Reviewerly/exHarmony huggingface dataset:
Generalization in Reinforcement Learning by Soft Data Augmentation

Extensive efforts have been made to improve the generalization ability of Reinforcement Learning (RL) methods via domain randomization and data augmentation. However, as more factors of variation are introduced during training, optimization becomes increasingly challenging, and empirically may result in lower sample efficiency and unstable training. Instead of learning policies directly from augmented data, we propose SOft Data Augmentation (SODA), a method that decouples augmentation from policy learning. Specifically, SODA imposes a soft constraint on the encoder that aims to maximize the mutual information between latent representations of augmented and non-augmented data, while the RL optimization process uses strictly non-augmented data. Empirical evaluations are performed on diverse tasks from DeepMind Control suite as well as a robotic manipulation task, and we find SODA to significantly advance sample efficiency, generalization, and stability in training over state-of-the-art vision-based RL methods.
A Short-Term PV Power Forecasting Method Using a Hybrid Kmeans-GRA-SVR Model under Ideal Weather Condition

With the continuous increase of solar penetration rate, it has brought challenges to the smooth operation of the power grid.Therefore, to make photovoltaic power generation not affect the smooth operation of the grid, accurate photovoltaic power prediction is required.And short-term forecasting is essential for the deployment of daily power generation plans.In this paper, A short-term photovoltaic power generation forecast method based on K-means++, grey relational analysis (GRA) and support vector regression (SVR) (Hybrid Kmeans-GRA-SVR, HKGSVR) was proposed.The historical power data was clustered through the multi-index K-means++ algorithm.And the similar days and the nearest neighbor similar day of the prediction day were selected by the GRA algorithm.Then, similar days and nearest neighbor similar days were used to train SVR to obtain an accurate photovoltaic power prediction model.Under ideal weather, the average values of MAE, RMSE, and R 2 were 0.8101 kW, 0.9608 kW, and 99.66%, respectively.The average computation time was 1.7487 s, which was significantly better than the SVR model.Thus, the demonstrated numerical results verify the effectiveness of the proposed model for short-term PV power prediction.
Run, agent, run! architecture and benchmarking of actor-based agents

The paper introduces an Agent-Oriented Programming (AOP) framework based on the Belief-Desire-Intention (BDI) model of agency. The novelty of this framework is in relying on the Actor model, instantiating each intentional agent as an autonomous micro-system run by actors. The working hypothesis behind this choice is that defining the agents via actors results in a more fine-grained modular architecture and that the execution of agent-oriented programs is enhanced (in scalability as well as in performance) by relying on robust implementations of Actor models such as Akka. The framework is benchmarked and analyzed quantitatively and qualitatively against three other AOP frameworks: Jason, ASTRA and Sarl.
Application of Convolutional Neural Network in multi-channel Scenario D2D Communication Transmitting Power Control

In order to optimize the communication performance of the underlying D2D communication in a multichannel scenario by adjusting the user's transmit power, we present a power control scheme of D2D communication in multichannel scenario based on Convolutional Neural Network (CNN). By using the transmit power of CNN self-learning D2D transmitter, we can maximize the spectrum efficiency of D2D receiver, which is helpful to consider the channel state of each link. In the case of optimizing transmitting power control, conventional methods need to run multiple iterating process to find out the solution of a complex optimizing problem. But deep-learning methods, which is driven by data, can find out a solution with extremely low time consumption by well-trained artificial neural network. In this paper, we note that the input of the neural network is channel status of users and the output is the strategy of transmitting power control. Simulations show that CNN can find out the transmitting power control plan which is closed to the conventional method with low time consumption.
Research on wireless distributed financial risk data stream mining based on dual privacy protection

Abstract With the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy. Therefore, how to prevent privacy leakage during the efficient mining process poses new challenges to the data mining technology. This article is mainly aimed at the current privacy data leakage in financial data mining, combined with existing data mining technology to study data mining and privacy protection. First, a data mining model for dual privacy protection is defined, which can better meet the characteristics of distributed data streams while achieving privacy protection effects. Secondly, a privacy-oriented data stream mining algorithm is proposed, which uses random interference technology to effectively protect the original sensitive data. Finally, the analysis and discussion of the algorithm in this paper through simulation experiments show that the algorithm is feasible and effective, and can better adapt to the distributed data flow distribution and dynamic characteristics, while achieving better privacy protection effects, effectively reduced communication load.
A two-level deep learning approach for emotion recognition in Arabic news headlines

