Reviewerly/exHarmony huggingface dataset:
Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization

This paper focuses on stochastic proximal gradient methods for optimizing a smooth non-convex loss function with a non-smooth non-convex regularizer and convex constraints. To the best of our knowledge we present the first non-asymptotic convergence results for this class of problem. We present two simple stochastic proximal gradient algorithms, for general stochastic and finite-sum optimization problems, which have the same or superior convergence complexities compared to the current best results for the unconstrained problem setting. In a numerical experiment we compare our algorithms with the current state-of-the-art deterministic algorithm and find our algorithms to exhibit superior convergence.
Multifaceted Domain-Specific Document Embeddings

Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms.Further, they transform each document into a single embedding vector, making it hard to capture different notions of document similarity or explain why two documents are considered similar.In this work, we propose our Faceted Domain Encoder, a novel approach to learn multifaceted embeddings for domain-specific documents.It is based on a Siamese neural network architecture and leverages knowledge graphs to further enhance the embeddings even if only a few training samples are available.The model identifies different types of domain knowledge and encodes them into separate dimensions of the embedding, thereby enabling multiple ways of finding and comparing related documents in the vector space.We evaluate our approach on two benchmark datasets and find that it achieves the same embedding quality as state-of-the-art models while requiring only a tiny fraction of their training data.
Information and communication technologies as resources of inclusive education

Informatization is one of the stages in the development of modern society. This process is characterized by the increasing role of education in all spheres of human activity and the increasing importance of artificial intelligence and communications. One of the key areas of informatization is the addition of education to new theories and practices that are aimed at using advanced information technologies for the purpose of teaching and teaching the modern generation. Information and communication technologies (ICT) are a form of informatization. On the other hand, now they are actively being introduced into the educational process as teaching aids. The successful implementation of informatization is largely determined by the human and material potential of educational institutions and the degree of teacher training. The process of introducing ICT into practice is chaotic, but today there is a pattern in it, and researchers around the world find and explore it.
Organization Of Students’ Interactive Communication In A Webinar.

The modern paradigm of pedagogy development is aimed to foster not only a trained and intellectual member of society but also to create a comfortable environment to contribute to their development, social and professional formation.This article is devoted to the study of students' interactive communication phenomenon in the conditions of information educational environment, as well as its implementation in the modern educational process, and particularly -during webinars.In the work, such conceptual definitions as "interactive information environment", "educational environment", "educational process specifics", "interactive communication", etc. are used.Within the framework of the study, the specifics of these definitions were considered, their most important features in the context of the chosen topic were identified.The authors believe that webinars implemented through information educational environments have great potential for students' interactive communication organization both within the educational process and in extracurricular activities.The use of information educational environments significantly changes the quality of educational process, allows to implement blended learning.Information educational environments support synchronous and asynchronous modes of interaction between educational process subjects.The article provides a description of the research aimed at identifying the advantages and disadvantages of information educational environments in the educational process and identifying the pedagogical conditions for the organization of students' interactive communication during webinars.The questionnaire questions were aimed at studying students' satisfaction with the capacities of information environments used in the educational process and the level of their readiness for interactive communication in the conditions of information educational environments.
Creating a TRNSYS Model of a Spiral Flow Flat Plate Solar Water Heater (SFSWH) and Comparingit to a Parallel Flow Flat Plate Collector: A Recent Study

A specific SFSWH system for domestic hot water is proposed in Kovilpatti, Tamilnadu, 9°10'N 77°52'E. The results were clarified using parallel flow solar water heaters as a contrast after this SFSWH device was successfully simulated in TRNSYS Software. To begin, a complete model of a spiral flow flat plate collector is created in TRNSYS software, accounting for heat transfer, outlet temperature, and performance. This model is then theoretically compared to a parallel flow flat plate collector using the TRNSYS simulation programme. With the aid of NASA surface meteorology and solar energy year readings, as well as ISRO's solar calculator application, the SFSWH device performs a role relative to Kovilpatti, Tamilnadu, India. An experimental setup was done at Kovilpatti to prove the accuracy of the TRNSYS model of the SFSWH, and necessary results were obtained under Kovilpatti weather conditions. The validation of this SFSWH TRNSYS model simulation programme is completed by comparing the expected software results with the original results. The SFSWH TRNSYS model result shows that the SFSWH performs better than a traditional parallel flow water heater. As a consequence, the TRNSYS model is dependable, and it alternates the experimentation.
3. Automated personalization of input methods and processes

