Reviewerly/exHarmony huggingface dataset:
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels

Off-policy reinforcement learning (RL) from pixel observations is notoriously unstable. As a result, many successful algorithms must combine different domain-specific practices and auxiliary losses to learn meaningful behaviors in complex environments. In this work, we provide novel analysis demonstrating that these instabilities arise from performing temporal-difference learning with a convolutional encoder and low-magnitude rewards. We show that this new visual deadly triad causes unstable training and premature convergence to degenerate solutions, a phenomenon we name catastrophic self-overfitting. Based on our analysis, we propose A-LIX, a method providing adaptive regularization to the encoder's gradients that explicitly prevents the occurrence of catastrophic self-overfitting using a dual objective. By applying A-LIX, we significantly outperform the prior state-of-the-art on the DeepMind Control and Atari 100k benchmarks without any data augmentation or auxiliary losses.
Validating Syntactic Correctness Using Unsupervised Clustering Algorithms

When developing a complex system in an open platform setting, users need to compose and maintain a systematic requirement specification. This paper proposes a solution to guarantee a syntactically accurate requirement specification that minimizes the ambiguity caused by ungrammatical sentences. Our system has a set of standard jargon and templates that are used as a guideline to write grammatically correct sentences. Given a database of standard technical Korean (STK) templates, the system that we have designed and implemented divides a new sentence into a specific cluster. If the system finds an identical template in a cluster, it confirms the new sentence as a sound one. Otherwise, the system uses unsupervised clustering algorithms to return the template that most closely resembles the syntax of the inputted sentence. We tested our proposed system in the field of open platform development for a railway train. In the experiment, our system learned to partition templates into clusters while reducing null attributes of an instance using the autoencoding procedure. Given a set of clusters, the system was able to successfully recommend templates that were syntactically similar to the structure of the inputted sentence. Since the degree of similarity for 500 instances was 97.00% on average, we conclude that our robust system can provide an appropriate template that users can use to modify their syntactically incorrect sentences.
Augment to Detect Anomalies with Continuous Labelling

Anomaly detection is to recognize samples that differ in some respect from the training observations. These samples which do not conform to the distribution of normal data are called outliers or anomalies. In real-world anomaly detection problems, the outliers are absent, not well defined, or have a very limited number of instances. Recent state-of-the-art deep learning-based anomaly detection methods suffer from high computational cost, complexity, unstable training procedures, and non-trivial implementation, making them difficult to deploy in real-world applications. To combat this problem, we leverage a simple learning procedure that trains a lightweight convolutional neural network, reaching state-of-the-art performance in anomaly detection. In this paper, we propose to solve anomaly detection as a supervised regression problem. We label normal and anomalous data using two separable distributions of continuous values. To compensate for the unavailability of anomalous samples during training time, we utilize straightforward image augmentation techniques to create a distinct set of samples as anomalies. The distribution of the augmented set is similar but slightly deviated from the normal data, whereas real anomalies are expected to have an even further distribution. Therefore, training a regressor on these augmented samples will result in more separable distributions of labels for normal and real anomalous data points. Anomaly detection experiments on image and video datasets show the superiority of the proposed method over the state-of-the-art approaches.
BusiNet -- a Light and Fast Text Detection Network for Business Documents

For digitizing or indexing physical documents, Optical Character Recognition (OCR), the process of extracting textual information from scanned documents, is a vital technology. When a document is visually damaged or contains non-textual elements, existing technologies can yield poor results, as erroneous detection results can greatly affect the quality of OCR. In this paper we present a detection network dubbed BusiNet aimed at OCR of business documents. Business documents often include sensitive information and as such they cannot be uploaded to a cloud service for OCR. BusiNet was designed to be fast and light so it could run locally preventing privacy issues. Furthermore, BusiNet is built to handle scanned document corruption and noise using a specialized synthetic dataset. The model is made robust to unseen noise by employing adversarial training strategies. We perform an evaluation on publicly available datasets demonstrating the usefulness and broad applicability of our model.
Anomaly Detection with Improved Data Accuracy and False Positive Reduction in Distributed Wireless Sensor Network

Abstract The authors have requested that this preprint be removed from Research Square.
Implementation of Google Translate Application Programming Interface (API) as a Text and Audio Translator

