low doses as a boosting agent of other protease inhibitors (PIs), is known to be associated with metabolic complications and gastrointestinal disturbances. The rate of accumulation of ritonavir within cells is still debated due to scarce data and methodological limitations. Therefore, our aim was to evaluate intracellular ritonavir penetration when used with different boosted PIs in the clinical setting. Methods Patients administered with atazanavir/ritonavir (300/100 mg, once daily), darunavir/ritonavir [600/100 mg, twice daily (darunavir‐600) and 800/100 mg, once daily (darunavir‐800)], lopinavir/ritonavir (400/100 mg, twice daily) and tipranavir/ritonavir (500/200 mg, twice daily) were considered. Blood sampling at the end of the dosing interval (Ctrough) was performed. Peripheral blood mononuclear cell (PBMC)‐associated and plasma ritonavir and PI concentrations were measured by validated HPLC methods. PBMC count and individual mean cell volume (MCV) were measured using a Coulter Counter instrument. Results One hundred patients were enrolled. Frequencies of ritonavir‐boosted PIs were atazanavir, 37%; darunavir‐600, 23%; lopinavir, 19%; tipranavir, 13%; and darunavir‐800, 8%. The median intracellular and plasma concentrations of ritonavir were 1279 ng/mL (IQR 727–2087) and 170 ng/mL (IQR 82–384), respectively, accounting for a cellular accumulation ratio of 7.69 (5.7–10.9). Significant differences in ritonavir intracellular concentrations emerged among different PIs (P < 0.001): specifically between darunavir‐600 and atazanavir (P < 0.001), between darunavir‐600 and tipranavir (P = 0.009), between atazanavir and lopinavir (P < 0.001) and between lopinavir and tipranavir (P = 0.027). Conclusions Our study showed a higher rate of ritonavir intracellular accumulation than previously reported, possibly due to the more accurate calculation of intracellular concentrations by MCV. The ratio varied according to concomitantly administered PIs, suggesting their influence on the rate of ritonavir intracellular penetration.
PENGARUH MODEL PEMBELAJARAN TALKING STICK TERHADAP KEMAMPUAN MEMBACA PEMAHAMAN SISWA KELAS VIII SMP PGRI SUKAMORO KABUPATEN BANYUASIN

ABSTRAK Penelitian ini bertujuan untuk mendeskripsikan keaktifan belajar siswa dalam pembelajaran membaca pemahaman selama diterapkannya model pembelajaran talking stick, dan untuk mengetahui pengaruh model pembelajaran talking stick terhadap kemampuan membaca pemahaman siswa kelas VIII di SMP PGRI Sukamoro Kabupaten Banyuasin. Populasi dalam penelitian ini adalah siswa kelas VIII, sedangkan sampel dalam penelitian ini adalah siswa VIII.1 sebagai kelas eksperimen dan kelas VIII.2 sebagai kelas kontrol. Metode penelitian yang dilakukan secara eksperimen, dengan teknik pengumpulan data tes, dan non tes sedangkan teknik analisis data menggunakan uji-t. Berdasarkan hasil analisis data penelitian dapat disimpulkan bahwa hasil observasi selama proses pembelajaran membaca pemahaman menggunakan model pembelajaran talking stick, terlihat bahwa proses membaca pemahaman siswa sangat aktif. Hasil kebenaran pengujian hipotesis yang penulis ajukan terlihat bahwa t hitung > t tabel , yakni 3,37>1,67, hal ini dapat diartikan bahwa ada pengaruh model pembelajaran talking stick terhadap kemampuan membaca pemahaman siswa kelas VIII di SMP PGRI Sukamoro Kabupaten Banyuasin, terbukti kebenarannya dan dapat diterima. Kata kunci: Model pembelajaran talking stick dan kemampuan membaca pemahaman
MINOR PLANET 2002 EX12 (=169P/NEAT) AND THE ALPHA CAPRICORNID SHOWER

Minor planet 2002 EX12 (=comet 169P/NEAT) is identified as the parent body of the alpha Capricornid shower, based on a good agreement in the calculated and observed direction and speed of the approaching meteoroids for ejecta 4500–5000 years ago. The meteoroids that come to within 0.05 AU of Earth's orbit show the correct radiant position, radiant drift, approach speed, radiant dispersion, duration of activity, and distribution of dust at the other node, but meteoroids ejected 5000 years ago by previously proposed parent bodies do not. A more recent formation epoch is excluded because not enough dust would have evolved into Earth's path. The total mass of the stream is about 9 × 1013 kg, similar to that of the remaining comet. Release of so much matter in a short period of time implies a major disruption of the comet at that time. The bulk of this matter still passes inside Earth's orbit, but will cross Earth's orbit 300 years from now. As a result, the alpha Capricornids are expected to become a major annual shower in 2220–2420 a.d., stronger than any current annual shower.
Comparison of Competing Theories about Ideation and Creativity

