Reviewerly/exHarmony huggingface dataset:
Reliable Actors with Retry Orchestration

Cloud developers have to build applications that are resilient to failures and interruptions. We advocate for a fault-tolerant programming model for the cloud based on actors, retry orchestration, and tail calls. This model builds upon persistent data stores and messages queues readily available on the cloud. Retry orchestration not only guarantees that (1) failed actor invocations will be retried but also that (2) completed invocations are never repeated and (3) it preserves a strict happen-before relationship across failures within call stacks. Tail calls can break complex tasks into simple steps to minimize re-execution during recovery. We review key application patterns and failure scenarios. We formalize a process calculus to precisely capture the mechanisms of fault tolerance in this model. We briefly describe our implementation. Using an application inspired by a typical enterprise scenario, we validate the functional correctness of our implementation and assess the impact of fault preparedness and recovery on performance.
The main methods of introducing design elements and graphics in video

The subject of the study is the process of many aspects of audiovisual creativity that must be taken into account when creating a video. The object of the work is to create a movie trailer through the use of design elements and graphics in the video sequence. The study examines the trailer of a post-apocalyptic fantasy genre called "Genesis", in which the two main characters are depicted as opposites. The work examines and describes in detail all the stages of creating a video trailer for the film, including visual representation (rendering the texture of the central objects, choosing a font to display the title of the film, controlling the movement of actors and moving frames), audio accompaniment of the video sequence (music, voiceover, sound effects). The paper notes that the main goal of all the stages of trailer development considered in the article is to create an audiovisual project of professional quality. The main methods used in the study are visual effects modeling and 3D modeling in Houdini, creating realistic matte paintings in Photoshop and video compositing in Nuke. The main conclusions of the study are those specialized programs that allow you to create a high-quality author's product. This software will become the core of the project, since it is in it that all the elements will be combined into one final clip due to the high-quality integration of various objects of the scene. The novelty of the research lies in the fact that within the framework of the work, the characteristics of the possibilities of working with various video editors and graphic programs were carried out, which together help the creators of movie trailers and other similar products to realize the tasks of entertainment and quality of the projects produced.
Graph Summarization with Graph Neural Networks

The goal of graph summarization is to represent large graphs in a structured and compact way. A graph summary based on equivalence classes preserves pre-defined features of a graph's vertex within a $k$-hop neighborhood such as the vertex labels and edge labels. Based on these neighborhood characteristics, the vertex is assigned to an equivalence class. The calculation of the assigned equivalence class must be a permutation invariant operation on the pre-defined features. This is achieved by sorting on the feature values, e. g., the edge labels, which is computationally expensive, and subsequently hashing the result. Graph Neural Networks (GNN) fulfill the permutation invariance requirement. We formulate the problem of graph summarization as a subgraph classification task on the root vertex of the $k$-hop neighborhood. We adapt different GNN architectures, both based on the popular message-passing protocol and alternative approaches, to perform the structural graph summarization task. We compare different GNNs with a standard multi-layer perceptron (MLP) and Bloom filter as non-neural method. For our experiments, we consider four popular graph summary models on a large web graph. This resembles challenging multi-class vertex classification tasks with the numbers of classes ranging from $576$ to multiple hundreds of thousands. Our results show that the performance of GNNs are close to each other. In three out of four experiments, the non-message-passing GraphMLP model outperforms the other GNNs. The performance of the standard MLP is extraordinary good, especially in the presence of many classes. Finally, the Bloom filter outperforms all neural architectures by a large margin, except for the dataset with the fewest number of $576$ classes.
Programa de estrategias lúdicas corporales para potenciar la educación emocional en los niños de 4 años

The objective of this research was to design a program of corporal ludic strategies to promote emotional education in 4-year-old children of the Benjamín Rosales school in the city of Guayaquil. A quantitative approach methodology, propositional correlational type, non-experimental design was used. For the diagnosis, a questionnaire was applied to 12 individuals and an unstructured interview was carried out to determine the play strategies used by the teachers. The result is a program based on corporal play strategies to potentiate emotional education in children, based on Piaget's (1998) theory of creative strategies, reaching the conclusion that the use of the program will allow strengthening the relationship between the different needs to improve education by means of playful games that favor intellectual and motor development.
Group-based Cryptography in the Quantum Era

