Reviewerly/exHarmony huggingface dataset:
Hide-and-Seek Privacy Challenge.

The clinical time-series setting poses a unique combination of challenges to data modeling and sharing. Due to the high dimensionality of clinical time series, adequate de-identification to preserve privacy while retaining data utility is difficult to achieve using common de-identification techniques. An innovative approach to this problem is synthetic data generation. From a technical perspective, a good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between high-dimensional variables across time. From the privacy perspective, the model should prevent patient re-identification by limiting vulnerability to membership inference attacks. The NeurIPS 2020 Hide-and-Seek Privacy Challenge is a novel two-tracked competition to simultaneously accelerate progress in tackling both problems. In our head-to-head format, participants in the synthetic data generation track (i.e. hiders) and the patient re-identification track (i.e. seekers) are directly pitted against each other by way of a new, high-quality intensive care time-series dataset: the AmsterdamUMCdb dataset. Ultimately, we seek to advance generative techniques for dense and high-dimensional temporal data streams that are (1) clinically meaningful in terms of fidelity and predictivity, as well as (2) capable of minimizing membership privacy risks in terms of the concrete notion of patient re-identification.
COVR: A Test-Bed for Visually Grounded Compositional Generalization with Real Images

While interest in models that generalize at test time to new compositions has risen in recent years, benchmarks in the visually-grounded domain have thus far been restricted to synthetic images.In this work, we propose COVR, a new test-bed for visually-grounded compositional generalization with real images.To create COVR, we use real images annotated with scene graphs, and propose an almost fully automatic procedure for generating question-answer pairs along with a set of context images.COVR focuses on questions that require complex reasoning, including higherorder operations such as quantification and aggregation.Due to the automatic generation process, COVR facilitates the creation of compositional splits, where models at test time need to generalize to new concepts and compositions in a zero-or few-shot setting.We construct compositional splits using COVR and demonstrate a myriad of cases where state-ofthe-art pre-trained language-and-vision models struggle to compositionally generalize.
Towards a Design Space for Shadows as Ambient Displays

In this paper, we present a design space for using shadows in interactive systems. The main elements of the design space are optical properties, dynamicity, reality, temporality, and projection surface. The paper contributes a systematic approach for designing interactive systems with shadows, adding to the prior art, which consists mostly of focused case studies.
Rethinking Deinterlacing for Early Interlaced Videos

In recent years, high-definition restoration of early videos have received much attention. Real-world interlaced videos usually contain various degradations mixed with interlacing artifacts, such as noises and compression artifacts. Unfortunately, traditional deinterlacing methods only focus on the inverse process of interlacing scanning, and cannot remove these complex and complicated artifacts. Hence, this paper proposes an image deinterlacing network (DIN), which is specifically designed for joint removal of interlacing mixed with other artifacts. The DIN is composed of two stages, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , a cooperative vertical interpolation stage for splitting and fully using the information of adjacent fields, and a field-merging stage to perceive movements and suppress ghost artifacts. Experimental results demonstrate the effectiveness of the proposed DIN on both synthetic and real-world test sets.
Implementasi E-Learning Berbasis Moodle di Masa Pandemi Covid 19

This study aims to determine the results of the implementation of moodle-based e-learning. This type of research is a descriptive analysis with a frequency distribution, namely concluding based on the data obtained during the study. The research sample was 71 people. The results of the study found that there were 19.3% of students who answered strongly agree; 61.5% of students who answered agreed; 16.4% answered disagree 2.7% answered strongly disagreed with using moodle-based e-learning during the Covid-19 pandemic. For some students who answered disagree and strongly disagreed, it was because the student concerned did not yet have a laptop and their place of residence was far from urban areas. In conclusion, the use of moodle-based e-learning is very appropriate, in order to overcome conventional learning activities that cannot be done due to the COVID-19 pandemic and the availability of facilities and infrastructure is very much needed so that learning outcomes can be realized.&#x0D; Keywords: e-Learning, Moodle
Charting Experience Categories for Museum Exhibitions

In this poster, we chart and identify user experience categories with museum experiences. The experience categories were searched from narratives people provided in a survey-based user study (n = 48), where people reflected their positive and negative experiences, technology use and possible ethical concerns in museum exhibitions they had visited. Cultural heritage is a special design domain, as it can involve not only fragile old materials but also cultural sensitivities related to the events in history. The paper highlights the role of interactivity and multi-sensory experiences in museums, and our research contributes background knowledge for designers and practitioners working with museum exhibits.
Limitations of Knowledge Distillation for Zero-shot Transfer Learning