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Self supervised contrastive learning for digital histopathology

Unsupervised learning has been a long-standing goal of machine learning and is especially important for medical image analysis, where the learning can compensate for the scarcity of labeled datasets. A promising subclass of unsupervised learning is self-supervised learning, which aims to learn salient features using the raw input as the learning signal. In this paper, we use a contrastive self-supervised learning method called SimCLR that achieved state-of-the-art results on natural-scene images and apply this method to digital histopathology by collecting and pretraining on 57 histopathology datasets without any labels. We find that combining multiple multi-organ datasets with different types of staining and resolution properties improves the quality of the learned features. Furthermore, we find using more images for pretraining leads to a better performance in multiple downstream tasks. Linear classifiers trained on top of the learned features show that networks pretrained on digital histopathology datasets perform better than ImageNet pretrained networks, boosting task performances by more than 28% in F1 scores on average. These findings may also be useful when applying newer contrastive techniques to histopathology data. Pretrained PyTorch models are made publicly available at this https URL.
EvolGAN: Evolutionary Generative Adversarial Networks

We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both. The new method leads to the generation of significantly higher quality images while preserving the original generator's diversity. Human raters preferred an image from the new version with frequency 83.7% for Cats, 74% for FashionGen, 70.4% for Horses, and 69.2% for Artworks - minor improvements for the already excellent GANs for faces. This approach applies to any quality scorer and GAN generator.
Maxim Variation, Conventional, and Particularized Implicature on Students’ Conversation

Conversation is one of the activities that students do in their lives. Conversation is in relation to the maxim and implicature. Without realizing, the students usually violate and obey the maxim. Thereby, this study aims at knowing the types of maxim and implicature.The study used qualitative design. This study used transcription of the students’ conversation. Besides, this study used coding analysis. The findings of this study showed that most of the students obeyed the maxims of quality, quantity, manner and relevance, however, some students still disobeyed the maxims. Furthermore, there were two types of implicature that the students did in conversation namely conventional and conversational implicature. Therefore, by knowing the maxim and the implicature, the students can know the role in the conversation.
WFNet: A Wider and Finer Network for Salient Object Detection

Multi-layer feature integration has demonstrated its superiority in salient object detection. However, the salient regions generated by most models still suffer from inconsistencies and coarse region boundaries. In this paper, to solve these two problems, we proposed a wider and finer network named WFNet. Firstly, a wider feature enhancement module (WFE) is designed to expand the receptive fields of deep semantic features, which makes the network look wider and more accurate while locating salient regions. Secondly, to improve the regional continuity and reduce background noise, we introduce a finer feature fusion module (F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> M) which consists of scale-invariant average pooling and detailed feature integration module with channel-wise attention. Finally, we propose an edge-region complementary strategy (ERC) and an edge-focused loss (EL), which can supplement the diluted deep semantics and let the network pay more attention to boundary pixels of salient objects. Benefit from rich deep semantics and more detailed edge features, WFNet can predict saliency maps with clear boundaries under the guidance of edge-focused loss. Experimental results prove that the proposed method outperforms state-of-the-art methods on five benchmarks without any post-processing.
On The Adversarial Robustness of 3D Point Cloud Classification

3D point clouds play pivotal roles in various safety-critical fields, such as autonomous driving, which desires the corresponding deep neural networks to be robust to adversarial perturbations. Though a few defenses against adversarial point cloud classification have been proposed, it remains unknown whether they can provide real robustness. To this end, we perform the first security analysis of state-of-the-art defenses and design adaptive attacks on them. Our 100% adaptive attack success rates demonstrate that current defense designs are still vulnerable. Since adversarial training (AT) is believed to be the most effective defense, we present the first in-depth study showing how AT behaves in point cloud classification and identify that the required symmetric function (pooling operation) is paramount to the model's robustness under AT. Through our systematic analysis, we find that the default used fixed pooling operations (e.g., MAX pooling) generally weaken AT's performance in point cloud classification. Still, sorting-based parametric pooling operations can significantly improve the models' robustness. Based on the above insights, we further propose DeepSym, a deep symmetric pooling operation, to architecturally advance the adversarial robustness under AT to 47.01% without sacrificing nominal accuracy, outperforming the original design and a strong baseline by 28.5% (∼2.6×) and 6.5%, respectively, in PointNet.
Password-authenticated searchable encryption

