Reviewerly/exHarmony huggingface dataset:
중등 미용 교사의 교직경력별 핵심역량 수준 및 차이 분석

The purpose of this study is to analyze how the level of teacher core competency differs according to the teaching experience of a beauty school teacher. A survey was conducted to analyze the core competencies of beauty teachers. The analysis method is a one-way ANOVA. As a result of analyzing the core competency level of beauty teachers, ‘executive competency’ was the highest, followed by ‘attitude competency’, ‘knowledge competency’. In the case of knowledge, compared to ‘theoretical knowledge of the major and the major subject in charge’, ‘practical ability for the major and the major subject in charge’, ‘the ability to apply teaching and learning evaluation related to the major’, ‘the professor related to the major’, ‘Learning evaluation development ability’ was evaluated low, In the case of performance, the average was slightly higher than that of knowledge and attitude in all competencies, and the level of ‘the ability to instruct students to acquire a certificate related to a major’ was evaluated high. In terms of attitude, ‘the ability to communicate smoothly with peers’ was high. As a conclusion of this study, it was found that in order to respond to the internal and external beauty subject education environment and increase the quality change of beauty subject education, It is necessary to strengthen the competence of professional beauty teachers.
Synthesis-Guided Feature Learning for Cross-Spectral Periocular Recognition

A common yet challenging scenario in periocular biometrics is cross-spectral matching - in particular, the matching of visible wavelength against near-infrared (NIR) periocular images. We propose a novel approach to cross-spectral periocular verification that primarily focuses on learning a mapping from visible and NIR periocular images to a shared latent representational subspace, and supports this effort by simultaneously learning intra-spectral image reconstruction. We show the auxiliary image reconstruction task (and in particular the reconstruction of high-level, semantic features) results in learning a more discriminative, domain-invariant subspace compared to the baseline while incurring no additional computational or memory costs at test-time. The proposed Coupled Conditional Generative Adversarial Network (CoGAN) architecture uses paired generator networks (one operating on visible images and the other on NIR) composed of U-Nets with ResNet-18 encoders trained for feature learning via contrastive loss and for intra-spectral image reconstruction with adversarial, pixel-based, and perceptual reconstruction losses. Moreover, the proposed CoGAN model beats the current state-of-art (SotA) in cross-spectral periocular recognition. On the Hong Kong PolyU benchmark dataset, we achieve 98.65% AUC and 5.14% EER compared to the SotA EER of 8.02%. On the Cross-Eyed dataset, we achieve 99.31% AUC and 3.99% EER versus SotA EER of 4.39%.
Research Guides: Open Access: Home

This subject guide is intended to serve as a learning tool to educate our users on open access and the changing world of scholarly communications.
Neural network-based optimization of intelligent odometry system in an autonomous robot for accurate position and orientation estimation

The purpose of this study is to determine the accurate orientation and position of the intelligent dummy system for the Verdino autonomous robot. The position and orientation of the autonomous robot are calculated using an odometric mathematics model derived from the robot's trajectory equations. The odometry encoders are the system inputs, and the wheel diameter and distance are used to parameterize the model. This model is used to get the optimum nominal parameters by doing a minimal square minimization. This model is updated with a measurement of the real-time wheel diameter to improve the accuracy of the results. Based on data, an odometric model is trained using a neural network model. This neural network is put to the test, and the results indicate that by employing a smart odometry system, the neural network can improve estimate accuracy.
Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition

Decomposing a scene into its shape, reflectance and illumination is a fundamental problem in computer vision and graphics. Neural approaches such as NeRF have achieved remarkable success in view synthesis, but do not explicitly perform decomposition and instead operate exclusively on radiance (the product of reflectance and illumination). Extensions to NeRF, such as NeRD, can perform decomposition but struggle to accurately recover detailed illumination, thereby significantly limiting realism. We propose a novel reflectance decomposition network that can estimate shape, BRDF, and per-image illumination given a set of object images captured under varying illumination. Our key technique is a novel illumination integration network called Neural-PIL that replaces a costly illumination integral operation in the rendering with a simple network query. In addition, we also learn deep low-dimensional priors on BRDF and illumination representations using novel smooth manifold auto-encoders. Our decompositions can result in considerably better BRDF and light estimates enabling more accurate novel view-synthesis and relighting compared to prior art. Project page: this https URL
Creative Pedagogy for Higher Education