Pretrained transformer-based encoders such as BERT have been demonstrated to achieve state-of-the-art performance on numerous NLP tasks. Despite their success, BERT style encoders are large in size and have high latency during inference (especially on CPU machines) which make them unappealing for many online applications. Recently introduced compression and distillation methods have provided effective ways to alleviate this shortcoming. However, the focus of these works has been mainly on monolingual encoders. Motivated by recent successes in zero-shot cross-lingual transfer learning using multilingual pretrained encoders such as mBERT, we evaluate the effectiveness of Knowledge Distillation (KD) both during pretraining stage and during fine-tuning stage on multilingual BERT models. We demonstrate that in contradiction to the previous observation in the case of monolingual distillation, in multilingual settings, distillation during pretraining is more effective than distillation during fine-tuning for zero-shot transfer learning. Moreover, we observe that distillation during fine-tuning may hurt zero-shot cross-lingual performance. Finally, we demonstrate that distilling a larger model (BERT Large) results in the strongest distilled model that performs best both on the source language as well as target languages in zero-shot settings.
Implementation of Blockchain Technology in the Tourism Industry: A Systematic Literature Review

Purpose/objectives: in the last ten years, the implementation of blockchain (BC) technology offers many opportunities for all economic sectors such as finance, health, trade, agriculture, etc.This technology based on distributed digital ledger collects and stores data permanently and in an ordered way, allowing stakeholders to have access to information cryptologically protected.Since 2014, the blockchain technology has been introduced in the tourism industry with the goal to improve this sector.In this contest the present study intents to: 1) carrying out a systematic literature review (SLR) on the implementation of BC technology in tourism economic sector; 2) providing an analysis of its advantages and future challenges.-Design/methodology: The SLR was conducted using detailed criteria to identify scientific papers.Moreover, specific keywords and databases were chosen.The time frame considered included the years 2017-2021 (31 st March).-Findings: the review analysis indicates that the use of BC technology contributes to increase transparency and safety of the services provided and decrease cost above all of tourist.However, some challenges remain open which further studies could try to solve.-Originality/value: to date the academic literature published on this topic is limited, so these aspectshave not yet been adequately analyzed.Therefore, the present article intends to show an update framework about BC technology and its impact on tourism industry.-Possible practical implications: results might be helpful for stakeholders understand the potential impact of this technology on the business tourism ecosystem and for scholars to better define future research areas and trend less explored in this field.
Enhanced Dense Space Attention Network for Super-Resolution Construction From Single Input Image

In some applications, such as surveillance and biometrics, image enlargement is required to inspect small details on the image.One of the image enlargement approaches is by using convolutional neural network (CNN)-based super-resolution construction from a single image.The first CNN-based image superresolution algorithm is the super-resolution CNN (SRCNN) developed in 2014.Since then, many researchers have proposed several versions of CNN-based algorithms for image super-resolution to improve the accuracy or reduce the model's running time.Currently, some algorithms still suffered from the vanishing-gradient problem and relied on a large number of layers.Thus, the motivation of this work is to reduce the vanishinggradient problem that can improve the accuracy, and at the same time, reduce the running time of the model.In this paper, an enhanced dense space attention network (EDSAN) model is proposed to overcome the problems.The EDSAN model adopted a dense connection and residual network to utilize all the features to correlate the low-level feature and high-level feature as much as possible.Besides, implementing the convolution block attention module (CBAM) layer and multiscale block (MSB) helped reduce the number of layers required to achieve comparable results.The model is evaluated through peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) metrics.EDSAN achieved the most significant improvement, about 1.42% when compared to the CRN model using the Set5 dataset at a scale factor of 3. Compared to the ERN model, EDSAN performed the best, with a 1.22% improvement made when using the Set5 dataset at a scale factor of 4. In terms of overall performance, EDSAN performed very well in all datasets at a scale factor of 2 and 3.In conclusion, EDSAN successfully solves the problems above, and it can be used in different applications such as biometric identification applications and real-time video applications.
Online Learning of English Phonology in Saudi Arabia: Investigating College Students’ Perceptions during the Pandemic Period

EFL curricula in Saudi Arabia are very comprehensive, with all components of language included in the syllabus. Phonology, or the study of speech sounds, is especially significant in the context of foreign language learning, given its central role in verbal communication. Accuracy of production is a factor of precision in the instruction, which brings in its offing learning challenges in the virtual mode. In the wake of the Covid-19 imposed norms EFL education in Saudi Arabia has undergone drastic changes, giving education new dimensions that deserve investigation. The current study investigates learner perceptions of English phonology online instruction to check its efficacy in the changed paradigm. The study is quantitative in nature, the participants are undergraduate female EFL learners at Qassim University, and the instrument is an online survey. Measurements over six factors indicate that the sample population hold positive perspectives for English phonology virtual learning, the feature that they demonstrate the highest positive attitude for is the teacher’s interactive role, whereas pair and group discussion find the least favor with them. The study has long term implications in foreign language learning environments of the future as virtual learning appears to be here to stay.
Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modeling and Automation

