Reviewerly/exHarmony huggingface dataset:
Notice of Removal: A Novel Representation for Permutations

In this paper, we present a variable-length binary code for permutations of degree n, where n is a power of 2. The Lehmer code and its variants provide a bijection between permutations and the indexes in lexicographic ordering. They provide compression of permutations approaching Shannon bound at the expense of structural information. The variable-length code proposed in this paper has asymptotically optimal compression capability while preserving structural information of permutations. In other words, the code encodes the way a permutation is organized, and is optimal in the sense that the ratio of average codeword length and the entropy of permutations approaches 1 as n tends to infinity. The encoding and decoding are efficient. Like the Lehmer code and other enumerative codes, it is not necessary to construct look-up tables. The code is complete and satisfies the prefix condition. Furthermore, the codeword length indicates how “well shuffled” a permutation is. Permutations with longer codewords seem more “random.” An enumeration method of the variable-length code is also given by using T. Cover's enumerative coding scheme and Schalkwijk code. This gives a different indexing from the Lehmer code. This work can be extended to the case of arbitrary n in a straightforward way.
Determinants of Women Business Growth: Evidence from Micro and Small Scale Enterprises in Hawassa City Administration, Sidama Regional State, Ethiopia

Increasing number of women-owned business enterprises has left its mark on the global economy by generating job opportunities, improves women's economic empowerment, gender equality in business, and sustainable economic growth.So studying the topic determinants of women business growth is burning issue that attracted the attention of researcher across the world.To this end, the purpose of this study was to identify factors affecting women operated business growth with reference to MSEs in Hawassa city.In order to achieve this objective, the researchers have used explanatory type research design and quantitative research approach in which six hypotheses have been tested.Primary data has been collected using structured questionnaires.Based on the regressions result, this study revealed that access to finance, access to land, access to technology, communication skill, training and increase in tax amount has statistically significant effect on growth of women operated MSEs in Hawassa city.Also access to land was the best predictor of women business growth and lack of training was the least predictor of women business growth in the study area.Based on the finding of the study, the researchers concluded the growth of women micro and small scale enterprises was significantly influenced individual, organizational and environmental factors incorporated in the model.
Modeling evidential grids using semantic context information for dynamic scene perception

Uncertainty about urban environments stems not only from imprecise pose estimation and noisy information in images but also from the lack of semantic information. This article presents an approach to improve the perception capability of intelligent vehicles in complex urban environments. The new method uses the meta-knowledge extracted from semantic context images associated with depth information to model occupancy grids from stereo vision. It uses the evidential formalism of the Dempster–Shafer theory to manage uncertainties involved in grid discretization, partial observation of the environment and also dynamic elements present in the scene. Real experiments carried out in a challenging urban environment using the KITTI benchmark are reported, from which meaningful evaluations compared to the standard evidential grid are done to show that the proposed method is able to better handle semantic, dynamic and uncertainty aspects in the environment representation.
Learning Parameters of ptSTL Formulas with Backpropagation

In this paper, a backpropagation based algorithm is presented to learn parameters of past time Signal Temporal Logic (ptSTL) formulas. A differentiable weight matrix over the parameter values and a loss function based on the mismatch value of the corresponding formulas over the labeled dataset are used in the algorithm. Analysis over a sample dataset shows that the algorithm solves the ptSTL parameter synthesis problem in an efficient way.
Single-Stage Object Detection from Top-View Grid Maps on Custom Sensor Setups

We present our approach to unsupervised domain adaptation for single-stage object detectors on top-view grid maps in automated driving scenarios. Our goal is to train a robust object detector on grid maps generated from custom sensor data and setups. We first introduce a single-stage object detector for grid maps based on RetinaNet. We then extend our model by image- and instance-level domain classifiers at different feature pyramid levels which are trained in an adversarial manner. This allows us to train robust object detectors for unlabeled domains. We evaluate our approach quantitatively on the nuScenes and KITTI benchmarks and present qualitative domain adaptation results for unlabeled measurements recorded by our experimental vehicle. Our results demonstrate that object detection accuracy for unlabeled domains can be improved by applying our domain adaptation strategy.
Spatial and Temporal Downscaling of Solar Radiation Using Statistical Techniques