Personalization, aiming at supporting users individually and according to their individual needs and prerequisites, has been discussed in a number of domains, including learning, searching, or information retrieval. In the field of human- computer interaction, personalization also bears high potential as users might exhibit varying and strongly individual preferences and abilities related to interaction. For instance, users with certain kinds of motor impairments might not be able to use certain input devices and methods, such as touchscreens and touch-based interaction. At least a high amount of time consuming individual configuration typically arises. Further, interaction preferences might also vary among people without known impairments. Thus, personalized interaction, taking into account these prerequisites, might offer individualized support and solutions to potential problems. Personalized interaction involves automated selection and configuration of input devices but also adaptation of applications and user interfaces. This chapter discusses personalized interaction generally and presents a software framework that provides a template for a feasible technical infrastructure. Further, it explains a specific case study of personalized interaction that was implemented on the basis of the framework, and discusses an evaluation process and results for this use case.
Riemannian Convex Potential Maps

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited by representational and computational tradeoffs. We propose and study a class of flows that uses convex potentials from Riemannian optimal transport. These are universal and can model distributions on any compact Riemannian manifold without requiring domain knowledge of the manifold to be integrated into the architecture. We demonstrate that these flows can model standard distributions on spheres, and tori, on synthetic and geological data. Our source code is freely available online at this http URL
Сочетание вручную построенных признаков и признаков глубоких сетей для автоматического обнаружеия дефектов в процессе визуализации квадратичной частотно-модулированной тепловой волны

Recent advancements of non-destructive testing and evaluation (NDT&amp;E) reached the fourth revolution with machine learning, artificial intelligence, and the internet of things as key enablers in parallel with industry 4.0. Nevertheless, Active thermography (AT) is a non-contact, whole field, safe, remote, cost-efficient, and widely used NDT technique for subsurface anomaly detection. In AT, the automatic defect detection is modelled as object localization and semantic segmentation in thermograms. This paper presents a feature fusion network that fuses the global features extracted using a deep neural network (DNN) with the deep features extracted using a convolutional neural network (CNN). A set of handcrafted time-domain statistical and frequency domain features of thermal profiles are given to the DNN sub-network whereas, the CNN sub-network is fed with the thermal profiles in the feature fusion network. Experimentation is carried out over carbon fiber reinforced polymer (CFRP) sample with artificially drilled flat bottom holes excited by quadratic frequency-modulated optical stimulus. Experimental results showed that the feature fusion enhanced the defect detection capability compared to the local networks with a significant increment in signal-to-noise ratio, accuracy, and F-score.
What Makes for End-to-End Object Detection?

Object detection has recently achieved a breakthrough for removing the last one non-differentiable component in the pipeline, Non-Maximum Suppression (NMS), and building up an end-to-end system. However, what makes for its one-to-one prediction has not been well understood. In this paper, we first point out that one-to-one positive sample assignment is the key factor, while, one-to-many assignment in previous detectors causes redundant predictions in inference. Second, we surprisingly find that even training with one-to-one assignment, previous detectors still produce redundant predictions. We identify that classification cost in matching cost is the main ingredient: (1) previous detectors only consider location cost, (2) by additionally introducing classification cost, previous detectors immediately produce one-to-one prediction during inference. We introduce the concept of score gap to explore the effect of matching cost. Classification cost enlarges the score gap by choosing positive samples as those of highest score in the training iteration and reducing noisy positive samples brought by only location cost. Finally, we demonstrate the advantages of end-to-end object detection on crowded scenes. The code is available at: \url{this https URL}.
HOW DOES TELEWORK ACT AS A SOLUTION TO THE PUBLIC SECTOR IN THE TIME OF PANDEMIC?