In surface mining, assay measurements taken from production drilling often provide useful information that enables initially inaccurate surfaces (for example, mineralization boundaries) created using sparse exploration data to be revised and subsequently improved. Recently, a Bayesian warping technique was proposed to reshape modeled surfaces using geochemical observations and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning (ML) into this warping framework to make the likelihood computation generalizable. The technique works by adjusting the position of vertices on the surface to maximize the integrity of modeled geological boundaries with respect to sparse geochemical observations. Its foundation is laid by a Bayesian derivation in which the geological domain likelihood given the chemistry, $$p(g\!\mid \!{\mathbf {c}})$$ , plays a role similar to $$p(y({\mathbf {c}})\!\mid \! g)$$ for certain categorical mappings $$y:{\mathbb {R}}^K\rightarrow {\mathbb {Z}}$$ . This observation allows a manually calibrated process centered on the latter to be automated, since ML techniques may be used to estimate the former in a data-driven way. Machine learning performance is evaluated for gradient boosting, neural network, random forest, and other classifiers in a binary and multi-class context using precision and recall rates. Once ML likelihood estimators are integrated into the surface warping framework, surface shaping performance is evaluated using unseen data by examining the categorical distribution of test samples located above and below the warped surface. Large-scale validation experiments are performed to assess the overall efficacy of ML-assisted surface warping as a fully integrated component within an ore grade estimation system, where the posterior mean is obtained via Gaussian process (GP) inference with a Matérn 3/2 kernel. This article illustrates an application of machine learning within a complex system where grade estimation is accomplished by integrating boundary warping with ML and other components.
A SURVEY ON GREEN CLOUD COMPUTING

Green cloud computing is about designing cloud computing resources in a manner which reduces its impact on the surrounding environment. Green clouds can save a lot of energy and are also capable of reducing operational costs. This is a necessity today as global warming levels continue to rise across the planet. As more and more businesses shift towards cloud, it is important for all cloud providers to adopt green cloud computing. This paper provides an overview of green cloud computing and also talks about the ways in which it can be achieved. It also talks about the applications and challenges being faced for the implementation of green clouds
Language learning strategies employed by higher and lower achieving students in learning speaking

The objectives of this research were to find out the types of language learning strategies employed by higher and lower achieving students on speaking skill and to find out the impacts of language learning strategies employed by them. A questionnaire was used as the instrument of this study. The sample of this research were 82 students who were chosen based on the criteria: (1) the students already passed all the Speaking I to IV classes, (2) the students consist of higher achieving students (score ≥B) and lower achieving students (score ≤C), and (3) the students allowed the researcher to obtain their speaking scores. Thus, purposive sampling was used here. In this research, the researcher used Strategy Inventory for Language Learning (SILL) by Oxford (2003) to determine the students’ language learning strategy. The result revealed that the higher achieving students used memory, cognitive, compensation, metacognitive, affective, and social strategies in learning speaking. On the other hand, the lower achieving students generally did not use the learning strategies in their learning activities. Therefore, as the impact of this learning habit, they do not possess a good speaking ability and achieve low scores in speaking class.
A CRYPTO SYSTEM USING VIGENERE AND POLYBIUS CIPHER

The Cryptography is a Greek word which implies the craft of ensuring data by changing it into a muddled organization and unreadable format. Cryptography is a method to secure sensitive data for storage and communication in the presence of third parties called adversaries. It is a mix of arithmetic and software engineering. The drastic growth of the Internet over a period of time made an unintentional security. Even though security is the measure worries over the internet, numerous applications have been created and structured without considering fundamental destinations of data security that is confidentiality, authentication, and protection. To gain some undesirable clients or individuals to gain admittance to the data, cryptography is required. This paper introduces a new hybrid security cipher by combining the two most important Ciphers such as Polybius Cipher and Vigenere Cipher. This hybrid encryption cipher provides greater security as compared to classic ciphers.
Integral and impossible‐differential attacks on the reduced‐round Lesamnta‐LW‐BC