In this expository article we present an overview of the current state-of-the-art in post-quantum group-based cryptography. We describe several families of groups that have been proposed as platforms, with special emphasis in polycyclic groups and graph groups, dealing in particular with their algorithmic properties and cryptographic applications. We then, describe some applications of combinatorial algebra in fully homomorphic encryption. In the end we discussing several open problems in this direction.
Performance Measurement of Security Academic Information System using Maturity Level

This study aims to information security in academic information systems to provide recommendations for improvements in information security management by the expected maturity level based on ISO/IEC 27002:2013. By using a qualitative descriptive approach, data collection and validation techniques with triangulation techniques are interviews, observation, and documentation. The data were analyzed by using gap analysis and to measure the maturity level determined 15 objective control and 45 security controls scattered in 5 clauses, the result of the research found that the performance of academic information system maturity level at level 2. That is, the current level of maturity is below the expected maturity level, so it needs to be increased to the expected level.
On computing with some convex relaxations for the maximum-entropy sampling problem

Based on a factorization of an input covariance matrix, we define a mild generalization of an upper bound of Nikolov (2015) and Li and Xie (2020) for the NP-Hard constrained maximum-entropy sampling problem (CMESP). We demonstrate that this factorization bound is invariant under scaling and also independent of the particular factorization chosen. We give a variable-fixing methodology that could be used in a branch-and-bound scheme based on the factorization bound for exact solution of CMESP. We report on successful experiments with a commercial nonlinear-programming solver. We further demonstrate that the known "mixing" technique can be successfully used to combine the factorization bound with the factorization bound of the complementary CMESP, and also with the "linx bound" of Anstreicher (2020).
PSTR: End-to-End One-Step Person Search With Transformers

We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture. PSTR comprises a person search-specialized (PSS) module that contains a detection encoder-decoder for person detection along with a discriminative re-id decoder for person re-id. The discriminative re-id decoder utilizes a multi-level supervision scheme with a shared decoder for discriminative re-id feature learning and also comprises a part attention block to encode relationship between different parts of a person. We further introduce a simple multi-scale scheme to support re-id across person instances at different scales. PSTR jointly achieves the diverse objectives of object-level recognition (detection) and instance-level matching (re-id). To the best of our knowledge, we are the first to propose an end-to-end one-step transformer-based person search framework. Experiments are performed on two popular benchmarks: CUHK-SYSU and PRW. Our extensive ablations reveal the merits of the proposed contributions. Further, the proposed PSTR sets a new state-of-the-art on both benchmarks. On the challenging PRW benchmark, PSTR achieves a mean average precision (mAP) score of 56.5%. The source code is available at \url{https://github.com/JialeCao001/PSTR}.
DARTS without a Validation Set: Optimizing the Marginal Likelihood

The success of neural architecture search (NAS) has historically been limited by excessive compute requirements. While modern weight-sharing NAS methods such as DARTS are able to finish the search in single-digit GPU days, extracting the final best architecture from the shared weights is notoriously unreliable. Training-Speed-Estimate (TSE), a recently developed generalization estimator with a Bayesian marginal likelihood interpretation, has previously been used in place of the validation loss for gradient-based optimization in DARTS. This prevents the DARTS skip connection collapse, which significantly improves performance on NASBench-201 and the original DARTS search space. We extend those results by applying various DARTS diagnostics and show several unusual behaviors arising from not using a validation set. Furthermore, our experiments yield concrete examples of the depth gap and topology selection in DARTS having a strongly negative impact on the search performance despite generally receiving limited attention in the literature compared to the operations selection.
Exact Gravity Duals for Simple Quantum Circuits

Holographic complexity proposals have sparked interest in quantifying the cost of state preparation in quantum field theories and its possible dual gravitational manifestations. The most basic ingredient in defining complexity is the notion of a class of circuits that, when acting on a given reference state, all produce a desired target state. In the present work we build on studies of circuits performing local conformal transformations in general two-dimensional conformal field theories and construct the exact gravity dual to such circuits. In our approach to holographic complexity, the gravity dual to the optimal circuit is the one that minimizes an externally chosen cost assigned to each circuit. Our results provide a basis for studying exact gravity duals to circuit costs from first principles.
Differentially Private Regret Minimization in Episodic Markov Decision Processes

