Reviewerly/exHarmony huggingface dataset:
The Challenge of Disproportionate Importance of Temporal Features in Predicting HPC Power Consumption

In this work, we demonstrate the challenges in predicting HPC cluster power consumption in the face of significant temporal skew in power consumption behavioral patterns. Predicting large power swings that extend several megawatts has significant operational value for HPC centers, however, prediction is challenging due to the relative rarity of such events and also due to the abrupt or disjoint deviation from the average power consumption levels. To study the impact of this challenge, we have trained a recurrent neural network (RNN) as a reasonably sophisticated model to predict power consumption of the one-year worth of node power consumption data from the Summit supercomputer located in the Oak Ridge Leadership Computing Facility. By studying the prediction results, we have found that although simple usage of RNN models can provide good results on average power consumption levels, it would fail at predicting the power swings that have more operational value. With such results, we discuss potential next steps in addressing such issues aiming towards a robust usage of power prediction techniques in HPC operations.
A Review on Essential Resources Utilized for Face Recognition

In facial identification tasks, face alignment is the most important aspect. Utilizing localization of various face landmarks for structural face normalization, in particular, has proven to be extremely reliable, significantly enhancing recognition performance. This article presents a survey on popular repositories such as BioID face repository, XM2VTS repository, BUHMAP-DB repository, MUCT face repository, and PUT face repository and their characteristics. Along with that, the tools are essential for building applications concerning facial recognition in real-world aspects. The popular part of applications for face recognition such as localization of landmarks, estimating the facial position, and Multi-directional viewable face recognition are also discussed. Of all the existing repositories, AFLW is highly popular due to the existence of a high number of faces in real-world aspects.
Uncertainty-based out-of-distribution detection requires suitable function space priors.

The need to avoid confident predictions on unfamiliar data has sparked interest in out-of-distribution (OOD) detection. It is widely assumed that Bayesian neural networks (BNNs) are well suited for this task, as the endowed epistemic uncertainty should lead to disagreement in predictions on outliers. In this paper, we question this assumption and show that proper Bayesian inference with function space priors induced by neural networks does not necessarily lead to good OOD detection. To circumvent the use of approximate inference, we start by studying the infinite-width case, where Bayesian inference can be exact due to the correspondence with Gaussian processes. Strikingly, the kernels induced under common architectural choices lead to uncertainties that do not reflect the underlying data generating process and are therefore unsuited for OOD detection. Importantly, we find this OOD behavior to be consistent with the corresponding finite-width networks. Desirable function space properties can be encoded in the prior in weight space, however, this currently only applies to a specified subset of the domain and thus does not inherently extend to OOD data. Finally, we argue that a trade-off between generalization and OOD capabilities might render the application of BNNs for OOD detection undesirable in practice. Overall, our study discloses fundamental problems when naively using BNNs for OOD detection and opens interesting avenues for future research.
Central Bank Digital Currencies and a Euro for the Future

Central bank digital currencies (CBDCs) have grasped the interest of central banks, policy makers, regulators, industry, and the general public, especially over the past year. The number of central banks actively engaging in CBDCs in one way or another, has increased from one third in 2018, to almost 90% as of March 2021.This report aims to provide a foundational understanding of CBDCs, the factors that necessitate their issuance, and an overview of the events leading up to relevant initiatives, public tests and live implementations. We focus on a European CBDC (henceforth also referred to as the ‘digital euro’) to explore the design space of its possible future implementation.
Machines Learning Models Based on Feature Selection and Bayesian Optimization for Predicting Daily Global Solar Radiation

Prediction of daily global solar radiation (H) with simple and high accurate models would be beneficial for solar energy conversion systems. In this paper, we proposed a hybrid machine learning methodology integrating two feature selection methods and a Bayesian optimization algorithm to predict  H   in the city of Fez, Morocco. First, we identified the most significant predictors using two methods of feature importance: Mean Decrease in Impurity (MDI) and Permutation Feature Importance (PFI). Then, based on the feature selection results, ten models were developed and compared: (1) five standalone machine learning models including Classification and Regression Trees (CART), Random Forests (RF), Bagged Trees Regression (BTR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP); and (2) the same models tuned by the Bayesian optimization (BO) algorithm: CART-BO, RF-BO, BTR-BO, SVR-BO, and MLP-BO. Both MDI and PFI techniques revealed that extraterrestrial solar radiation and sunshine duration fraction were the most influential features. The BO approach improved the predictive accuracy of MLP, CART, SVR, and BTR models and prevented the CART model from overfitting. Among the studied models, the SVR-BO algorithm provided the best trade-off between prediction accuracy (RMSE=0.4473kWh/m²/day, MAE=0.3381kWh/m²/day, and R²=0.9465), stability, and computational cost.
Using Multiple Reference Audios and Style Embedding Constraints for Speech Synthesis

