Reviewerly/exHarmony huggingface dataset:
FrenLyS: A Tool for the Automatic Simplification of French General Language Texts

Lexical simplification (LS) aims at replacing words considered complex in a sentence by simpler equivalents.In this paper, we present the first automatic LS service for French, FrenLyS, which offers different techniques to generate, select and rank substitutes.The paper describes the different methods proposed by our tool, which includes both classical approaches (e.g. generation of candidates from lexical resources, frequency filter, etc.) and more innovative approaches such as the exploitation of CamemBERT, a model for French based on the RoBERTa architecture.To evaluate the different methods, a new evaluation dataset for French is introduced.
RRFT: A Rank-Based Resource Aware Fault Tolerant Strategy for Cloud Platforms

The applications that are deployed in the cloud to provide services to the users encompass a large number of interconnected dependent cloud components. Multiple identical components are scheduled to run concurrently in order to handle unexpected failures and provide uninterrupted service to the end user, which introduces resource overhead problem for the cloud service provider. Furthermore such resource-intensive fault tolerant strategies bring extra monetary overhead to the cloud service provider and eventually to the cloud users. In order to address these issues, a novel fault tolerant strategy based on the significance level of each component is developed. The communication topology among the application components, their historical performance, failure rate, failure impact on other components, dependencies among them, etc., are used to rank those application components to further decide on the importance of one component over others. Based on the rank, a Markov Decision Process (MDP) model is presented to determine the number of replicas that varies from one component to another. A rigorous performance evaluation is carried out using some of the most common practically useful metrics such as, recovery time upon a fault, average number of components needed, number of parallel components successfully executed, etc., to quote a few, with similar component ranking and fault tolerant strategies. Simulation results demonstrate that the proposed algorithm reduces the required number of virtual and physical machines by approximately 10% and 4.2%, respectively, compared to other similar algorithms.
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

Recent pretrained language models extend from millions to billions of parameters. Thus the need to fine-tune an extremely large pretrained model with a limited training corpus arises in various downstream tasks. In this paper, we propose a straightforward yet effective fine-tuning technique, Child-Tuning, which updates a subset of parameters (called child network) of large pretrained models via strategically masking out the gradients of the non-child network during the backward process. Experiments on various downstream tasks in GLUE benchmark show that Child-Tuning consistently outperforms the vanilla fine-tuning by 1.5~8.6 average score among four different pretrained models, and surpasses the prior fine-tuning techniques by 0.6~1.3 points. Furthermore, empirical results on domain transfer and task transfer show that Child-Tuning can obtain better generalization performance by large margins.
Local Phase Correction Method Based on Multi-Frequency Phase Heterodyne

The multi-frequency phase heterodyne method is the most common method applied in the unwrap phase, but it failed to reduce the noise error in the image and the sinusoidal error from the projector. In order to address these issues, this study proposes a local phase correction method. This study starts to determine whether a phase map needs to be corrected by calculating the deviation between the predicted and actual phase according to the slope of the projected standard fringe pattern. Then the phase is corrected by different intensities at different sections. Finally, the method compensates the phase error of the detail part with the minimum error phase correction. Experimental results showed that the local phase correction method is the optimal phase correction method, which reduced the phase error by 86.5%. Finally, the phase map of the metal workpiece surface was calculated by the various methods. And the phase map with the highest quality was obtained by using the local phase correction method.
Narrative Embedding: Re-Contextualization Through Attention

Narrative analysis is becoming increasingly important for a number of linguistic tasks including summarization, knowledge extraction, and question answering.We present a novel approach for narrative event representation using attention to re-contextualize events across the whole story.Comparing to previous analysis we find an unexpected attachment of event semantics to predicate tokens within a popular transformer model.We test the utility of our approach on narrative completion prediction, achieving state of the art performance on Multiple Choice Narrative Cloze and scoring competitively on the Story Cloze Task.
Implementation of Naive Bayes Algorithm on Sentiment Analysis Application

Natural Language Processing (NLP) is one of the branches of artificial intelligence science where this branch science is the basis for developing sentiment analysis.The application in NLP in sentiment analysis includes Pre-processing text consisting of featured selection and tokenization.For the classification process, the determination of the algorithm is determined by comparing the results of the classification predictions of naïve Bayes, Weighted Instances, and Zero-R with the data that has been calculated for its frequency terms.The results of the testing analysis showed naïve Bayes had a stable accuracy after being tested with an accuracy value of 99.62% in the training data and 94% in the Testing Data, with an average classification failure of 0.13%.The results of the acquisition of words are used as a corpus for the construction of sentiment-level sentence analysis applications.The application development by the Naive Bayes algorithm was built using the PHP programming language and literary library.The method of application development using the waterfall starts from the analysis process to the application implementation.Based on testing the accuracy of 30 comments classified by the system, it produces an accuracy value of 86.66%.However, the accuracy of comments that have been classified as applications were retested using machine learning Weka which resulting in an accuracy value of 93.33%.The difference in accuracy is due to the Naive Bayes algorithm in utilizing the appearance of words to form a sentiment classification.
Next-Generation Passive Optical Network Based on Sparse Code Multiple Access and Graph Neural Networks