We study regret minimization in finite horizon tabular Markov decision processes (MDPs) under the constraints of differential privacy (DP). This is motivated by the widespread applications of reinforcement learning (RL) in real-world sequential decision making problems, where protecting users' sensitive and private information is becoming paramount. We consider two variants of DP -- joint DP (JDP), where a centralized agent is responsible for protecting users' sensitive data and local DP (LDP), where information needs to be protected directly on the user side. We first propose two general frameworks -- one for policy optimization and another for value iteration -- for designing private, optimistic RL algorithms. We then instantiate these frameworks with suitable privacy mechanisms to satisfy JDP and LDP requirements, and simultaneously obtain sublinear regret guarantees. The regret bounds show that under JDP, the cost of privacy is only a lower order additive term, while for a stronger privacy protection under LDP, the cost suffered is multiplicative. Finally, the regret bounds are obtained by a unified analysis, which, we believe, can be extended beyond tabular MDPs.
Problems of the university teaching transition to e-learning

The article discusses the higher educational institutions transition problems to a distance learning operation mode during the pandemic, taking into account the technical universities work specifics. The article describes the problems that have arisen as a result of e-learning mode transition, as well as some solutions to these problems. The article focuses on: the teachers’ readiness problems; the e-courses quality for technical specialities students; the teachers’ developed materials copyright; virtual learning environments to replace the laboratory stands and equipment for students in distance learning process; distance learning educational process implementation technical support problems. The described problems’ proposed solutions’ implementation can be carried out in higher educational institutions without hiring additional personnel.
Eluding Secure Aggregation in Federated Learning via Model Inconsistency

Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of secure aggregation prevents the server from learning the value and the source of the individual model updates provided by the users, hampering inference and data attribution attacks. In this work, we show that a malicious server can easily elude secure aggregation as if the latter were not in place. We devise two different attacks capable of inferring information on individual private training datasets, independently of the number of users participating in the secure aggregation. This makes them concrete threats in large-scale, real-world federated learning applications. The attacks are generic and equally effective regardless of the secure aggregation protocol used. They exploit a vulnerability of the federated learning protocol caused by incorrect usage of secure aggregation and lack of parameter validation. Our work demonstrates that current implementations of federated learning with secure aggregation offer only a "false sense of security".
Pseudocylindrical Convolutions for Learned Omnidirectional Image Compression

Although equirectangular projection (ERP) is a convenient form to store omnidirectional images (also known as 360-degree images), it is neither equal-area nor conformal, thus not friendly to subsequent visual communication. In the context of image compression, ERP will over-sample and deform things and stuff near the poles, making it difficult for perceptually optimal bit allocation. In conventional 360-degree image compression, techniques such as region-wise packing and tiled representation are introduced to alleviate the over-sampling problem, achieving limited success. In this paper, we make one of the first attempts to learn deep neural networks for omnidirectional image compression. We first describe parametric pseudocylindrical representation as a generalization of common pseudocylindrical map projections. A computationally tractable greedy method is presented to determine the (sub)-optimal configuration of the pseudocylindrical representation in terms of a novel proxy objective for rate-distortion performance. We then propose pseudocylindrical convolutions for 360-degree image compression. Under reasonable constraints on the parametric representation, the pseudocylindrical convolution can be efficiently implemented by standard convolution with the so-called pseudocylindrical padding. To demonstrate the feasibility of our idea, we implement an end-to-end 360-degree image compression system, consisting of the learned pseudocylindrical representation, an analysis transform, a non-uniform quantizer, a synthesis transform, and an entropy model. Experimental results on $19,790$ omnidirectional images show that our method achieves consistently better rate-distortion performance than the competing methods. Moreover, the visual quality by our method is significantly improved for all images at all bitrates.
Connecting sufficient conditions for domain adaptation: source-guided uncertainty, relaxed divergences and discrepancy localization

Recent advances in domain adaptation establish that requiring a low risk on the source domain and equal feature marginals degrade the adaptation's performance. At the same time, empirical evidence shows that incorporating an unsupervised target domain term that pushes decision boundaries away from the high-density regions, along with relaxed alignment, improves adaptation. In this paper, we theoretically justify such observations via a new bound on the target risk, and we connect two notions of relaxation for divergence, namely $\beta-$relaxed divergences and localization. This connection allows us to incorporate the source domain's categorical structure into the relaxation of the considered divergence, provably resulting in a better handling of the label shift case in particular.
Tight Wavelet Filter Banks with Prescribed Directions