As Coronavirus COVID 19 became a global pandemic, and its repercussions have affected the global economy badly, teleworking was being forced as an alternative pioneer to be implemented in the public sector. This study will discuss adapting Telework as a solution in crisis times in developing countries (Jordan). Systematic Literature Review was adopted to explore the challenges and best practices for adopting Telework in the Public Sector, especially during the crisis. Afterward, interviews were conducted with experts, professionals, and academics from Jordan to discuss the best solutions for adapting Telework in the Jordanian Public Sector. This paper has found several challenges facing the adoption of Telework in the public sector, and they were mainly related to Security, Infrastructure, and Technological Illiteracy. Moreover, this paper has also suggested solutions through the interviews. The most prominent solutions focused on finding a legal framework for the Telework, providing appropriate infrastructure and equipment for the employees to work remotely, and offering frequent technical support for the teleworkers to ensure the Data's security and confidentiality. There was a lack of studies related to Telework in the public sector by reviewing the literature, especially in developing countries. This paper will be a reference for other researchers to explore the challenges and the solutions.
Batik sales product recommendation system on E-Commerce Website using FP-Growth Algorithm

espanolEl estudio tiene como objetivo investigar el sistema de recomendacion de productos de ventas de Batik en el sitio web de comercio electronico utilizando el algoritmo FP-Growth. En este caso, la venta de tela batik se basa en el sitio web. El sistema del sitio web esta hecho en el lenguaje de programacion PHP. Como resultado, el proceso de implementacion de recomendaciones de productos que pueden clasificarse como un producto mas vendido, el producto aparecera directamente en la pagina principal del sitio web, las recomendaciones del producto cambiaran segun las necesidades. A partir de esta investigacion, se puede concluir que se puede implementar la aplicacion de un sistema de recomendacion de productos en sitios web de comercio electronico que venden batik. EnglishThe study aims to investigate the Batik sales product recommendation system on E-Commerce Website using the FP-Growth Algorithm. In this case, the sale of batik cloth is websitebased. The website system is made in the PHP programming language. As a result, the process of implementing product recommendations that can be categorized as a best seller product, the product will appear directly on the main page of the website, product recommendations will change according to needs. From this research, it can be concluded that the application of a product recommendation system on e-commerce websites selling batik can be implemented.
Exploiting Shared Representations for Personalized Federated Learning

Deep neural networks have shown the ability to extract universal feature representations from data such as images and text that have been useful for a variety of learning tasks. However, the fruits of representation learning have yet to be fully-realized in federated settings. Although data in federated settings is often non-i.i.d. across clients, the success of centralized deep learning suggests that data often shares a global feature representation, while the statistical heterogeneity across clients or tasks is concentrated in the labels. Based on this intuition, we propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. We prove that this method obtains linear convergence to the ground-truth representation with near-optimal sample complexity in a linear setting, demonstrating that it can efficiently reduce the problem dimension for each client. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions, for example in meta-learning and multi-task learning. Further, extensive experimental results show the empirical improvement of our method over alternative personalized federated learning approaches in federated environments with heterogeneous data.
Unsupervised Discovery of 3D Physical Objects

We study the problem of unsupervised physical object discovery. Unlike existing frameworks that aim to learn to decompose scenes into 2D segments purely based on each object's appearance, we explore how physics, especially object interactions, facilitates learning to disentangle and segment instances from raw videos, and to infer the 3D geometry and position of each object, all without supervision. Drawing inspiration from developmental psychology, our Physical Object Discovery Network (POD-Net) uses both multi-scale pixel cues and physical motion cues to accurately segment observable and partially occluded objects of varying sizes, and infer properties of those objects. Our model reliably segments objects on both synthetic and real scenes. The discovered object properties can also be used to reason about physical events.
Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle

Syntax is fundamental to our thinking about language.Failing to capture the structure of input language could lead to generalization problems and over-parametrization.In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM).The model explicitly models the structure with an incremental parser and maintains the conditional probability setting of a standard language model (left-to-right).To train the incremental parser and avoid exposure bias, we also propose a novel dynamic oracle, so that SOM is more robust to wrong parsing decisions.Experiments show that SOM can achieve strong results in language modeling, incremental parsing and syntactic generalization tests, while using fewer parameters than other models.
Blockchain and the Evolution of Information Society

Today we are witnessing a multi-layered metamorphosis of our culture and society, accelerated by the technological developments of the last decades. The main drivers of this metamorphosis are information and interconnectivity. Here we will try to analyze this transformation from a holistic perspective.
BSDNet: Balanced Sample Distribution Network for Real-Time Semantic Segmentation of Road Scenes