Sparse code multiple access (SCMA) is a promising technology to provide high throughput and overall improved system performance at affordable cost for next-generation passive optical networks (NG-PONs).Message passing algorithm (MPA) based on a factor graph is usually used for low-complexity multiuser detection in SCMA.However, MPA requires accurate and effective channel estimation due to the interference between the user's signals on the same resource block and suffers uncertain convergence caused by the cycles in the factor graph.In this paper, a graph neural networks (GNN)-based detection method is proposed for SCMA-PON, which performs channel impairment compensation and signal detection in a joint manner.25.5 Gb/s SCMA-OFDM system over 20/60 km single mode fiber link is simulated to demonstrate the feasibility with 8.5-G class optical devices.The simulation results show that GNN-based detection method outperforms MPA and is more robust to the nonlinear distortion for the same level of computational complexity.
Wasserstein Selective Transfer Learning for Cross-domain Text Mining

Transfer learning (TL) seeks to improve the learning of a data-scarce target domain by using information from source domains.However, the source and target domains usually have different data distributions, which may lead to negative transfer.To alleviate this issue, we propose a Wasserstein Selective Transfer Learning (WSTL) method.Specifically, the proposed method considers a reinforced selector to select helpful data for transfer learning.We further use a Wasserstein-based discriminator to maximize the empirical distance between the selected source data and target data.The TL module is then trained to minimize the estimated Wasserstein distance in an adversarial manner and provides domain invariant features for the reinforced selector.We adopt an evaluation metric based on the performance of the TL module as delayed reward and a Wasserstein-based metric as immediate rewards to guide the reinforced selector learning.Compared with the competing TL approaches, the proposed method selects data samples that are closer to the target domain.It also provides better state features and reward signals that lead to better performance with faster convergence.Extensive experiments on three real-world text mining tasks demonstrate the effectiveness of the proposed method.
ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation

Unsupervised domain adaptive classifcation intends to improve the classifcation performance on unlabeled target domain. To alleviate the adverse effect of domain shift, many approaches align the source and target domains in the feature space. However, a feature is usually taken as a whole for alignment without explicitly making domain alignment proactively serve the classifcation task, leading to sub-optimal solution. In this paper, we propose an effective Task-oriented Alignment (ToAlign) for unsupervised domain adaptation (UDA). We study what features should be aligned across domains and propose to make the domain alignment proactively serve classifcation by performing feature decomposition and alignment under the guidance of the prior knowledge induced from the classifcation task itself. Particularly, we explicitly decompose a feature in the source domain into a task-related/discriminative feature that should be aligned, and a task-irrelevant feature that should be avoided/ignored, based on the classifcation meta-knowledge. Extensive experimental results on various benchmarks (e.g., Offce-Home, Visda-2017, and DomainNet) under different domain adaptation settings demonstrate the effectiveness of ToAlign which helps achieve the state-of-the-art performance. The code is publicly available at https://github.com/microsoft/UDA
Gradient-Based Adversarial Factual Consistency Evaluation for Abstractive Summarization

Neural abstractive summarization systems have gained significant progress in recent years.However, abstractive summarization often produce inconsisitent statements or false facts.How to automatically generate highly abstract yet factually correct summaries?In this paper, we proposed an efficient weaksupervised adversarial data augmentation approach to form the factual consistency dataset.Based on the artificial dataset, we train an evaluation model that can not only make accurate and robust factual consistency discrimination but is also capable of making interpretable factual errors tracing by backpropagated gradient distribution on token embeddings.Experiments and analysis conduct on public annotated summarization and factual consistency datasets demonstrate our approach effective and reasonable.Our codes can be found at https://github.com/parZival27/GrAdualCC
Forecasting COVID-19 Cases in Indonesia Using Hybrid Double Exponential Smoothing

The COVID-19 epidemic has spread throughout countries around the world. In Indonesia, this case was detected in early March 2020, and until now, there is still an increase in positive cases of COVID-19. The purpose of this paper is to predict COVID-19 cases in Indonesia using a time series approach. The method used is H-WEMA method because this method can capture trend data patterns following the conditions of COVID-19 cases in Indonesia. Based on the analysis results, H-WEMA can predict COVID-19 cases very well. The forecasted results of the COVID-19 cases in Indonesia still have an upward trend, so it needs the cooperation of all elements of community to reduce the spread of COVID-19.
Digitally Assessing Text Comprehension in Grades 3-4: Test Development and Validation