Abstract We introduce Password Authenticated Searchable Encryption (PASE) , a novel searchable encryption scheme where a single human-memorizable password can be used to outsource (encrypted) data with associated keywords to a group of servers and later retrieve this data through the encrypted keyword search procedure. PASE ensures that only the legitimate user who knows the initially registered password can perform these operations. In particular, PASE guarantees that no single server can mount an offline attack on the user’s password or learn any information about the encrypted keywords. The concept behind PASE protocols extends previous concepts behind searchable encryption by removing the requirement on the client to store high-entropy keys, thus making the protocol device-agnostic on the user side. In this paper, we model the functionality of PASE along with two security requirements (indistinguishability against chosen keyword attacks and authentication) and propose an efficient direct construction in a two-server setting those security we prove in the standard model under the Decisional Diffie–Hellman assumption. Our constructions support outsourcing and retrieval procedures based on multiple keywords and allow users to change their passwords without any need for the re-encryption of the outsourced data. Our theoretical efficiency comparisons and experimental performance and scalability measurements show that the proposed scheme is practical and offers high performance in relation to computations and communications on the user side. The practicality of our PASE scheme is further demonstrated through its implementation within a JavaScript-based web application that can readily be executed on any (mobile) browser and remains practical for commodity user devices such as laptops and smartphones.
Recycling qubits in near-term quantum computers

Quantum computers are capable of efficiently contracting unitary tensor networks, a task that is likely to remain difficult for classical computers. For instance, networks based on matrix product states or the multi-scale entanglement renormalization ansatz (MERA) can be contracted on a small quantum computer to aid the simulation of a large quantum system. However, without the ability to selectively reset qubits, the associated spatial cost can be exorbitant. In this paper, we propose a protocol that can unitarily reset qubits when the circuit has a common convolutional form, thus dramatically reducing the spatial cost for implementing the contraction algorithm on general near-term quantum computers. This protocol generates fresh qubits from used ones by partially applying the time-reversed quantum circuit over qubits that are no longer in use. In the absence of noise, we prove that the state of a subset of these qubits becomes $|0\ldots 0\rangle$, up to an error exponentially small in the number of gates applied. We also provide a numerical evidence that the protocol works in the presence of noise. We also provide a numerical evidence that the protocol works in the presence of noise, and formulate a condition under which the noise-resilience follows rigorously.
A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers

This study aims to describe a model that will apply image processing and traditional machine learning techniques specifically Support Vector Machines, Naïve-Bayes, and k-Nearest Neighbors to identify whether or not a given breast histopathological image has Invasive Ductal Carcinoma (IDC). The dataset consisted of 54,811 breast cancer image patches of size 50px x 50px, consisting of 39,148 IDC negative and 15,663 IDC positive. Feature extraction was accomplished using Oriented FAST and Rotated BRIEF (ORB) descriptors. Feature scaling was performed using Min-Max Normalization while K-Means Clustering on the ORB descriptors was used to generate the visual codebook. Automatic hyperparameter tuning using Grid Search Cross Validation was implemented although it can also accept user supplied hyperparameter values for SVM, Naïve Bayes, and K-NN models should the user want to do experimentation. Aside from computing for accuracy, the AUPRC and MCC metrics were used to address the dataset imbalance. The results showed that SVM has the best overall performance, obtaining accuracy = 0.7490, AUPRC = 0.5536, and MCC = 0.2924.
An Interoperable Architecture for Usable Password-Less Authentication