The end-to-end speech synthesis model can directly take an utterance as reference audio, and generate speech from the text with prosody and speaker characteristics similar to the reference audio. However, an appropriate acoustic embedding must be manually selected during inference. Due to the fact that only the matched text and speech are used in the training process, using unmatched text and speech for inference would cause the model to synthesize speech with low content quality. In this study, we propose to mitigate these two problems by using multiple reference audios and style embedding constraints rather than using only the target audio. Multiple reference audios are automatically selected using the sentence similarity determined by Bidirectional Encoder Representations from Transformers (BERT). In addition, we use "target" style embedding from a pre-trained encoder as a constraint by considering the mutual information between the predicted and "target" style embedding. The experimental results show that the proposed model can improve the speech naturalness and content quality with multiple reference audios and can also outperform the baseline model in ABX preference tests of style similarity.
IMPORTANT FACTORS AFFECTING MODERN CLOUD TECHNOLOGIES USING IN EDUCATION

Cloud solutions are promising directions among modern technologies using in education. Manycompanies today are gradually moving their business processes including education business tothe cloud or preparing to move to it. Cloud technology was one of the ideas that first emerged inthe end of 1980s when scientists were talking about the concept of distance education. One of themain reasons for implementing this concept was the high cost of computers and software in thecase of using systems without using of cloud technologies in distance education. Therefore, theidea of connecting several employees to a common cloud processor at the same time wasconsidered. Implementation of cloud computer technologies began in 1990s, and the firsteconomically successful solution was released around 2000s. In the case of using cloudcomputer capabilities customers could purchase the software power needed to perform thecalculations. The second major factor influencing today’s clouds is the ability to connect to aglobal network for everyone in everywhere. The basic principle of this technology is that userscan access services from anywhere in the world.
Metric Learning For Context-Aware Recommender Systems

Context-Aware Recommender Systems (CARS) refer to recommender systems that can incorporate side information regarding to users, items and ratings. In the present study, we are concerned with CARS, where the side information is provided in the form of item-attribute matrix with entries indicating whether an item has an attribute. We propose to multiply this matrix with user-item rating matrix to represent the the users in the attribute space of the items. We then apply a popular metric learning method, specifically Mahalanobis Metric Learning (MMC), in the attribute space to calculate the distances between the users and their favorite items as less as possible. We recommend the n items that are closest to the users based on these calculations. We verify the effectiveness of the proposed method on two famous MovieLens datasets that differ in size showing that using metric learning increases the success of CARS up to 7% in comparison with using the traditional cosine similarity.
On the Computational Properties of Obviously Strategy-Proof Mechanisms

We present a polynomial-time algorithm that determines, given some choice rule, whether there exists an obviously strategy-proof mechanism for that choice rule.
Artificial Intelligence in Games

In modern video games, which often feature sophisticated and realistic worlds, the need for smart and comprehensive agents who understand the various aspects of complex environments is pressing. Since video game artificial intelligence (AI) is often customized for each game, video game AI tools are currently focused on assisting video game developers in creating custom AI quickly and easily. Player modeling (real-time facial emotion recognition, automated difficulty adaptation, and stealth assessment), natural language processing (NLP; sentiment analysis and essay scoring on free texts), and believable non-player characters (NPCs; emotional and socio-cultural, non-verbal bodily motion, and lip-synchronized speech) are all examples of serious game AI functionalities. Since these components can be reused, game developers can create high-quality serious games for less money and in less time. As a result, this chapter focuses on how the gaming world has adapted AI Technology.
An Account of Intensional and Extensional Actions, and its Application to Belief, Nondeterministic Actions and Fallible Sensors

In general, an agent may have incomplete and inaccurate knowledge about its environment. As well, actions may not turn out as intended or may have nondeterministic effects, and sensors may on occasion give incorrect results. We present a general, qualitative approach to reasoning about action and change in such a setting. The approach is expressed as an extension to basic action theories in the situation calculus, where an agent's epistemic state is modelled by a set of situations, where each situation is assigned a non-negative integer representing its plausibility. The agent's epistemic state is updated by modifying these plausibility values after the execution of an action, taking into account the possibility of unexpected results. To this end, we consider actions to have an intensional aspect, under the control of and determined by the agent, and an extensional aspect, not directly accessible to the agent and controlled by "nature". This leads to two distinct but related related notions of belief, an extensional "bird's eye" view which models an agent's beliefs wrt actually-executed actions, and an intensional view representing beliefs from the agent's point of view. We argue that the approach is significantly more general and comprehensive than previous accounts, and leads to a unified view of failed actions and nondeterminism with respect to physical and sensing actions.
RAN-GNNs: Breaking the Capacity Limits of Graph Neural Networks