In recent years, semantic segmentation based on deep convolutional neural networks has developed rapidly. However, it is still a challenge to balance the computing cost and segmentation performance for the current semantic segmentation methods. This paper proposes a lightweight real-time semantic segmentation model Balanced Sample Distribution Network (BSDNet), to solve this problem. In BSDNet, we introduce the Balanced Sample Distribution Module (BSDModule) to balance the sampling distribution of convolution and obtain features with a larger receptive field. To optimize the segmentation effect, we introduce a Shuffle Channel Attention Module (SCAModule) to enhance the interaction of channel features at the cost of a small number of parameters. BSDModule and SCAModule are lightweight and flexible and can adapt to different types of network structures. Extensive experiments on CityScapes and CamVid show that the proposed method can balance the computing cost and segmentation performance. Specifically, on the CityScapes with 512×1024 resolution, BSDNet-Xception39 achieves 68.3% MIoU and 84.6 FPS with only 1.2M parameters.
Inverse Reinforcement Learning Through Max-Margin Algorithm

Reinforcement Learning (RL) methods provide a solution for decision-making problems under uncertainty. An agent finds a suitable policy through a reward function by interacting with a dynamic environment. However, for complex and large problems it is very difficult to specify and tune the reward function. Inverse Reinforcement Learning (IRL) may mitigate this problem by learning the reward function through expert demonstrations. This work exploits an IRL method named Max-Margin Algorithm (MMA) to learn the reward function for a robotic navigation problem. The learned reward function reveals the demonstrated policy (expert policy) better than all other policies. Results show that this method has better convergence and learned reward functions through the adopted method represents expert behavior more efficiently.
Methodological Lessons Learned From Conducting a Population-Based Phone Survey on Nutrition Practices in the Kyrgyz Republic

To share lessons from a survey on nutrition practices, where the methodology shifted from in-person to phone interviews due to the COVID-19 pandemic. The USAID Advancing Nutrition Project is conducting an impact evaluation in Kyrgyz Republic to determine the effect of project interventions on nutrition behaviors. Municipalities in Batken and Jalalabad regions were randomly assigned to intervention or comparison areas.The stepped wedge design includes baseline, midpoint, and endline surveys, carried out at 12-month intervals. The baseline survey was completed in late 2020. Difference in differences analysis and analysis of project exposure variables will be used to determine impact. Due to COVID-19, all interviews were carried out using computer assisted telephone interviewing (CATI). We interviewed 2,091 women with children under two years of age, with phone numbers obtained from health facilities in survey areas. The response rate (11.6% completed interviews) was similar or higher than typical phone surveys. Interviews included complex questions related to 11 nutrition practices, including women's and children's dietary recall. Average interview length was 31 minutes. 4% of respondents dropped before completing the call. Advantages of phone interviews included reduced travel costs, improved quality control, and real-time data review. Disadvantages included high non-response, inability to observe items such as handwashing stations, and potential biases related to phone ownership. Our experience suggests that it is possible to carry out a complex, population-based nutrition survey using CATI. Response rates may be improved by building rapport at the start of the call. Interview length can be reduced using block randomization. Baseline levels of most indicators were similar to previous in-person surveys in Kyrgyzstan, suggesting CATI survey results can potentially provide accurate estimates of nutrition behaviors. United States Agency for International Development.
KAJIAN PENANGANAN BANJIR DENGAN SISTEM POMPA DI SUNGAI BENDUNG, KOTA PALEMBANG

Floods around the Bendung River in Palembang City have caused material and non-material losses. One of the reasons was the decreasing river capacity and backwater during the Musi River flood season, which is the Bendung River's outlet. This paper discusses the impact of adding a pump system to reduce flood impact. 1-dimensional and 2-dimensional numerical model using MIKE 11 and MIKE FLOOD were made to analyze the best measures. Various alternative scenarios were implemented in the model. Based on the modeling results, using the most optimal alternative, alternative 4 with 6 pumps of 36 m3/s and normalization of the Bendung River channel by 1 meter. The inundation area was reduced by 0.74 km2. Also, the maximum inundation height for the flood decreased by 0.6 meters, and the inundation duration was reduced by 8.5 hours. These modeling results can be a reference for managers and policymakers in managing flood events.Keywords: River channel normalization, pump system, numerical model, flood control, Bendung river