Passwords are the de facto standard for authentication despite their significant weaknesses. While businesses are currently focused on implementing multi-factor authentication to provide greater security, user adoption is still low. An alternative, WebAuthn, uses cryptographic key pairs to provide password-less authentication. WebAuthn has been standardised and is resilient to phishing attacks. However, its adoption is also very low; the barriers to adoption include usability and resilience of keys. We propose a novel architecture for password-less authentication designed to improve usability and deployability. Our architecture is based on the WebAuthn standards and supports registration and login to web-services. We support a WebAuthn authenticator that generates and uses the key pairs on the client device by providing resilience for these key pairs by using a backup key store in the cloud. We also propose a WebAuthn authenticator using a key store in the cloud so that password-less authentication can be used interoperably between devices. We also assess the properties of these architectures against identified threats and how they can form the basis for improving usability and lowering the technical barriers to adoption of password-less authentication.
Apollo: Transferable Architecture Exploration

The looming end of Moore's Law and ascending use of deep learning drives the design of custom accelerators that are optimized for specific neural architectures. Architecture exploration for such accelerators forms a challenging constrained optimization problem over a complex, high-dimensional, and structured input space with a costly to evaluate objective function. Existing approaches for accelerator design are sample-inefficient and do not transfer knowledge between related optimizations tasks with different design constraints, such as area and/or latency budget, or neural architecture configurations. In this work, we propose a transferable architecture exploration framework, dubbed Apollo, that leverages recent advances in black-box function optimization for sample-efficient accelerator design. We use this framework to optimize accelerator configurations of a diverse set of neural architectures with alternative design constraints. We show that our framework finds high reward design configurations (up to 24.6% speedup) more sample-efficiently than a baseline black-box optimization approach. We further show that by transferring knowledge between target architectures with different design constraints, Apollo is able to find optimal configurations faster and often with better objective value (up to 25% improvements). This encouraging outcome portrays a promising path forward to facilitate generating higher quality accelerators.
MODERNIZATION OF ELECTRONIC LEARNING SYSTEM OF THE UNIVERSITY TO THE NEEDS OF THE PARTICIPANTS OF THE EDUCATIONAL PROCESS

Electronic education has been an integral part of the educational process at higher education institutions for more than a decade. However, it had had rather a supporting role in Ukraine before the pandemic, whereas today it is crucial to provide full-fledged learning, communication and collaboration for all educational process participants with the help of electronic resources. Therefore, there is a need in modernization of the electronic learning system of the university according to current requirements and challenges. The article outlines the ways the e-learning system changes implementation using the example of Borys Grinchenko Kyiv University. The attention is paid to educational environment personalization for different participants of the learning process, possibilities for information arrangement and monitoring. Thanks to the arrangement of electronic learning courses (ELC) according to the created educational programmes in the e-learning system, students have access to current information about all disciplines which belong to a definite educational professional or scientific programme: distribution by semesters, forms of control, progress in the courses, etc. At the same time it provides administration with the possibility to track activities of applicants for education and academic staff in every educational programme. Electronic deanery module implemented in Borys Grinchenko Kyiv University e-learning system allows to monitor educational process, get access to current data on every ELC progress rate and also to personalize the educational process for every student through individual learning plans. With the help of the e-learning system students can get remote access to the materials of all disciplines, fulfil tasks, send them to teachers for review and take tests. The blocks in the user's personal dashboard allow to get quick access to communication and collaboration tools for online classes arrangement, group work and receiving feedback from teachers, to the University’s library and other useful resources that simplify educational process arrangement.
Weblinks

Without any doubt, creating links and navigational trails is fundamental to hypertext. The Web is the widest spread representative among all systems in the history of hypertext, although its underlying core concept is kept simple. This is why the Web's widely used link functionality is naive, supporting only embedded, unary and unidirectional links. A strength of the Web is its extensibility, giving us the opportunity to augment the current functionality of links. We showcase a browser plugin, which enables users to create and share complex links over the existing Web. Furthermore, we discuss the CB-OHS Mother, its link model and how this relates to existing work. The implementation adopts latest standardization efforts and is an update to older attempts of enabling external link services for the Web.
Liputan6: A Large-scale Indonesian Dataset for Text Summarization

In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from Liputan6.com, an online news portal, and obtain 215,827 document–summary pairs. We leverage pre-trained language models to develop benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have low ROUGE scores, and expose both issues with ROUGE itself, as well as with extractive and abstractive summarization models.