Graph neural networks (GNNs) have become a staple in problems addressing learning and analysis of data defined over graphs. However, several results suggest an inherent difficulty in extracting better performance by increasing the number of layers. Recent works attribute this to a phenomenon peculiar to the extraction of node features in graph-based tasks, i.e., the need to consider multiple neighborhood sizes at the same time and adaptively tune them. In this article, we investigate the recently proposed randomly wired architectures in the context of GNNs. Instead of building deeper networks by stacking many layers, we prove that employing a randomly wired architecture can be a more effective way to increase the capacity of the network and obtain richer representations. We show that such architectures behave like an ensemble of paths, which are able to merge contributions from receptive fields of varied size. Moreover, these receptive fields can also be modulated to be wider or narrower through the trainable weights over the paths. We also provide extensive experimental evidence of the superior performance of randomly wired architectures over multiple tasks and five graph convolution definitions, using recent benchmarking frameworks that address the reliability of previous testing methodologies.
A Bayes-Optimal View on Adversarial Examples

Since the discovery of adversarial examples - the ability to fool modern CNN classifiers with tiny perturbations of the input, there has been much discussion whether they are a bug that is specific to current neural architectures and training methods or an inevitable feature of high dimensional geometry. In this paper, we argue for examining adversarial examples from the perspective of Bayes-Optimal classification. We construct realistic image datasets for which the Bayes-Optimal classifier can be efficiently computed and derive analytic conditions on the distributions under which these classifiers are provably robust against any adversarial attack even in high dimensions. Our results show that even when these gold standard optimal classifiers are robust, CNNs trained on the same datasets consistently learn a vulnerable classifier, indicating that adversarial examples are often an avoidable bug. We further show that RBF SVMs trained on the same data consistently learn a robust classifier. The same trend is observed in experiments with real images in different datasets.
Computing Irregular Component Spacing of Digital Ink Chinese Characters Handwritten by Junior International Students

There are many irregular component problems in digital ink Chinese characters handwritten by junior international students. The irregular component spacing is one of them. It is necessary to compute the irregularity based on reference characters. This paper presents one quantitative computing approach based on the database of reference Chinese characters' component spacing. The database provides heuristic information for component extraction of digital ink Chinese characters, rule selection for component spacing calculation, and reference data for computing irregularity. Digital ink is used to collect handwritten Chinese characters because of its natural writing and rich information. The automatic extraction of digital ink Chinese character components is realized by reading the heuristic information from the database, and the interactive correction is also provided. Taking the components as a whole, the boundary distances between each component are calculated. Based on the information of reference Chinese character component spacing, the difference and ratio of the component spacing between digital ink characters and reference ones are calculated. This not only provides correction data, but also indicates the severity of the irregularity. The experimental data includes six categories of Chinese character structures, and each structure includes six examples. The calculation results can be used for statistical analysis and rule discovery, which provides data support for the study of Chinese character acquisition.
Towards Quantum Large-Scale Password Guessing on Real-World Distributions

Password-based authentication is a central tool for end-user security. As part of this, password hashing is used to ensure the security of passwords at rest. If quantum computers become available at sufficient size, they are able to significantly speed up the computation of preimages of hash functions. Using Grover's algorithm, at most, a square-root speedup can be achieved, and thus it is expected that quantum password guessing also admits a square-root speedup. However, password inputs are not uniformly distributed but highly biased. Moreover, typical password attacks do not only compromise a random user's password but address a large fraction of all users' passwords within a database of millions of users.
Contrastive Representations Pre-Training for Enhanced Discharge Summary BERT