A prerequisite for child reading support at school is adequate assessment. Embedding (repeated) assessment into daily teaching routine is often challenging for teachers in terms of time and organization. The use of digital tools can help teachers in the assessment process (in preparation, evaluation, documentation, etc.). A digital assessment tool (Graz Reading Comprehension test: GraLeV), focusing on assessing reading comprehension skills in Grades 3 and 4 is currently being developed in Austria. This reading assessment covers reading comprehension at the word, sentence, and text level. Text level is assessed via two subtests (Subtest I: presentation of nonsense-stories and corresponding questions, and Subtest II: maze selection). The other levels consist of one subtest each. This paper focusses on the subtests at text level. More specifically, the paper reports the results of two studies. Study 1 describes the development phases and the first piloting of these two subtests (data collection: 10/2019-12/2019). Testing 273 students with preliminary versions of the subtests (Subtest I: 30 items, Subtest II: 60 items) produced information on (a) item difficulty, (b) item discriminatory power, and (c) time limits for future speed testing. Items not meeting the required quality criteria were excluded. The final version of Subtest I consists of 16 questions referring to eight different, short, nonsense-texts. Its testing time (without instructions) is three minutes. The final version of the Subtest II consists of 2 texts each with 15 maze selections (30 items) and testing time is 100 seconds. The internal consistency is found to be good for Subtest I (α=.87) and Subtest II (α=.78 to .80). Study 2 reports on testing for validity and retest-reliability (data collection: 09/2020-11/2020). Student scores in another reading comprehension test, together with teacher assessments of reading comprehension, were used to assess congruent validity. Divergent validity was assessed using teacher assessments of mathematical and socio-emotional skills. As expected, the correlations with the congruent measures were higher than those with the divergent measures. A subsample was tested twice with the GraLeV. Retest-reliability was acceptable for Subtest II. However, the scores obtained at time 2 were higher compared to those at time 1 in both subtests. This is probably the result of increased student familiarity with the digital device and the digital test environment at time 2. The results are discussed in the light of teachers’ needs for standardized digital assessments in order to facilitate the tailoring of student reading support.
Learning from Temporal Gradient for Semi-supervised Action Recognition

Semi-supervised video action recognition tends to enable deep neural networks to achieve remarkable performance even with very limited labeled data. However, existing methods are mainly transferred from current image-based methods (e.g., FixMatch). Without specifically utilizing the temporal dynamics and inherent multimodal attributes, their results could be suboptimal. To better leverage the encoded temporal information in videos, we introduce temporal gradient as an additional modality for more attentive feature extraction in this paper. To be specific, our method explicitly distills the fine-grained motion representations from temporal gradient (TG) and imposes consistency across different modalities (i.e., RGB and TG). The performance of semi-supervised action recognition is significantly improved without additional computation or parameters during inference. Our method achieves the state-of-the-art performance on three video action recognition benchmarks (i.e., Kinetics-400, UCF-101, and HMDB-51) under several typical semi-supervised settings (i.e., different ratios of labeled data).
Privacy-preserving FairSwap: Fairness and privacy interplay

Abstract Fair exchange protocols are among the most important cryptographic primitives in electronic commerce. A basic fair exchange protocol requires that two parties who want to exchange their digital items either receive what they have been promised, or lose nothing. Privacy of fair exchange requires that no one else (other than the two parties) learns anything about the items. Fairness and privacy have been considered as two distinct properties of an exchange protocol. In this paper, we show that subtle ways of leaking the exchange item to the third parties affect fairness in fair exchange protocols when the item is confidential. Our focus is on Fair-Swap, a recently proposed fair exchange protocol that uses a smart contract for dispute resolution, has proven security in UC (Universal Composability) framework, and provides privacy when both parties are honest. We demonstrate, however, that FairSwap’s dispute resolution protocol leaks information to the public and this leakage provides opportunities for the dishonest parties to influence the protocol’s fairness guarantee. We then propose an efficient privacy-enhanced version of Fair-Swap, prove its security and give an implementation and performance evaluation of our proposed system. Our privacy enhancement uses circuit randomization, and we prove its security and privacy in an extension of universal composability model for non-monolithic adversaries that would be of independent interest.
TEACHING INFORMATICS IN UPPER SECONDARY SCHOOL - PREPARING STUDENTS FOR THE FUTURE

K. Salum1, P. Luik1, M. Lepp1, M. Laanpere2 1University of Tartu (ESTONIA) 2Tallinn University (ESTONIA)
Efficient exploratory clustering analyses in large-scale exploration processes