We evaluated the forecast performance of solar radiation on the Korean Peninsula from the solar radiation data provided by the UASIBS-KIER model of KIER with the solar radiation forecast data generated by the GFS model of the NCEP.In this study, the spatial and temporal downscaling of the GFS model, a cumulative solar radiation forecast model per 3 hours units, was conducted to measure forecast performance.First, in the case of spatial downscaling, K-means clustering of solar radiation values corresponding to all latitude and longitude values of the Korean Peninsula was performed, and seven clusters were formed.Second, in the case of temporal downscaling, the cumulative solar radiation forecast value over 3 h was converted to the cumulative solar radiation value over 1 h.To measure the downscaling performance, the downscaling results of the cumulative forecast over 1 h of the GFS model were compared with the observed solar radiation values of the UASIBS-KIER model.We performed downscaling by using the same average ratio as the cumulative value over 3 h.In addition, we performed downscaling by using the correlation between the cumulative value over 1 h by the UASIBS-KIER model.Based on the rRMSE and rMAE, the use of the correlation was found to be more efficient that the use of the same average ratio.
Infosys Machine Translation System for WMT20 Similar Language Translation Task

This paper describes Infosys’s submission to the WMT20 Similar Language Translation shared task. We participated in Indo-Aryan language pair in the language direction Hindi to Marathi. Our baseline system is byte-pair encoding based transformer model trained with the Fairseq sequence modeling toolkit. Our final system is an ensemble of two transformer models, which ranked first in WMT20 evaluation. One model is designed to learn the nuances of translation of this low resource language pair by taking advantage of the fact that the source and target languages are same alphabet languages. The other model is the result of experimentation with the proportion of back-translated data to the parallel data to improve translation fluency.
The Skill-Action Architecture: Learning Abstract Action Embeddings for Reinforcement Learning

The option framework, one of the most promising Hierarchical Reinforcement Learning (HRL) frameworks, is developed based on the Semi-Markov Decision Problem (SMDP) and employs a triple formulation of the option (i.e., an action policy, a termination probability, and an initiation set). These design choices, however, mean that the option framework: 1) has low sample efficiency, 2) cannot use more stable Markov Decision Problem (MDP) based learning algorithms, 3) represents abstract actions implicitly, and 4) is expensive to scale up. To overcome these problems, here we propose a simple yet effective MDP implementation of the option framework: the Skill-Action (SA) architecture. Derived from a novel discovery that the SMDP option framework has an MDP equivalence, SA hierarchically extracts skills (abstract actions) from primary actions and explicitly encodes these knowledge into skill context vectors (embedding vectors). Although SA is MDP formulated, skills can still be temporally extended by applying the attention mechanism to skill context vectors. Unlike the option framework, which requires M action policies for M skills, SA's action policy only needs one decoder to decode skill context vectors into primary actions. Under this formulation, SA can be optimized with any MDP based policy gradient algorithm. Moreover, it is sample efficient, cheap to scale up, and theoretically proven to have lower variance. Our empirical studies on challenging infinite horizon robot simulation environments demonstrate that SA not only outperforms all baselines by a large margin, but also exhibits smaller variance, faster convergence, and good interpretability. On transfer learning tasks, SA also outperforms the other models and shows its advantage on reusing knowledge across tasks. A potential impact of SA is to pave the way for a large scale pre-training architecture in the reinforcement learning area.
Comparison Between Facial Expression Recognition Algorithms - For Effective Method

Facial expression is an ancient element for identifying humans. Human behavior, thinking or mood can be easily understood through facial expression. At present, facial expression can be evaluated by algorithm based on facial expression AI. In this paper, comparative studies have been done in methods related to facial recognition and an attempt has been made to evaluate it. Previous and recent research paper has been investigated to find out the related effective method.
NRC Systems for Low Resource German-Upper Sorbian Machine Translation 2020: Transfer Learning with Lexical Modifications

We describe the National Research Council of Canada (NRC) neural machine translation systems for the German-Upper Sorbian supervised track of the 2020 shared task on Unsupervised MT and Very Low Resource Supervised MT. Our models are ensembles of Transformer models, built using combinations of BPE-dropout, lexical modifications, and backtranslation.
Learn by Guessing: Multi-step Pseudo-label Refinement for Person Re-Identification