Constructing tight wavelet filter banks with prescribed directions is challenging. This paper presents a systematic method for designing a tight wavelet filter bank, given any prescribed directions. There are two types of wavelet filters in our tight wavelet filter bank. One type is entirely determined by the prescribed information about the directionality and makes the wavelet filter bank directional. The other type helps the wavelet filter bank to be tight. In addition to the flexibility in choosing the directions, our construction method has other useful properties. It works for any multi-dimension, and it allows the user to have any prescribed number of vanishing moments along the chosen directions. Furthermore, our tight wavelet filter banks have fast algorithms for analysis and synthesis. Concrete examples are given to illustrate our construction method and properties of resulting tight wavelet filter banks.
Implicit Neural Video Compression

We propose a method to compress full-resolution video sequences with implicit neural representations. Each frame is represented as a neural network that maps coordinate positions to pixel values. We use a separate implicit network to modulate the coordinate inputs, which enables efficient motion compensation between frames. Together with a small residual network, this allows us to efficiently compress P-frames relative to the previous frame. We further lower the bitrate by storing the network weights with learned integer quantization. Our method, which we call implicit pixel flow (IPF), offers several simplifications over established neural video codecs: it does not require the receiver to have access to a pretrained neural network, does not use expensive interpolation-based warping operations, and does not require a separate training dataset. We demonstrate the feasibility of neural implicit compression on image and video data.
Lite Vision Transformer with Enhanced Self-Attention

Despite the impressive representation capacity of vision transformer models, current light-weight vision transformer models still suffer from inconsistent and incorrect dense predictions at local regions. We suspect that the power of their self-attention mechanism is limited in shallower and thinner networks. We propose Lite Vision Transformer (LVT), a novel light-weight transformer network with two enhanced self-attention mechanisms to improve the model performances for mobile deployment. For the low-level features, we introduce Convolutional Self-Attention (CSA). Unlike previous approaches of merging convolution and self-attention, CSA introduces local self-attention into the convolution within a kernel of size 3x3 to enrich low-level features in the first stage of LVT. For the high-level features, we propose Recursive Atrous Self-Attention (RASA), which utilizes the multi-scale context when calculating the similarity map and a recursive mechanism to increase the representation capability with marginal extra parameter cost. The superiority of LVT is demonstrated on ImageNet recognition, ADE20K semantic segmentation, and COCO panoptic segmentation. The code is made publicly available.
Reinforcement Learning with Feedback from Multiple Humans with Diverse  Skills

A promising approach to improve the robustness and exploration in Reinforcement Learning is collecting human feedback and that way incorporating prior knowledge of the target environment. It is, however, often too expensive to obtain enough feedback of good quality. To mitigate the issue, we aim to rely on a group of multiple experts (and non-experts) with different skill levels to generate enough feedback. Such feedback can therefore be inconsistent and infrequent. In this paper, we build upon prior work -- Advise, a Bayesian approach attempting to maximise the information gained from human feedback -- extending the algorithm to accept feedback from this larger group of humans, the trainers, while also estimating each trainer's reliability. We show how aggregating feedback from multiple trainers improves the total feedback's accuracy and make the collection process easier in two ways. Firstly, this approach addresses the case of some of the trainers being adversarial. Secondly, having access to the information about each trainer reliability provides a second layer of robustness and offers valuable information for people managing the whole system to improve the overall trust in the system. It offers an actionable tool for improving the feedback collection process or modifying the reward function design if needed. We empirically show that our approach can accurately learn the reliability of each trainer correctly and use it to maximise the information gained from the multiple trainers' feedback, even if some of the sources are adversarial.
Managing the pedagogical culture of university teachers in the context of the impact of COVID-19

The main purpose of the article is to study the features of the pedagogical culture of university teachers in the context of the impact of COVID-19. New trends in education that emerged at the turn of the second and third millennia, the emergence of new paradigms of education, a new system of social values and goals of education, its dialogue with human culture as its creator and subject capable of cultural self-education, stimulated a special interest in the study of the phenomenon of "pedagogical culture" and its components. The events taking place in the world as a result of the spread of the coronavirus infection COVID-19 refer to a global emergency of a nature. Of course, in these forced conditions, it is difficult for teachers to simultaneously master new methods and tools of distance learning, develop methodological support for the disciplines taught, or transform it into a digital form and immediately introduce it into the educational process. As a result of the study, the main trends in the development of modern pedagogical culture of university teachers in the context of the impact of COVID-19 were identified.