Reviewerly/exHarmony huggingface dataset:
Self-supervised Bilingual Syntactic Alignment for Neural Machine Translation

While various neural machine translation (NMT) methods have integrated mono-lingual syntax knowledge into the linguistic representation of sequence-to-sequence, no research is available on aligning the syntactic structures of target language with the corresponding source language syntactic structures. This work shows the first attempt of a source-target bilingual syntactic alignment approach SyntAligner by mutual information maximization-based self-supervised neural deep modeling. Building on the word alignment for NMT, our SyntAligner firstly aligns the syntactic structures of source and target sentences and then maximizes their mutual dependency by introducing a lower bound on their mutual information. In SyntAligner, the syntactic structure of span granularity is represented by transforming source or target word hidden state into a source or target syntactic span vector. A border-sensitive span attention mechanism then captures the correlation between the source and target syntactic span vectors, which also captures the self-attention between span border-words as alignment bias. Lastly, a self-supervised bilingual syntactic mutual information maximization-based learning objective dynamically samples the aligned syntactic spans to maximize their mutual dependency. Experiment results on three typical NMT tasks: WMT'14 English to German, IWSLT'14 German to English, and NC'11 English to French show the SyntAligner effectiveness and universality of syntactic alignment.
Overview of Big Data and Cloud Computing Laws, Regulations and Policies in Central Asia

‘Cloud’ is a way of delivering computing resources as a utility service via the internet. Throughout the Central Asia, Clouds are being used to provide computing resources as a utility service through internet. “Cloud Computing” itself is emerging as an innovative area of technology and attracting substantial investments by government and private sector into infrastructure, platforms, and applications. Central Asia is demonstrating exponential growth in technological arena and there is a demand for cloud resources which is driven by such developments as the deployment on a vast scale of mobile apps and the rapid emergence of ‘Big Data’. However, legal infrastructure is premature to facilitate these new technologies. This paper is an attempt to provide an overview of contemporary laws, regulations and policies related to Cloud Computing and Big Data in Central Asia. This paper also endorses the idea that all of technology laws related to Central Asia are the subject matter of Central Asia Tech Law.
Display to Labeled Proofs and Back Again for Tense Logics

We introduce translations between display calculus proofs and labeled calculus proofs in the context of tense logics. First, we show that every derivation in the display calculus for the minimal tense logic Kt extended with general path axioms can be effectively transformed into a derivation in the corresponding labeled calculus. Concerning the converse translation, we show that for Kt extended with path axioms, every derivation in the corresponding labeled calculus can be put into a special form that is translatable to a derivation in the associated display calculus. A key insight in this converse translation is a canonical representation of display sequents as labeled polytrees. Labeled polytrees, which represent equivalence classes of display sequents modulo display postulates, also shed light on related correspondence results for tense logics.
Remote control and data analysis system based on Alibaba Cloud

Abstract In order to eliminate the digital gap between equipment control and production information management, a smart factory remote control and data analysis system based on a cloud-fog hybrid computing model is proposed. Alibaba Cloud Server serves as the cloud computing layer to provide data storage and services, and the Internet of Things platform serves as the fog terminal. The computing layer completes data filtering and system early warning. The system consists of five parts: perception layer, gateway layer, platform layer, application layer and persistence layer. Each layer is designed and analyzed. The dead zone-adaptive swing door trending algorithm is used for data compression, and the system pressure test experiment and the data compression accuracy test experiment are completed. The experimental results show that this system improves the control of data in the production process, reduces the amount of data storage, and has high system compatibility to meet actual production needs.
Participative Decision Making and Organizational Commitment: The Mediatory Role of Influence and Communication

In a high turnover environment, inculcating organizational commitment among employees is a big challenge. We proposed a theoretical framework explaining how participative decision making affects commitment. For this, we modified the Jackson (1983) model explaining the effect of Participation in Decision Making on communication, and further extend it to Organizational commitment in the perspective of Elele & Fields (2010). The participative decision was measured by employee engagement in decision making (EE), meeting load (ML), and management support (MS). These factors increase, Perceived Influence among employees (PI), Job-related communication (JR), and Personal communication (PC). Moreover, thorough these communication factors, organizational commitment got increase. This is classified as Affective commitment (AC), normative commitment (NC), and continuance commitment (CC). To establish its empirical validity, we conducted a survey based on a close-ended questionnaire, from 200 employees, working in different organizations of Karachi. Data were analyzed using structured equation modeling and confirmatory factor analysis. The result suggested that Job-related communication effectively mediate employee engagement in decision making and both Affective and normative commitment. Similarly, Personal communication and Perceived Influence also mediate the effect of meeting load on continuance and normative commitment respectively. Moreover, management support increases both Perceived Influence and Job-related communication. And lastly, employee engagement has a significant positive effect on Perceived Influence. Overall findings backed our theory and explain the strong effect of Participative decision making on commitment mediated by communication.
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation

Policy optimization methods are popular reinforcement learning (RL) algorithms, because their incremental and on-policy nature makes them more stable than the value-based counterparts. However, the same properties also make them slow to converge and sample inefficient, as the on-policy nature is not amenable to data reuse and the incremental updates result in a large iteration complexity before a near optimal policy is discovered. These empirical findings are mirrored in theory in the recent work of~\citet{agarwal2020pc}, which provides a policy optimization method PC-PG that provably finds near optimal policies in the linear MDP model and is robust to model misspecification, but suffers from an extremely poor sample complexity compared with value-based techniques. In this paper, we propose a new algorithm, COPOE, that overcomes the poor sample complexity of PC-PG while retaining the robustness to model misspecification.COPOE makes several important algorithmic enhancements of PC-PG, such as enabling data reuse, and uses more refined analysis techniques, which we expect to be more broadly applicable to designing new RL algorithms.The result is an improvement in sample complexity from $\widetilde{O}(1/\epsilon^{11})$ for PC-PG to $\widetilde{O}(1/\epsilon^3)$ for COPOE, nearly bridging the gap with value-based techniques.
Preventing face morphing attacks by using legacy face images

Countries allow citizens to upload a face image or provide printed copies to authorities to issue their passport. This allows prior image manipulation with criminal intent. A composite image can be created by blending the images of two individuals before submitting the composite image to the authorities. Depending on several factors, the submitted morphed face image can fool the issuing officer to issue a legitimate document. The document can then be successfully used by either contributor to attack the automatic Face Recognition Systems (FRS) operating, for example, at Automatic Border Control (ABC) airport gates. This is known as a Morphing Attack (MA), an identity sharing scheme with serious consequences. Here, the security vulnerabilities due to MAs are identified and analysed, and an additional security measure that allows mitigating the risk or preventing MAs in certain scenarios is proposed. The measure introduces more comparisons by keeping the old passport or ID card image in the chip, in passport renewal applications or first time passport applications, respectively. This approach is implemented with two FRSs on a challenging dataset and the dramatic decrease in the vulnerability is shown. Finally, their performance is compared with a state-of-the-art MA detection algorithm on the same dataset.
When quantum state tomography benefits from willful ignorance

We show that quantum state tomography with perfect knowledge of the measurement apparatus proves to be, in some instances, inferior to strategies discarding all information about the measurement at hand, as in the case of data pattern tomography. In those scenarios, the larger uncertainty about the measurement is traded for the smaller uncertainty about the reconstructed signal. This effect is more pronounced for minimal or nearly minimal informationally complete measurement settings, which are of utmost practical importance.
University in Pandemic Conditions: E-Learning in the Representations and Evaluations of Teachers and Students

The development of information technologies is an important element of modern society. Their potential is quite high: access to different kinds of information, increased connectivity, high mobility, etc. Accordingly, at the State, professional and social levels, the question of the realization of these educational opportunities is being brought up to the fore and is being actively discussed: the replacement of correspondence education by distance education and the expansion of the face-to-face distance learning format. The worldwide COVID‑19 pandemic, the transfer of employees to remote work, and educational institutions to remote format showed the opportunities and challenges of the practical implementation of remote forms. The experiences of the participants in this process had much in common, but also significant differences. The analysis of this experience, including through sociological methods, is of great scientific and practical importance. The article presents the results of a sociological study conducted in June-­July 2020 among teachers and students of the Kuban State University, devoted to the problems of the university’s transition to distance learning in the context of the COVID‑19 pandemic. As a completely new form, total distance learning has created many difficulties for participants in the educational process. On the basis of the results of the study, it was concluded that teachers and students had encountered several types of problems in the transition to a new form of education: organizational and technical, pedagogical and socio-­psychological. The latter group of complexities can be seen as fundamental, requiring ad hoc and, in a sense, longer-term adaptation.
The Model May Fit You: User-Generalized Cross-Modal Retrieval