Unsupervised Domain Adaptation (UDA) methods for person Re-Identification (Re-ID) rely on target domain samples to model the marginal distribution of the data. To deal with the lack of target domain labels, UDA methods leverage information from labeled source samples and unlabeled target samples. A promising approach relies on the use of unsupervised learning as part of the pipeline, such as clustering methods. The quality of the clusters clearly plays a major role in methods performance, but this point has been overlooked. In this work, we propose a multi-step pseudo-label refinement method to select the best possible clusters and keep improving them so that these clusters become closer to the class divisions without knowledge of the class labels. Our refinement method includes a cluster selection strategy and a camera-based normalization method which reduces the within-domain variations caused by the use of multiple cameras in person Re-ID. This allows our method to reach state-of-the-art UDA results on DukeMTMC-Market1501 (source-target). We surpass state-of-the-art for UDA Re-ID by 3.4% on Market1501-DukeMTMC datasets, which is a more challenging adaptation setup because the target domain (DukeMTMC) has eight distinct cameras. Furthermore, the camera-based normalization method causes a significant reduction in the number of iterations required for training convergence.
Strong polygamy of multi-party q-expected quantum correlations

We show that the polygamous nature of multi-party quantum correlations can be characterized in a stronger form based on Tsallis q-entropy and q-expectation value; we establish a class of strong polygamy inequalities of multi-party entanglement in terms of Tsallis q-entropy and q-expectation value for $$q \ge 1$$ . Our new class of inequalities is tighter than the usual polygamy inequalities of multi-party entanglement, and the tightness is explicitly illustrated by an example. Moreover, our new class of inequalities is concerned with the entanglement distributed between a single party and any possible subsets of the rest parties, whereas the usual polygamy inequality only considers the entanglement between one party and another. We further establish the equivalence between strong polygamy of quantum entanglement and quantum discord distributed in multi-party quantum systems.
Digital Watermarking System Performance Using QIM Techniques and Wavelet Transforms

The use of internet increase day by day. Because of this, user can easily transfer digital documents from one place to another. During this process, many security issues arise like copyright protection, authentication, tampering etc. Digital watermarking system is one of the electronic security mechanisms using which these issues can be solved. This paper mainly focuses on the Wavelet Transform methods Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). The performance of both the transforms with digital watermarking system is compared. This comparison is also done based on various wavelet filters like Haar, Symlet, Biorthogonal, etc. To apply the blind watermarking system and to work with Lossy compression format like JPEG, QIM method is suggested. The modification rules are discussed for QIM techniques and the performance is measured with other QIM techniques. In this method, measurement of imperceptibility is done using Peak Signal Noise Ratio (PSNR) whereas measurement of robustness, is done using Bit Error Rate (BER). With IWT and proposed QIM method, the blind watermarking system gives better performance than DWT and other QIM techniques.
A Case for Unikernels in IoT: Enhancing Security and Performance

One of the main concerns in an IoT system is security. The nature of security compromise can be at different levels of software running on the device—operating system vulnerability, application level security bugs, bad passwords, and/or incorrect/misconfigured configurations. Since an IoT device only run a single process, it is possible to secure such IoT systems using Unikernels (i.e., single address space operating systems that have the application and OS functionality into a single address space). This can reduce the attack surface and is easily to verify (formally). Besides this, since the IoT use case is specialized, it lends easily to Unikernels. There are additional side benefits such as low memory footprint, cpu efficiency etc. In this paper the advantages and challenges of such a software stack are shown.
eTranslation’s Submissions to the WMT 2020 News Translation Task

The paper describes the submissions of the eTranslation team to the WMT 2020 news translation shared task. Leveraging the experience from the team’s participation last year we developed systems for 5 language pairs with various strategies. Compared to last year, for some language pairs we dedicated a lot more resources to training, and tried to follow standard best practices to build competitive systems which can achieve good results in the rankings. By using deep and complex architectures we sacrificed direct re-usability of our systems in production environments but evaluation showed that this approach could result in better models that significantly outperform baseline architectures. We submitted two systems to the zero shot robustness task. These submissions are described briefly in this paper as well.
Can Existing Methods Debias Languages Other than English? First Attempt to Analyze and Mitigate Japanese Word Embeddings

It is known that word embeddings exhibit biases inherited from the corpus, and those biases reflect social stereotypes. Recently, many studies have been conducted to analyze and mitigate biases in word embeddings. Unsupervised Bias Enumeration (UBE) (Swinger et al., 2019) is one of approach to analyze biases for English, and Hard Debias (Bolukbasi et al., 2016) is the common technique to mitigate gender bias. These methods focused on English, or, in smaller extent, on Indo-European languages. However, it is not clear whether these methods can be generalized to other languages. In this paper, we apply these analyzing and mitigating methods, UBE and Hard Debias, to Japanese word embeddings. Additionally, we examine whether these methods can be used for Japanese. We experimentally show that UBE and Hard Debias cannot be sufficiently adapted to Japanese embeddings.
Neural Machine Translation for Similar Languages: The Case of Indo-Aryan Languages