Through this translator program, it is craved that it can avail the general public to understand foreign language videos, can be useful in the world of education and technology, and can avail the persons with disabilities be up to communicate. The method used is a classification method that functions to detect the flow of shapes to instruct the class attribute as the task of the input attribute by generating automatic output through three stages, namely Machine Learning, Natural Language Processing, and Speech. The results showed that 90.38% of videos were successfully translated into text and audio, 9.62% of videos failed to be translated because the owner limited public interaction, and 89%-97% synchronization between text and audio. In this research, a text and audio translator program has been created using the Application Programming Interface (API). This program is a configuration of deep learning, machine translation, and text-to-speech designed using the high-level programming language python. The system used is a predictive system in which the system tries to predict the output equally the wishes of the user.
Analysis and optimization of Dual Parallel Partition Sorting with OpenMP

Purpose Sorting is a very important algorithm to solve problems in computer science. The most well-known divide and conquer sorting algorithm is quicksort. It starts with dividing the data into subarrays and finally sorting them. Design/methodology/approach In this paper, the algorithm named Dual Parallel Partition Sorting ( DPPSort ) is analyzed and optimized. It consists of a partitioning algorithm named Dual Parallel Partition ( DPPartition ). The DPPartition is analyzed and optimized in this paper and sorted with standard sorting functions named qsort and STLSort which are quicksort, and introsort algorithms, respectively. This algorithm is run on any shared memory/multicore systems. OpenMP library which supports multiprocessing programming is developed to be compatible with C/C++ standard library function. The authors’ algorithm recursively divides an unsorted array into two halves equally in parallel with Lomuto's partitioning and merge without compare-and-swap instructions. Then, qsort/STLSort is executed in parallel while the subarray is smaller than the sorting cutoff. Findings In the authors’ experiments, the 4-core Intel i7-6770 with Ubuntu Linux system is implemented. DPPSort is faster than qsort and STLSort up to 6.82× and 5.88× on Uint64 random distributions, respectively. Originality/value The authors can improve the performance of the parallel sorting algorithm by reducing the compare-and-swap instructions in the algorithm. This concept can be used to develop related problems to increase speedup of algorithms.
Centralized Framework to Send Notifications from Multiple Tools and Services on Multiple Messaging Platforms

Abstract: One of the reasons for advancement in the field of technology and IT is that a lot of tech companies constantly innovate and come up with new ideas and products. Apart from the mainstream products and services, organizations also have a lot of tools and services to support the software development life cycle of their product. Different engineering teams would have different sets of tools and services that are used internally within an organization. Every patch of code goes through the phases of the software development life cycle which takes a considerable amount of time. Developers have to proactively monitor the status of the runs, jobs that validates their patch. This can block them and waste a lot of time. Notification is one of the means to inform users. Notifying developers at the right time can save their time, they can triage issues if any, or merge the code to production or take appropriate actions based on the status. With the growing number of tasks, runs and jobs being triggered, a notification feature will add value to the service users. But developing notification modules for all the services individually can be inefficient. A better solution would be to have a centralized notification framework that is capable of sending notifications from multiple services. This paper presents a generic notification framework that can be integrated with any kind of tools or services and will be able to send notifications on any messaging platform. The framework also addresses aspects like scalability, fault tolerance, reliability, maintenance and works in real-time
Facial Image Reconstruction from Functional Magnetic Resonance Imaging via GAN Inversion with Improved Attribute Consistency

Neuroscience studies have revealed that the brain encodes visual content and embeds information in neural activity. Recently, deep learning techniques have facilitated attempts to address visual reconstructions by mapping brain activity to image stimuli using generative adversarial networks (GANs). However, none of these studies have considered the semantic meaning of latent code in image space. Omitting semantic information could potentially limit the performance. In this study, we propose a new framework to reconstruct facial images from functional Magnetic Resonance Imaging (fMRI) data. With this framework, the GAN inversion is first applied to train an image encoder to extract latent codes in image space, which are then bridged to fMRI data using linear transformation. Following the attributes identified from fMRI data using an attribute classifier, the direction in which to manipulate attributes is decided and the attribute manipulator adjusts the latent code to improve the consistency between the seen image and the reconstructed image. Our experimental results suggest that the proposed framework accomplishes two goals: (1) reconstructing clear facial images from fMRI data and (2) maintaining the consistency of semantic characteristics.
Removing Batch Normalization Boosts Adversarial Training