Abstract A measure of ideational behaviour, often used to estimate the potential for creative thinking, was administered to 796 children and their parents and teachers. Correlations among groups were explored. The data provided an opportunity to (a) compare four theories of creativity (a one-factor theory, 2 two-factor theories, and a three-factor theory) and (b) determine empirically how the measure of ideation should be scored (based on its empirical structure). Results of confirmatory factor analyses indicated that one of the twofactor theories (Process and Product) best fit the data and was useful for comparisons of the children and their parents and teachers. Practical implications of the differences between parents and teachers are explored. Any effort to fulfil creative potentials, for example, would probably be the most likely to succeed if children, parents, and teachers agreed, and just as probable are difficulties if the three groups disagreed or considered different things when judging creative potential. Limitations of the study are also discussed.
Myxoid degeneration in a huge uterine leiomyoma : an unusual benign pathology mimiking malignancy

Uterine leiomyoma are the most common benign uterine tumor. Leiomyoma is a firm and rubbery solid tumor. Infrequently, cystic or myxoid degeneration occurred in the tumor. Giant leiomyoma commonly arise from the uterus which are susceptible to this type of degeneration. We report a case of large leiomyoma arising from the posterior wall of the uterus with myxoid degeneration in a 40-year-old women complained of pelvic heaviness with a palpable pelviabdominal mass. The patient has had hysterectomy with salpingo-oophorectomy. The mass weighted about five kgs. Final histological findings concluded to a uterine myxoid leiomyoma. In conclusion, myxoid leiomyoma of the uterus is rare, its diagnosis remains histological. Leiomyosarcoma should always be ruled out. Although it should be differentiated from malignant ovarian tumor preoperative.
Towards a Unified Analysis of Random Fourier Features

Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. The existing theoretical analysis of the approach, however, remains focused on specific learning tasks and typically gives pessimistic bounds which are at odds with the empirical results. We tackle these problems and provide the first unified risk analysis of learning with random Fourier features using the squared error and Lipschitz continuous loss functions. In our bounds, the trade-off between the computational cost and the expected risk convergence rate is problem specific and expressed in terms of the regularization parameter and the \emph{number of effective degrees of freedom}. We study both the standard random Fourier features method for which we improve the existing bounds on the number of features required to guarantee the corresponding minimax risk convergence rate of kernel ridge regression, as well as a data-dependent modification which samples features proportional to \emph{ridge leverage scores} and further reduces the required number of features. As ridge leverage scores are expensive to compute, we devise a simple approximation scheme which provably reduces the computational cost without loss of statistical efficiency.
Deep Learning for Inverse Problems: Bounds and Regularizers

Inverse problems arise in a number of domains such as medical imaging, remote sensing, and many more, relying on the use of advanced signal and image processing approaches -- such as sparsity-driven techniques -- to determine their solution. This paper instead studies the use of deep learning approaches to approximate the solution of inverse problems. In particular, the paper provides a new generalization bound, depending on key quantity associated with a deep neural network -- its Jacobian matrix -- that also leads to a number of computationally efficient regularization strategies applicable to inverse problems. The paper also tests the proposed regularization strategies in a number of inverse problems including image super-resolution ones. Our numerical results conducted on various datasets show that both fully connected and convolutional neural networks regularized using the regularization or proxy regularization strategies originating from our theory exhibit much better performance than deep networks regularized with standard approaches such as weight-decay.
Attack and defence in cellular decision-making: lessons from machine learning.

Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and models of cellular decision-making (adaptive proofreading). We apply attacks from machine learning to simple decision-making models, and show explicitly the correspondence to antagonism by weakly bound ligands. Such antagonism is absent in more nonlinear models, which inspired us to implement a biomimetic defence in neural networks filtering out adversarial perturbations. We then apply a gradient-descent approach from machine learning to different cellular decision-making models, and we reveal the existence of two regimes characterized by the presence or absence of a critical point for the gradient. This critical point causes the strongest antagonists to lie close to the decision boundary. This is validated in the loss landscapes of robust neural networks and cellular decision-making models, and observed experimentally for immune cells. For both regimes, we explain how associated defence mechanisms shape the geometry of the loss landscape, and why different adversarial attacks are effective in different regimes. Our work connects evolved cellular decision-making to machine learning, and motivates the design of a general theory of adversarial perturbations, both for in vivo and in silico systems.
Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework

Iris segmentation and localization in non-cooperative environment is challenging due to illumination variations, long distances, moving subjects and limited user cooperation, etc. Traditional methods often suffer from poor performance when confronted with iris images captured in these conditions. Recent studies have shown that deep learning methods could achieve impressive performance on iris segmentation task. In addition, as iris is defined as an annular region between pupil and sclera, geometric constraints could be imposed to help locating the iris more accurately and improve the segmentation results. In this paper, we propose a deep multi-task learning framework, named as IrisParseNet, to exploit the inherent correlations between pupil, iris and sclera to boost up the performance of iris segmentation and localization in a unified model. In particular, IrisParseNet firstly applies a Fully Convolutional Encoder-Decoder Attention Network to simultaneously estimate pupil center, iris segmentation mask and iris inner/outer boundary. Then, an effective post-processing method is adopted for iris inner/outer circle localization.To train and evaluate the proposed method, we manually label three challenging iris datasets, namely CASIA-Iris-Distance, UBIRIS.v2, and MICHE-I, which cover various types of noises. Extensive experiments are conducted on these newly annotated datasets, and results show that our method outperforms state-of-the-art methods on various benchmarks. All the ground-truth annotations, annotation codes and evaluation protocols are publicly available at https://github.com/xiamenwcy/IrisParseNet.
Application of TAM model to the use of information technology

The purpose of this research is to see the application of modified TAM model by entering the experiential variable as a moderation variable to see one's intention in the use of technology especially internet banking. Data obtained through the distribution of questionnaires to customers. The study population is bank customers registered as users of internet banking services. The sample selection used a simple random sampling technique. Hypothesis testing using Partial Least Square (PLS) method through AMOS program. The results showed that the proposed five hypotheses, two significant and three insignificant. Perceived ease of use is significantly related to perceived usefulness. Per-ceived usefulness is not significantly related to intention to use. Perceived ease of use is significantly related to intention to use moderated by experience and not significantly correlated with intention to use moderated by the Experience.
VAE-GANs for Probabilistic Compressive Image Recovery: Uncertainty Analysis

Recovering high-quality images from limited sensory data is a challenging computer vision problem that has received significant attention in recent years. In particular, solutions based on deep learning, ranging from autoencoders to generative models, have been especially effective. However, comparatively little work has centered on the robustness of such reconstructions in terms of the generation of realistic image artifacts (known as hallucinations) and quantifying uncertainty. In this work, we develop experimental methods to address these concerns, utilizing a variational autoencoder-based generative adversarial network (VAE-GAN) as a probabilistic image recovery algorithm. We evaluate the model's output distribution statistically by exploring the variance, bias, and error associated with generated reconstructions. Furthermore, we perform eigen analysis by examining the Jacobians of outputs with respect to the aliased inputs to more accurately determine which input components can be responsible for deteriorated output quality. Experiments were carried out using a dataset of Knee MRI images, and our results indicate factors such as sampling rate, acquisition model, and loss function impact the model's robustness. We also conclude that a wise choice of hyperparameters can lead to the robust recovery of MRI images.
Discriminate Natural versus Loudspeaker Emitted Speech

In this work, we address a novel, but potentially emerging, problem of discriminating the natural human voices and those played back by any kind of audio devices in the context of interactions with in-house voice user interface. The tackled problem may find relevant applications in (1) the far-field voice interactions of vocal interfaces such as Amazon Echo, Google Home, Facebook Portal, etc, and (2) the replay spoofing attack detection. The detection of loudspeaker emitted speech will help avoiding false wake-ups or unintended interactions with the devices in the first application, while eliminating attacks involve the replay of recordings collected from enrolled speakers in the second one. At first we collect a real-world dataset under well-controlled conditions containing two classes: recorded speeches directly spoken by numerous people (considered as the natural speech), and recorded speeches played back from various loudspeakers (considered as the loudspeaker emitted speech). Then from this dataset, we build prediction models based on Deep Neural Network (DNN) for which different combination of audio features have been considered. Experiment results confirm the feasibility of the task where the combination of audio embeddings extracted from SoundNet and VGGish network yields the classification accuracy up to about 90%.
LEOPARD: Identifying Vulnerable Code for Vulnerability Assessment Through Program Metrics