In this paper we present the WIPRO-RIT systems submitted to the Similar Language Translation shared task at WMT 2020. The second edition of this shared task featured parallel data from pairs/groups of similar languages from three different language families: Indo-Aryan languages (Hindi and Marathi), Romance languages (Catalan, Portuguese, and Spanish), and South Slavic Languages (Croatian, Serbian, and Slovene). We report the results obtained by our systems in translating from Hindi to Marathi and from Marathi to Hindi. WIPRO-RIT achieved competitive performance ranking 1st in Marathi to Hindi and 2nd in Hindi to Marathi translation among 22 systems.
Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification

Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification (LID). In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness or non-linguists’ perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability to be the best predictor of the language representation similarity.
Management of students' learning activities by means of distance learning: motivational aspect

The article is devoted to solving the problem of discrepancy between the traditional practice of using distance courses in high school and the potential of their application to solve the managerial tasks of motivational content. The purpose of the study is to determine the requirements of the management representatives of training in high school for distance courses as a means of forming students' motives for learning and opening opportunities for their realization, the achievement of which requires prior study of relevant theoretical materials and practical experience. These requirements are considered at four levels of government. The level of pedagogical management (of a teacher) meets the requirements regarding the formation of students' motivation for different types of educational activities, mastering the training course in general, etc
Benefits of using Blockchain technology as an accounting auditing instrument

Purpose: Analyze the possible benefits of using Blockchain technology as a tool for accounting auditing.&#x0D; Methodology: In order to achieve the objective of this study, a theoretical essay was conducted through a selection of national and international literature on accounting auditing and the potential benefits of Blockchain technology, analyzing the relationship of these potential benefits with the procedures adopted by the accounting auditor.&#x0D; Results: Findings from the literature show that despite the complexities, Blockchain technology offers an opportunity to improve financial reporting and audit processes. Adoption of this technology may allow auditors to develop procedures to obtain audit evidence directly from Blockchains and to adapt procedures to reap its benefits, as well as address incremental risks and that while the audit process may become more continuous, auditors may They will still have to apply professional judgment in analyzing accounting estimates and other judgments made by management in preparing the financial statements.&#x0D; Contributions of the Study: It is believed that this work contributes to an active and continuous dialogue regarding the use of Blockchain in audit processes, as well as the knowledge of advantages that this technology offers to professionals, as well as arouse the interest of research on the use of Blockchain technology in accounting auditing.
Online education during the COVID19 pandemic: Perceptions and expectations of romanian students

The COVID-19 pandemic had a disruptive effect over all areas of our lives, including the education system and so, all universities had to rapidly shift from face-to-face teaching to online learning using online solutions and virtual platforms However, what was first seen as a temporary solution for an extraordinary situation starts to look more and more as a medium term general rule and so more research is needed in order to better understand student's perception regarding online learning systems use and the factors influencing their intentions to use online systems The present study was conducted between April and May 2020, through an online survey among 206 university students from Romania, both undergraduate and graduate and aimed at identifying students' perceptions regarding the use of the online learning systems provided by their universities The results of the study can also provide higher education institutions insight and information on how to manage the shift from face-to-face teaching to online learning by taking into consideration student's expectations Previous research showed that the online learning concept and activities were used to some extent by most Romanian universities even before the Covid19 pandemic, but mainly for online presentation of learning materials and short online tests (Traistaru & Cotoc, 2013;Grabara & Bosun, 2014) Forced by this new context, universities rapidly adjusted either by accessing third party platforms, such as Microsoft Teams, Google Classroom or Zoom or by using their in house Learning Managements Systems on Moodle or Blackboard to move all their classes online The present research revealed that although approximately 70% of the students questioned did not have any prior extensive experience in web-based learning, the majority of the sampled students supported the initiative, stating that the online learning platforms provided by the university enables them to accomplish their tasks quicker and also improving to some extent the quality of their work, while also having fun studying The study also revealed that the students have adequate knowledge of the COVID-19 pandemic and they are responsible with respecting the protective measurements imposed © 2020 Academic Conferences Limited All rights reserved