This century is called the digital age. In the twenty-first century, new scientific discoveries innumerable changes are occurring related to, informatization, globalization, the development of internet,artificial intelligence, robotics, astronautics, etc. The impact of this is quite significant on teaching and learning in Higher Educational Institutions. The purpose of the paper is to suggest models of creativity of innovative teaching methods and study the understanding of changes by teachers.
A Note on Simulation-Based Inference by Matching Random Features

We can, and should, do statistical inference on simulation models by adjusting the parameters in the simulation so that the values of {\em randomly chosen} functions of the simulation output match the values of those same functions calculated on the data. Results from the state-space reconstruction or geometry from a time series'' literature in nonlinear dynamics indicate that just $2d+1$ such functions will typically suffice to identify a model with a $d$-dimensional parameter space. Results from the features literature in machine learning suggest that using random functions of the data can be an efficient replacement for using optimal functions. In this preliminary, proof-of-concept note, I sketch some of the key results, and present numerical evidence about the new method's properties. A separate, forthcoming manuscript will elaborate on theoretical and numerical details.
Deploying MT Quality Estimation on a large scale: Lessons learned and open questions

This talk will focus on Memsource’s experience implementing MT Quality Estimation on a large scale within a translation management system. We will cover the whole development journey: from our early experimentation and the challenges we faced adapting academic models for a real world setting, all the way through to the practical implementation. Since the launch of this feature, we’ve accumulated a significant amount of experience and feedback, which has informed our subsequent development. Lastly we will discuss several open questions regarding the future role of quality estimation in translation.
Text Detection and Script Identification from Images using CNN

For humans, text is one of the most significant sources of knowledge. Detection of text in images has been an interesting computer vision topic in both industrial and academic study. There has been a lot of effort made into improving existing methods and establishing new ones. Sliding windows and connected component systems are more classic solutions.In this paper we are discussing some text detection methods.In this paper we are discussing text detection and identification based on convolutional neural network.Script identification is done by integrating local cnn and global cnn.
A/B Testing for Recommender Systems in a Two-sided Marketplace

Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. \textit{Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers}. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment, and (ii) a new experiment design mechanism that generates high quality experiments based on this quantification. Our approach, called UniCoRn ({Uni}fying {Co}unterfactual {R}a{n}kings), provides explicit control over the quality of the experiment and its computation cost. Further, we prove that our experiment design is optimal. Our approach is agnostic to the density of the producer-consumer network and does not rely on any treatment propagation assumption. Moreover, unlike the existing approaches, we do not need to know the underlying network in advance, making this widely applicable to the industrial setting where the underlying network is unknown and challenging to predict a priori due to its dynamic nature. We use simulations to thoroughly validate our approach and compare it against existing methods. We also implement UniCoRn in an edge recommendation application that serves tens of millions of members and billions of edge recommendations daily.
On the Power of Edge Independent Graph Models

Why do many modern neural-network-based graph generative models fail toreproduce typical real-world network characteristics, such as high triangledensity? In this work we study the limitations of edge independent random graphmodels, in which each edge is added to the graph independently with someprobability. Such models include both the classic Erd\{o}s-R\'{e}nyi andstochastic block models, as well as modern generative models such as NetGAN,variational graph autoencoders, and CELL. We prove that subject to a boundedoverlap condition, which ensures that the model does not simply memorize asingle graph, edge independent models are inherently limited in their abilityto generate graphs with high triangle and other subgraph densities. Notably,such high densities are known to appear in real-world social networks and othergraphs. We complement our negative results with a simple generative model thatbalances overlap and accuracy, performing comparably to more complex models inreconstructing many graph statistics.
Detecting Anomalous Event Sequences with Temporal Point Processes

Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security. In this paper, we frame the problem of detecting anomalous continuous-time event sequences as out-of-distribution (OoD) detection for temporal point processes (TPPs). First, we show how this problem can be approached using goodness-of-fit (GoF) tests. We then demonstrate the limitations of popular GoF statistics for TPPs and propose a new test that addresses these shortcomings. The proposed method can be combined with various TPP models, such as neural TPPs, and is easy to implement. In our experiments, we show that the proposed statistic excels at both traditional GoF testing, as well as at detecting anomalies in simulated and real-world data.
Privately Publishable Per-instance Privacy