Adversarial training (AT) defends deep neural networks against adversarial attacks. One challenge that limits its practical application is the performance degradation on clean samples. A major bottleneck identified by previous works is the widely used batch normalization (BN), which struggles to model the different statistics of clean and adversarial training samples in AT. Although the dominant approach is to extend BN to capture this mixture of distribution, we propose to completely eliminate this bottleneck by removing all BN layers in AT. Our normalizer-free robust training (NoFrost) method extends recent advances in normalizer-free networks to AT for its unexplored advantage on handling the mixture distribution challenge. We show that NoFrost achieves adversarial robustness with only a minor sacrifice on clean sample accuracy. On ImageNet with ResNet50, NoFrost achieves $74.06\%$ clean accuracy, which drops merely $2.00\%$ from standard training. In contrast, BN-based AT obtains $59.28\%$ clean accuracy, suffering a significant $16.78\%$ drop from standard training. In addition, NoFrost achieves a $23.56\%$ adversarial robustness against PGD attack, which improves the $13.57\%$ robustness in BN-based AT. We observe better model smoothness and larger decision margins from NoFrost, which make the models less sensitive to input perturbations and thus more robust. Moreover, when incorporating more data augmentations into NoFrost, it achieves comprehensive robustness against multiple distribution shifts. Code and pre-trained models are public at https://github.com/amazon-research/normalizer-free-robust-training.
Works of National Painting in the Development of Educational Programs of Pedagogical Specialties

The article actualizes the idea of developing system-modeling approaches to the development of technologies by students of pedagogical universities that ensure the use of works of domestic painting in the educational process. Within the framework of the project «Didactic potential of national painting in the implementation of the program of spiritual revival of the nation», a study is being conducted aimed at meaningful and didactic provision of work with the works of Kazakhstani artists in strict adherence to the program of spiritual revival, the methodological aspect of which is set out in the patriotic act "Tugan Zher". The relevance of the work on the introduction of national painting into the educational process is dictated by modern requirements in a number of scientific studies devoted to various types of Kazakh national art and the use of its potential in the spiritual revival of the nation, the appeal to the works of national painting is considered as an effective means of teaching. The project participants objectify the idea of a syncretic approach to the use of this tool: putting the knowledge paradigm to the fore and taking into account the possibilities of painting as an art form, it is necessary to consider the didactic aspect of using the works of domestic artists in the educational process. The scientific significance of the Project «Didactic potential of national painting in the implementation of the program of spiritual revival of the nation» considers the national spirit of this type of national creativity among the folklore, literary, theatrical, musical heritage of the Kazakh people for use as one of the effective forms of presentation of ethno-cultural views of the Kazakh people. The novelty of the project lies in the fact that it presents a system-modeled description of the work on the spiritual formation of secondary school students through the training of future teachers in the conditions of modern pedagogical universities. The importance of this kind of training is proved by the materials of the conducted experiments, in which both the performers declared in the Project and teachers of different types of schools took part. The selection, verification and description of innovative technologies in working with paintings are focused both on activating the emotional perception of students, and on instilling in them the skills of creative analysis of paintings, and on generalizing theoretical provisions that contribute to the development of the spiritual heritage of the people. The article is an analytical study of those areas of work that should be carried out at the second stage of the study, which determines the relevance and features of the formation of teachers' skills with students of secondary schools within the framework of the «Tugan Zher» program.
Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysis

Parliamentary and legislative debate transcripts provide informative insight into elected politicians' opinions, positions, and policy preferences. They are interesting for political and social sciences as well as linguistics and natural language processing (NLP) research. While existing research studied individual parliaments, we apply advanced NLP methods to a joint and comparative analysis of six national parliaments (Bulgarian, Czech, French, Slovene, Spanish, and United Kingdom) between 2017 and 2020. We analyze emotions and sentiment in the transcripts from the ParlaMint dataset collection and assess if the age, gender, and political orientation of speakers can be detected from their speeches. The results show some commonalities and many surprising differences among the analyzed countries.
Cloud Accounting: The Development Accounting Information System in Industry 4.0 in Indonesia