In real-world applications, a cross-model retrieval model trained on multimodal instances without considering differences in data distributions among users, termed as user domain shift, usually cannot generalize well to unknown user domains. In this paper, we define a new task of user-generalized cross-modal retrieval, and propose a novel Meta-Learning Multimodal User Generalization (MLMUG) method to solve it. MLMUG simulates the user domain shift with meta-optimization, which aims to embed multimodal data effectively and generalize the cross-modal retrieval model to any unknown user domains. We design a cross-modal embedding network with a learnable meta covariant attention module to encode transferable knowledge among different user domains. A user-adaptive metaoptimization scheme is proposed to adaptively aggregate gradients and meta-gradients for fast and stable meta-optimization.We build two benchmarks for user-generalized cross-modal retrieval evaluation. Experiments on the proposed benchmarks validate the generalization of our method compared with several stateof-the-art methods.
Hybrid Evolutionary Algorithm based Task Scheduling Mechanism for Resource Allocation in Cloud Environment

Distributed computing, grid computing, and virtualization have all resulted in the growth of cloud computing.. The existing research work, introduced a strategic theory (ST) based Improved Elephant Herd Optimization (IEHO) for feasible allotment procedure where the result function confess a straight payment system to meet the users requirements. However total users is huge in cloud computing, total tasks and total data are as well vast. Because the cost of every work on cloud services varies, client task scheduling in the cloud is not like conventional scheduling approaches. In a cloud computing system, it's critical to figure out how to effectively schedule work. As a results, the research proposes an unique Task Scheduling Mechanism (TSM) was proposed, which may suit users' needs while also improving resource usage and so improving the overall performance of the cloud cloud computing. The goal of this project is to plan task groups in a cloud computing platform with varying resource costs and computational performance. The suggested cloud scheduling solution uses an enhanced cost-based scheduling scheme to efficiently map workloads to existing cloud services. The scheduling technique, that is founded on Hybrid Evolutionary (HE)algorithms, increases the computation/communication proportion by combining user jobs as per a cloud resource's processing capacity and sending the aggregated activities to the resource. The simulation experiments show that the proposed Hybrid Evolutionary based Task Scheduling Mechanism (HE-TSM) is efficiently schedule the tasks along with both costs and performance in the cloud computing environment.
Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads.

A graph neural network (GNN) enables deep learning on structured graph data. There are two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which are expensive to purchase and maintain, and 2) limited memory on GPUs cannot scale to today's billion-edge graphs. This paper presents Dorylus: a distributed system for training GNNs. Uniquely, Dorylus can take advantage of serverless computing to increase scalability at a low cost. The key insight guiding our design is computation separation. Computation separation makes it possible to construct a deep, bounded-asynchronous pipeline where graph and tensor parallel tasks can fully overlap, effectively hiding the network latency incurred by Lambdas. With the help of thousands of Lambda threads, Dorylus scales GNN training to billion-edge graphs. Currently, for large graphs, CPU servers offer the best performance-per-dollar over GPU servers. Just using Lambdas on top of CPU servers offers up to 2.75x more performance-per-dollar than training only with CPU servers. Concretely, Dorylus is 1.22x faster and 4.83x cheaper than GPU servers for massive sparse graphs. Dorylus is up to 3.8x faster and 10.7x cheaper compared to existing sampling-based systems.
Object Retrieval and Localization in Large Art Collections Using Deep Multi-style Feature Fusion and Iterative Voting

The search for specific objects or motifs is essential to art history as both assist in decoding the meaning of artworks. Digitization has produced large art collections, but manual methods prove to be insufficient to analyze them. In the following, we introduce an algorithm that allows users to search for image regions containing specific motifs or objects and find similar regions in an extensive dataset, helping art historians to analyze large digitized art collections. Computer vision has presented efficient methods for visual instance retrieval across photographs. However, applied to art collections, they reveal severe deficiencies because of diverse motifs and massive domain shifts induced by differences in techniques, materials, and styles. In this paper, we present a multi-style feature fusion approach that successfully reduces the domain gap and improves retrieval results without labelled data or curated image collections. Our region-based voting with GPU-accelerated approximate nearest-neighbour search allows us to find and localize even small motifs within an extensive dataset in a few seconds. We obtain state-of-the-art results on the Brueghel dataset and demonstrate its generalization to inhomogeneous collections with a large number of distractors.
The impact of case and contact characteristics on contact tracing during the West Africa Ebola epidemic

A recent paper by Etard et al. highlights how clustering of Ebola exposures around a few cases, and overdispersion of contacts per case, leads to super-spreader events and epidemic propagation. 1 Etard J.F. Touré A. Sow M.S. et al. Heterogeneity of contact patterns with Ebola virus disease cases. J Infect. 2021; Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar Based on this evidence and that seen with SARS-Cov-2, 2 Majra D. Benson J. Pitts J. et al. SARS-CoV-2 (COVID-19) superspreader events. J Infect. 2021; 82: 36-40 Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar they suggest that targeting epidemic screening and communication control strategies in low resource settings may be beneficial.
Metro Water Fraudulent Prediction in Houses Using Convolutional Neural Network and Recurrent Neural Network