We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy (DP) gives us a worst-case bound that might be orders of magnitude larger than the privacy loss to a particular individual relative to a fixed dataset. The pDP framework provides a more fine-grained analysis of the privacy guarantee to a target individual, but the per-instance privacy loss itself might be a function of sensitive data. In this paper, we analyze the per-instance privacy loss of releasing a private empirical risk minimizer learned via objective perturbation, and propose a group of methods to privately and accurately publish the pDP losses at little to no additional privacy cost.
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble

Offline reinforcement learning (offline RL), which aims to find an optimal policy from a previously collected static dataset, bears algorithmic difficulties due to function approximation errors from out-of-distribution (OOD) data points. To this end, offline RL algorithms adopt either a constraint or a penalty term that explicitly guides the policy to stay close to the given dataset. However, prior methods typically require accurate estimation of the behavior policy or sampling from OOD data points, which themselves can be a non-trivial problem. Moreover, these methods under-utilize the generalization ability of deep neural networks and often fall into suboptimal solutions too close to the given dataset. In this work, we propose an uncertainty-based offline RL method that takes into account the confidence of the Q-value prediction and does not require any estimation or sampling of the data distribution. We show that the clipped Q-learning, a technique widely used in online RL, can be leveraged to successfully penalize OOD data points with high prediction uncertainties. Surprisingly, we find that it is possible to substantially outperform existing offline RL methods on various tasks by simply increasing the number of Q-networks along with the clipped Q-learning. Based on this observation, we propose an ensemble-diversified actor-critic algorithm that reduces the number of required ensemble networks down to a tenth compared to the naive ensemble while achieving state-of-the-art performance on most of the D4RL benchmarks considered.
RL for Latent MDPs: Regret Guarantees and a Lower Bound

In this work, we consider the regret minimization problem for reinforcement learning in latent Markov Decision Processes (LMDP). In an LMDP, an MDP is randomly drawn from a set of $M$ possible MDPs at the beginning of the interaction, but the identity of the chosen MDP is not revealed to the agent. We first show that a general instance of LMDPs requires at least $\Omega((SA)^M)$ episodes to even approximate the optimal policy. Then, we consider sufficient assumptions under which learning good policies requires polynomial number of episodes. We show that the key link is a notion of separation between the MDP system dynamics. With sufficient separation, we provide an efficient algorithm with local guarantee, {\it i.e.,} providing a sublinear regret guarantee when we are given a good initialization. Finally, if we are given standard statistical sufficiency assumptions common in the Predictive State Representation (PSR) literature (e.g., Boots et al.) and a reachability assumption, we show that the need for initialization can be removed.
Nested Variational Inference

We develop nested variational inference (NVI), a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each level of nesting. NVI is applicable to many commonly-used importance sampling strategies and provides a mechanism for learning intermediate densities, which can serve as heuristics to guide the sampler. Our experiments apply NVI to (a) sample from a multimodal distribution using a learned annealing path (b) learn heuristics that approximate the likelihood of future observations in a hidden Markov model and (c) to perform amortized inference in hierarchical deep generative models. We observe that optimizing nested objectives leads to improved sample quality in terms of log average weight and effective sample size.
A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models

We present a scalable post-processing algorithm for debiasing trained models, including deep neural networks (DNNs), which we prove to be near-optimal by bounding its excess Bayes risk. We empirically validate its advantages on standard benchmark datasets across both classical algorithms as well as modern DNN architectures and demonstrate that it outperforms previous post-processing methods while performing on par with in-processing. In addition, we show that the proposed algorithm is particularly effective for models trained at scale where post-processing is a natural and practical choice.
Perceived and Intended Sarcasm Detection with Graph Attention Networks

Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and interpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user’s neighborhood in an interaction graph, to contextualize the interpretation of the post. We distinguish between perceived and self-reported sarcasm identification. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with various dense user history representations. Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our experiments indicate that the graph network contributes to interpreting the sarcastic intentions of the author more than to predicting the sarcasm perception by others.
Salient Features of Being a Young African and the Effects of Poor Leadership