Abstract Clustering is a fundamental primitive in manifold applications. In order to achieve valuable results in exploratory clustering analyses, parameters of the clustering algorithm have to be set appropriately, which is a tremendous pitfall. We observe multiple challenges for large-scale exploration processes. On the one hand, they require specific methods to efficiently explore large parameter search spaces. On the other hand, they often exhibit large runtimes, in particular when large datasets are analyzed using clustering algorithms with super-polynomial runtimes, which repeatedly need to be executed within exploratory clustering analyses. We address these challenges as follows: First, we present LOG-Means and show that it provides estimates for the number of clusters in sublinear time regarding the defined search space, i.e., provably requiring less executions of a clustering algorithm than existing methods. Second, we demonstrate how to exploit fundamental characteristics of exploratory clustering analyses in order to significantly accelerate the (repetitive) execution of clustering algorithms on large datasets. Third, we show how these challenges can be tackled at the same time. To the best of our knowledge, this is the first work which simultaneously addresses the above-mentioned challenges. In our comprehensive evaluation, we unveil that our proposed methods significantly outperform state-of-the-art methods, thus especially supporting novice analysts for exploratory clustering analyses in large-scale exploration processes.
Cryptocurrency in India: to Ban or not to Ban

The author in this present article has tried to trace the evolution of Cryptocurrency in India. Furthermore, he analysed the Cryptocurrency jurisprudence in different jurisdictions wherein he found that some countries have banned it and some have not. He also tries to understand India's stance on Cryptocurrency and the implications of the Cryptocurrency and Regulation of Official Digital Currency Bill, 2021 which puts a blanket ban on ‘private’ Cryptocurrencies such as Bitcoin and introduce its own digital currency which is often termed as ‘Central Bank Digital Currency’ (‘CBDC’) like Tunisia’s eDinar (launched in 2015), or the most recent China’s digital Yuan (2020).
Pharmacy Students’ Perceptions and Performance from a Microlearning-based Virtual Practical on the Elucidation of Absolute Configuration of Drugs

Abstract: The Covid-19 Movement Control Order imposed by the government converted the delivery of lessons to online learning in the education sector. Pharmaceutical chemistry is a core subject for pharmacy students and first-year students were taught the elucidation of the absolute configuration of active pharmaceutical ingredients (API). The objective of this study is to investigate the pharmacy students’ perceptions and performance on learning new knowledge related to the concept of absolute configuration on API through micro-learning. Students were introduced and assessed on the stereochemistry of new terminologies through micro-learning activities. Various 3D molecular model capture in video format was uploaded on YouTube(YT) and embedded in the Google Form(GF) online assessment. The students’ video navigation was captured with YT video analytics, students learning performance was through GF quiz assessment responses, and the student’s attitudes through questionnaire survey. In conclusion, the performance of student pharmacists in learning and applying new knowledge delivered through online distance micro-learning was comparable to face-to-face active learning. Both YT video analytics and GF responses allowed real-time monitoring and interventions. Nonetheless, students required more time to learn new knowledge online compared to face-to-face active learning. In conclusion, the designed virtual micro-learning enabled students to learn bite-sized new knowledge while training their spatial ability through the application of 3D molecular models with reinforcement through quizzes. The virtual micro-learning design is practical and economical without much technological investment or training for both the educator and learner with easy access in an open distance learning environment and post-Covid.&#x0D; &#x0D; Keywords: Absolute configuration, Analytics, Micro-learning, Online assessment, Video
Development of a Genetic Method for X-ray Images Analysis based on a Neural Network Model

Background: Modern medicine depends on technical advances in the field of medical instrumentation and the development of medical software. One of the most important tasks for doctors is determination of the exact boundaries of tumors and other abnormal formations in the tissues of the human body. Objective: The paper considers the problems and methods of machine classification and recognition of radiographic images, as well as the improvement of artificial neural networks used to increase the quality and accuracy of detection of abnormal structures on chest radiographs. Methods: A modified genetic method for the optimization of parameters of the model on the basis of a convolutional neural network was developed to solve the problem of recognition of diagnostically significant signs of pneumonia on an X-ray of the lungs. The fundamental difference between the proposed genetic method and existing analogs is in the use of a special mutation operator in the form of an additive convolution of two mutation operators, which reduces neural network training time and also identifies "oneighborhood of solutions" that is most suitable for investigation. Results: A comparative evaluation of the effectiveness of the proposed method and known methods was given. It showed an improvement in accuracy of solving the problem of finding signs of pathology on an X-ray of the lungs. Conclusion: Practical use of the developed method will reduce complexity, increase reliability of search, accelerate the process of diagnosis of diseases and reduce a part of errors and repeated inspections of patients.