Identifying potentially vulnerable locations in a code base is critical as a pre-step for effective vulnerability assessment; i.e., it can greatly help security experts put their time and effort to where it is needed most. Metric-based and pattern-based methods have been presented for identifying vulnerable code. The former relies on machine learning and cannot work well due to the severe imbalance between non-vulnerable and vulnerable code or lack of features to characterize vulnerabilities. The latter needs the prior knowledge of known vulnerabilities and can only identify similar but not new types of vulnerabilities. In this paper, we propose and implement a generic, lightweight and extensible framework, LEOPARD, to identify potentially vulnerable functions through program metrics. LEOPARD requires no prior knowledge about known vulnerabilities. It has two steps by combining two sets of systematically derived metrics. First, it uses complexity metrics to group the functions in a target application into a set of bins. Then, it uses vulnerability metrics to rank the functions in each bin and identifies the top ones as potentially vulnerable. Our experimental results on 11 real-world projects have demonstrated that, LEOPARD can cover 74.0% of vulnerable functions by identifying 20% of functions as vulnerable and outperform machine learning-based and static analysis-based techniques. We further propose three applications of LEOPARD for manual code review and fuzzing, through which we discovered 22 new bugs in real applications like PHP, radare2 and FFmpeg, and eight of them are new vulnerabilities.
Natural Analysts in Adaptive Data Analysis

Adaptive data analysis is frequently criticized for its pessimistic generalization guarantees. The source of these pessimistic bounds is a model that permits arbitrary, possibly adversarial analysts that optimally use information to bias results. While being a central issue in the field, still lacking are notions of natural analysts that allow for more optimistic bounds faithful to the reality that typical analysts aren't adversarial. In this work, we propose notions of natural analysts that smoothly interpolate between the optimal non-adaptive bounds and the best-known adaptive generalization bounds. To accomplish this, we model the analyst's knowledge as evolving according to the rules of an unknown dynamical system that takes in revealed information and outputs new statistical queries to the data. This allows us to restrict the analyst through different natural control-theoretic notions. One such notion corresponds to a recency bias, formalizing an inability to arbitrarily use distant information. Another complementary notion formalizes an anchoring bias, a tendency to weight initial information more strongly. Both notions come with quantitative parameters that smoothly interpolate between the non-adaptive case and the fully adaptive case, allowing for a rich spectrum of intermediate analysts that are neither non-adaptive nor adversarial. Natural not only from a cognitive perspective, we show that our notions also capture standard optimization methods, like gradient descent in various settings. This gives a new interpretation to the fact that gradient descent tends to overfit much less than its adaptive nature might suggest.
Attention to eSports advertisement: effects of ad animation and in-game dynamics on viewers’ visual attention

ABSTRACT ESports refers to professional video gaming that is typically broadcasted with a live commentary on TV or the Internet. Despite the rapid growth of eSports industry and its potential for a valuable advertising platform, there has not been much discussion about effective advertising strategies for the placement of virtual ads in eSports. Based on previous research, one advertisement factor (i.e. ad animation) and one in-game factor (i.e. in-game dynamics) are identified and tested in an experiment (N = 116) with a 2 (ad animation: static vs. animated ads) × 2 (in-game dynamics: battle vs. non-battle scenes) mixed design. The results show that both ad animation and in-game dynamics have a significant impact on viewers’ visual attention, in terms of fixation count and duration measured by an eye-tracking device. The results also show a significant interaction in which the impact of ad animation on fixation duration is more magnified when in-game play is less busy (i.e. non-battle scenes), compared to when it is busy (i.e. battle scenes). Theoretical implications based on the limited-capacity model, as well as practical implications for practitioners are discussed.
Revisiting five decades of educational technology research: A content and authorship analysis of the British Journal of Educational Technology

Reflecting on 50 years of educational technology research, a content and authorship analysis was conducted of 1777 research article titles and abstracts, published in the British Journal of Educational Technology (BJET) from 1970 to Issue 3, 2018. A text‐mining tool (Leximancer) was used to identify key concepts and themes emerging throughout each of the five decades, which were then compared to those found in a previous analysis of Computers & Education, as well as the most cited BJET publications in each decade. The framework of West and Rich (2012) was then used to provide guidance on BJET’s rigour, impact and prestige. Common themes in BJET throughout the past 50 years have included the evolution of teaching and learning in distance education, the emergence of instructional design, misunderstanding between practitioners and learning designers, issues of pre and in‐service teacher education and technology uptake by educators and students, including the confidence to do so, the technology skills of educators and students, as well as a lack of institutional support to provide space and time for training and integration to occur. The authorship analysis revealed an encouraging increase in international identity over time, with 60% of articles in BJET published by an author outside of the UK over the past 50 years, and 79% in the past decade. Despite this, contributions from South America, the Middle East and Africa are vastly underrepresented, and authors from these regions need further support from the field. Suggested future research areas include finding alternative models of educator professional development, further exploration of the role of theory and policy, as well as the rise of co‐authorship and collaborative research.