The digitalization era of Industry 4.0 in Indonesia has significance roles in business sectors and accountancy in which they are progressing and developing. Future accountants are expected to equip upgraded-skills due to the globalization and digitalization of Industry 4.0. The trend of emergence technologies in accounting information system is Cloud Accounting. Hence, the digital transformation of the accounting information system is important for accountants to be fully aware that traditional accounting system will be gradually replaced to more digitalized accounting system. This paper focuses on the needs of Cloud Accounting and the extent of the development of accounting information system in Industry 4.0 in Indonesia. The research methodology of the study is qualitative analysis. Interview, questionnaire and focus group discussion were conducted to gather information from views of accounting business providers, practitioners, and accountants. Published journals of related topic were also examined to get viewpoints of the researchers. The results show that 90% of the respondents agree that accounting information system has evolved to more digitalized accounting system in Industry 4.0. Future accountants will have more challenging accountancy roles due to the digitalization on accounting information system in which digitalized accounting information system will take over with the advancement of technology through education and higher education should prepare future accountants by knowledge of Cloud Accounting in the curriculum of accounting study program
Improving disk failure detection accuracy via data augmentation

Frequently happening of disk failures seriously affects the dependability and service quality of cloud data centers. Recently, machine learning (ML) based methods are popularly adopted to proactively predict forthcoming disk failures via supervised learning. However, the high imbalance of failure samples and healthy samples is a huge obstacle for existing detection methods to establish high performance detection model. This paper presents a data augmentation method MSGMD, which can efficiently generate high quality failure samples to alleviate the data imbalance of the training set, so as to effectively improve the performance of any supervised failure detection models. First, MSGMD converts failure samples (multivariate time series) into multiple univariate time series via decomposing the spatial relations among features. Then it learns the temporal correlation of each feature via a policy-based reinforcement learning model trained in an adversarial way. After that, it generates failure samples by combining feature series sampled from learned distribution. Finally, it filters out low quality generated samples with a confidence-based method. Experimental results on real-world datasets show that, through data augmentation, MSGMD can improve the FDR and F1-Score of the state-of-the-art disk failure detection model by 31.59% and 30.74% respectively on average.
An optimal portfolio method based on real time prediction of gold and bitcoin prices

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Generalised Evasive Subspaces

We introduce and explore a new concept of evasive subspace with respect to a collection of subspaces sharing a common dimension, most notably partial spreads. We show that this concept generalises known notions of subspace scatteredness and evasiveness. We establish various upper bounds for the dimension of an evasive subspace with respect to arbitrary partial spreads, obtaining improvements for the Desarguesian ones. We also establish existence results for evasive spaces in a non-constructive way, using a graph theory approach. The upper and lower bounds we derive have a precise interpretation as bounds for the critical exponent of certain combinatorial geometries. Finally, we investigate connections between the notion of evasive space we introduce and the theory of rank-metric codes, obtaining new results on the covering radius and on the existence of minimal vector rank-metric codes.
Identifying the Context Shift between Test Benchmarks and Production Data

Machine learning models are often brittle on production data despite achieving high accuracy on benchmark datasets. Benchmark datasets have traditionally served dual purposes: first, benchmarks offer a standard on which machine learning researchers can compare different methods, and second, benchmarks provide a model, albeit imperfect, of the real world. The incompleteness of test benchmarks (and the data upon which models are trained) hinder robustness in machine learning, enable shortcut learning, and leave models systematically prone to err on out-of-distribution and adversarially perturbed data. The mismatch between a single static benchmark dataset and a production dataset has traditionally been described as a dataset shift. In an effort to clarify how to address the mismatch between test benchmarks and production data, we introduce context shift to describe semantically meaningful changes in the underlying data generation process. Moreover, we identify three methods for addressing context shift that would otherwise lead to model prediction errors: first, we describe how human intuition and expert knowledge can identify semantically meaningful features upon which models systematically fail, second, we detail how dynamic benchmarking - with its focus on capturing the data generation process - can promote generalizability through corroboration, and third, we highlight that clarifying a model's limitations can reduce unexpected errors. Robust machine learning is focused on model performance beyond benchmarks, and as such, we consider three model organism domains - facial expression recognition, deepfake detection, and medical diagnosis - to highlight how implicit assumptions in benchmark tasks lead to errors in practice. By paying close attention to the role of context, researchers can design more comprehensive benchmarks, reduce context shift errors, and increase generalizability.