Aim: The main aim of the study is to predict metro water fraud accurately by Recurrent Neural Network Algorithms and compare the prediction accuracy with Convolutional Neural Network. Materials and Methods: In the existing system Convolutional Neural Network algorithm is used and in the proposed system Recurrent Neural Network algorithm is used. CNN with sample size =20 and RNN with sample size =20 was iterated forty times for predicting the accuracy. The algorithms have been implemented and tested over a dataset which consists of 8002 records. Result: After performing the experiment we get mean accuracy of 94.5210 by using Recurrent Neural Network algorithm and we get accuracy of 93.4950 by using Convolutional Neural Network algorithm for metro water fraudulent prediction. There is a statistical significant difference in accuracy for two algorithms is p&lt;0.05 by performing independent samples t-tests. Conclusion: The comparison results show that the Recurrent Neural Network algorithm appears to be better performance than Convolutional Neural Network algorithms.
GPS-Free, Error Tolerant Path Planning for Swarms of Micro Aerial Vehicles with Quality Amplification ‡

We present an error tolerant path planning algorithm for Micro Aerial Vehicle (MAV) swarms. We assume navigation without GPS-like techniques. The MAVs find their path using sensors and cameras, identifying and following a series of visual landmarks. The visual landmarks lead the MAVs towards their destination. MAVs are assumed to be unaware of the terrain and locations of the landmarks. They hold a priori information about landmarks, whose interpretation is prone to errors. Errors are of two types, recognition or advice. Recognition errors follow from misinterpretation of sensed data or a priori information, or confusion of objects, e.g., due to faulty sensors. Advice errors are consequences of outdated or wrong information about landmarks, e.g., due to weather conditions. Our path planning algorithm is cooperative. MAVs communicate and exchange information wirelessly, to minimize the number of recognition and advice errors. Hence, the quality of the navigation decision process is amplified. Our solution successfully achieves an adaptive error tolerant navigation system. Quality amplification is parameterized with respect to the number of MAVs. We validate our approach with theoretical proofs and numeric simulations.
ASNet: Auto-Augmented Siamese Neural Network for Action Recognition

Human action recognition methods in videos based on deep convolutional neural networks usually use random cropping or its variants for data augmentation. However, this traditional data augmentation approach may generate many non-informative samples (video patches covering only a small part of the foreground or only the background) that are not related to a specific action. These samples can be regarded as noisy samples with incorrect labels, which reduces the overall action recognition performance. In this paper, we attempt to mitigate the impact of noisy samples by proposing an Auto-augmented Siamese Neural Network (ASNet). In this framework, we propose backpropagating salient patches and randomly cropped samples in the same iteration to perform gradient compensation to alleviate the adverse gradient effects of non-informative samples. Salient patches refer to the samples containing critical information for human action recognition. The generation of salient patches is formulated as a Markov decision process, and a reinforcement learning agent called SPA (Salient Patch Agent) is introduced to extract patches in a weakly supervised manner without extra labels. Extensive experiments were conducted on two well-known datasets UCF-101 and HMDB-51 to verify the effectiveness of the proposed SPA and ASNet.
An irreducibility criterion for polynomials over integers

In this article, we consider the polynomials of the form $f(x)=a_0+a_1x+a_2x^2+\cdots+a_nx^n\in \mathbb{Z}[x],$ where $|a_0|=|a_1|+\dots+|a_n|$ and $|a_0|$ is a prime. We show that these polynomials have a cyclotomic factor whenever reducible. As a consequence, we give a simple procedure for checking the irreducibility of trinomials of this form and separability criterion for certain quadrinomials.
A genetic fuzzy system for interpretable and parsimonious reinforcement learning policies

Reinforcement learning (RL) is experiencing a resurgence in research interest, where Learning Classifier Systems (LCSs) have been applied for many years. However, traditional Michigan approaches tend to evolve large rule bases that are difficult to interpret or scale to domains beyond standard mazes. A Pittsburgh Genetic Fuzzy System (dubbed Fuzzy MoCoCo) is proposed that utilises both multiobjective and cooperative coevolutionary mechanisms to evolve fuzzy rule-based policies for RL environments. Multiobjectivity in the system is concerned with policy performance vs. complexity. The continuous state RL environment Mountain Car is used as a testing bed for the proposed system. Results show the system is able to effectively explore the trade-off between policy performance and complexity, and learn interpretable, high-performing policies that use as few rules as possible.