Lesamnta-LW-BC is the internal block cipher of the Lesamnta-LW lightweight hash function, specified in ISO/IEC 29192-5:2016. It is based on the unbalanced Feistel network and Advanced Encryption Standard round function. In this study, the security of Lesamnta-LW-BC against integral and impossible-differential attacks is evaluated. Specifically, the authors searched for the integral distinguishers and impossible differentials with Mixed-Integer Linear Programming-based methods. As a result, the discovered impossible differential can reach up to 21 rounds, while three integral distinguishers reaching 18, 19 and 25 rounds are obtained, respectively. Moreover, it is also feasible to construct a 47-round integral distinguisher in the known-key setting. Finally, a 20-round key-recovery attack is proposed based on the discovered 18-round integral distinguisher and a 19-round key-recovery attack using a 17-round impossible differential. To the best of the authors' knowledge, this is the first third-party cryptanalysis of Lesamnta-LW-BC.
Content-based image retrieval for fabric images: A survey

In &lt;span&gt;recent years, a great deal of research has been conducted in the area of fabric image retrieval, especially the identification and classification of visual features. One of the challenges associated with the domain of content-based image retrieval (CBIR) is the semantic gap between low-level visual features and high-level human perceptions. Generally, CBIR includes two main components, namely feature extraction and similarity measurement. Therefore, this research aims to determine the content-based image retrieval for fabric using feature extraction techniques grouped into traditional methods and convolutional neural networks (CNN). Traditional descriptors deal with low-level features, while CNN addresses the high-level, called semantic features. Traditional descriptors have the advantage of shorter computation time and reduced system requirements. Meanwhile, CNN descriptors, which handle high-level features tailored to human perceptions, deal with large amounts of data and require a great deal of computation time. In general, the features of a CNN's fully connected layers are used for matching query and database images. In several studies, the extracted features of the CNN's convolutional layer were used for image retrieval. At the end of the CNN layer, hash codes are added to reduce &lt;/span&gt;search time.
Study on Acoustic Model Personalization in a Context of Collaborative Learning Constrained by Privacy Preservation

This paper investigates different approaches in order to improve the performance of a speech recognition system for a given speaker by using no more than 5 min of speech from this speaker, and without exchanging data from other users/speakers. Inspired by the federated learning paradigm, we consider speakers that have access to a personalized database of their own speech, learn an acoustic model and collaborate with other speakers in a network to improve their model. Several local personalizations are explored depending on how aggregation mechanisms are performed. We study the impact of selecting, in an adaptive way, a subset of speakers’s models based on a notion of similarity. We also investigate the effect of weighted averaging of fine-tuned and global models. In our approach, only neural acoustic model parameters are exchanged and no audio data is exchanged. By avoiding communicating their personal data, the proposed approach tends to preserve the privacy of speakers.Experiments conducted on the TEDLIUM 3 dataset show that the best improvement is given by averaging a subset of different acoustic models fine-tuned on several user datasets. Our approach applied to HMM/TDNN acoustic models improves quickly and significantly the ASR performance in terms of WER (for instance in one of our two evaluation datasets, from 14.84% to 13.45% with less than 5 min of speech per speaker) .
Deep Reinforcement Learning Approach with Adaptive Reward System for Robot Navigation in Dynamic Environments

Deep reinforcement learning (DRL) has the ability to learn and solve complex and challenging tasks when compared to traditional robot navigation methods. In this study, using DRL robot navigation developing with the ability to detect in a dynamic environment is researched. The robot has no prior knowledge of the environment in navigation tasks. In addition, the position of the random moving obstacle and target changes with each navigation task. Because of this, the navigation task has become more difficult, and the classic reward system has fallen behind. In this study, we propose a new adaptive reward system instead of the classic reward system used in Deep Q-Network (DQN) of DRL to increase efficiency of this algorithm. The results of the classical reward system and the new adaptive reward system were compared with other studies in the literature. As a result of these comparisons, we saw that the reward system we proposed was successful in the success rate and the number of reaching multiple targets.
Super-resolution reconstruction of seismic section image via multi-scale convolution neural network

The resolution of seismic section images can directly affect the subsequent interpretation of seismic data. In order to improve the spatial resolution of low-resolution seismic section images, a super-resolution reconstruction method based on multi-scale convolution is proposed. This method designs a multi-scale convolutional neural network to learn high-low resolution image feature pairs, and realizes mapping learning from low-resolution seismic section images to high-resolution seismic section images. This multi-scale convolutional neural network model consists of four convolutional layers and a sub-pixel convolutional layer. Convolution operations are used to learn abundant seismic section image features, and sub-pixel convolution layer is used to reconstruct high-resolution seismic section image. The experimental results show that the proposed method is superior to the comparison method in peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the total training time and reconstruction time, our method is about 22% less than the FSRCNN method and about 18% less than the ESPCN method.