Recently BERT has shown tremendous improvement in performance for various NLP tasks. BERT has been applied to many domains including biomedical field. Especially clinical domain, the semantic relationship between sentences is very important to understand patient’s medical record and health history in physical examination. However, in current Clinical BERT model, the pre-training method is difficult to capture sentence level semantics. To address this problem, we propose a contrastive representations pre-training (CRPT), which can enhance contextual meanings between sentences by replacing cross-entropy loss to contrastive loss in next sentence prediction (NSP) task. Also we tried to improve the performance by changing random masking technique to whole word masking (WWM) for masked language model (MLM). Especially, we focus on enhancing language representations of BERT model by pretraining with discharge summaries to optimize clinical studies. We demonstrate that our CRPT strategy yields performance improvements on clinical NLP task in BLUE (Biomedical Language Understanding Evaluation) Benchmark dataset.
Improving the Visual Quality of Video Frame Prediction Models Using the Perceptual Straightening Hypothesis

We present a simple and effective method to improve the visual quality of the predicted frames in a frame prediction model. A recent neuroscience study hypothesizes that the perceptual representations of a sequence of frames extracted from a natural video follow a straight temporal trajectory. The perceptual representations of a sequence of video frames are found using a computational model of the LGN and V1 areas of the human visual system. In this work, we leverage the strength of this perceptual straightening model to formulate a novel objective function for video frame prediction. In general, a frame prediction model takes past frames as input and predicts the future frame. We enforce the perceptual straightness constraint through adversarial training by introducing the proposed novel quality aware discriminator loss. Our quality aware discriminator imposes the linear relationship between the perceptual representation of the predicted frame and the perceptual representations of the past frames. Specifically, we claim that imposing a perceptual straightness constraint through the discriminator helps in predicting (i.e., generating) video frames that look more natural and therefore, having a higher perceptual quality. We demonstrate the effectiveness of our proposed objective function on two popular video datasets using two different frame prediction models. These experiments show that our solution is both consistent and stable, thereby allowing it to be integrated with other frame prediction models as well.
Surface Normals and Shape From Water

In this paper, we introduce a novel method for reconstructing surface normals and depth of dynamic objects in water. Past shape recovery methods have leveraged various visual cues for estimating shape (e.g., depth) or surface normals. Methods that estimate both compute one from the other. We show that these two geometric surface properties can be simultaneously recovered for each pixel when the object is observed underwater. Our key idea is to leverage multi-wavelength near-infrared light absorption along different underwater light paths in conjunction with surface shading. Our method can handle both Lambertian and non-Lambertian surfaces. We derive a principled theory for this surface normals and shape from water method and a practical calibration method for determining its imaging parameters values. By construction, the method can be implemented as a one-shot imaging system. We prototype both an off-line and a video-rate imaging system and demonstrate the effectiveness of the method on a number of real-world static and dynamic objects. The results show that the method can recover intricate surface features that are otherwise inaccessible.
SEOUL AR: Designing a Mobile AR Tour Application for Seoul Sky Observatory in South Korea

Skyscrapers are symbols of local landmarks, and their prevalence is increasing across the world owing to recent advances in architectural technology. In Korea, the Lotte World Tower, which is now the tallest skyscraper in Seoul, was constructed in 2017. In addition, it has an observatory deck called Seoul Sky, which is currently in operation. This study focuses on the design of Seoul AR, which is a mobile augmented reality (AR) tour application. Visitors can use Seoul AR when visiting the Seoul Sky Observatory, one of the representative landmarks of Seoul, and enjoy a 360° view of the entire landscape of Seoul in the observatory space. With Seoul AR, they can identify tourist attractions in Seoul with simple mission games. Users are also provided with information regarding the specific attraction they are viewing, as well as other information on transportation, popular restaurants, shopping places, etc., in order to increase the level of satisfaction of tourists visiting the Seoul Sky Observatory. The final design is revised through heuristic evaluation, and a study of users’ levels of satisfaction with Seoul AR is conducted through surveys completed by visitors to the Seoul Sky Observatory.
No-reference video quality assessment for user generated content based on deep network and visual perception

Video quality assessment (VQA) is an important technique in video service systems. In recent years, the development of deep learning has provided further possibilities for VQA. A no-reference VQA (NR-VQA) method that combines the attention mechanism and human visual perception is proposed for in-the-wild videos. First, a deep network consisting of a convolutional neural network and attention mechanism is constructed to extract depth perception features for frame-level images, and global covariance pooling is integrated into the downsampled features to extract the second-order information of the features. Second, a Transformer network is used for temporal modeling to learn the long-term dependence of the perceptual quality prediction. Finally, a temporal weighting strategy for visual perception is used for weighted summation of the frame-level scores to obtain the final video quality scores. The results of experiments on three public user-generated content authentic distorted video databases, namely KoNViD-1k, CVD2014, and LIVE-VQC, demonstrate that the proposed method can achieve effective quality assessment in authentic distortion and outperforms other partially recent NR-VQA methods.