Young Africans are broadly aware of the quality of life being experienced by their counterparts in the northern hemisphere. Consequently, they also desire to have the same comforts. Rising aspirations are good, and when met with opportunities they can lead to an explosion of dynamism among young people. This chapter critically documents the salient features of being young in Africa today by exploring those characteristics that intersect with age to define individual capabilities, opportunities, and the transition to adulthood. The chapter conceptualises the meaning of aspiration within the context of governance and what aspiration means for the young African. This is followed by a discussion of the pillars of governance. This discussion highlights the contribution of aspects of historical African culture in shaping modern day governance values and institutions. The chapter also critically evaluates approaches that are used to measure good governance and stresses that these existing metrics neither focus nor deliver for young people. The chapter concludes by detailing the challenges that young Africans grapple with as a result of a dislocation in political leadership and a mismatch between their aspirations and reality.
Language Models are Few-Shot Butlers

Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. Hence, these autoregressive models constitute ideal agents to operate in text-based environments where language understanding and generative capabilities are essential. Nonetheless, collecting expert demonstrations in such environments is a time-consuming endeavour. We introduce a two-stage procedure to learn from a small set of demonstrations and further improve by interacting with an environment. We show that language models fine-tuned with only 1.2% of the expert demonstrations and a simple reinforcement learning algorithm achieve a 51% absolute improvement in success rate over existing methods in the ALFWorld environment.
Rancang Bangun Sistem Informasi Rancang Bangusn Sistem Informasi Penilaian Kinerja Karyawan Menggunakan Metode Simple Additive Weighting (SAW) Dengan Pendekatan Psycological Appraisal (Studi Kasus : CV. Mutiara Feed Kampar): Penilaian Kinerja Karyawan

This system can assist the head of the company in managing employee performance, recapitulating all employee performance from each field of work for each individual. Based on the results that have been obtained from blackbox testing, all functions have been successfully executed and have followed the design that has been made. The UAT test results state that the system is 100% feasible to fulfill the test items being carried out.
Improved Pan-Private Stream Density Estimation.

Differential privacy is a rigorous definition for privacy that guarantees that any analysis performed on a sensitive dataset leaks no information about the individuals whose data are contained therein. In this work, we develop new differentially private algorithms to analyze streaming data. Specifically, we consider the problem of estimating the density of a stream of users (or, more generally, elements), which expresses the fraction of all users that actually appear in the stream. We focus on one of the strongest privacy guarantees for the streaming model, namely user-level pan-privacy, which ensures that the privacy of any user is protected, even against an adversary that observes, on rare occasions, the internal state of the algorithm. Our proposed algorithms employ optimally all the allocated privacy budget, are specially tailored for the streaming model, and, hence, outperform both theoretically and experimentally the conventional sampling-based approach.
Towards mental time travel: a hierarchical memory for reinforcement learning agents

Reinforcement learning agents often forget details of the past, especially after delays or distractor tasks. Agents with common memory architectures struggle to recall and integrate across multiple timesteps of a past event, or even to recall the details of a single timestep that is followed by distractor tasks. To address these limitations, we propose a Hierarchical Transformer Memory (HTM), which helps agents to remember the past in detail. HTM stores memories by dividing the past into chunks, and recalls by first performing high-level attention over coarse summaries of the chunks, and then performing detailed attention within only the most relevant chunks. An agent with HTM can therefore mentally time-travel -- remember past events in detail without attending to all intervening events. We show that agents with HTM substantially outperform agents with other memory architectures at tasks requiring long-term recall, retention, or reasoning over memory. These include recalling where an object is hidden in a 3D environment, rapidly learning to navigate efficiently in a new neighborhood, and rapidly learning and retaining new object names. Agents with HTM can extrapolate to task sequences an order of magnitude longer than they were trained on, and can even generalize zero-shot from a meta-learning setting to maintaining knowledge across episodes. HTM improves agent sample efficiency, generalization, and generality (by solving tasks that previously required specialized architectures). Our work is a step towards agents that can learn, interact, and adapt in complex and temporally-extended environments.