Reviewerly/exHarmony huggingface dataset:
Petrography of the Mesozoic Alkaline Rocks of the Medvedev Massif (South Yakutia, Aldan-Stanovoy Shield, Leglier Ore Cluster)

Abstract The article considers petrographic compositions of the Mesozoic alkaline magmatic rocks of the Medvedey massif. The latter is a part of the Central Aldan ore district and is spatially located in the central part of the Nymnyr block. The massif is a multiphase structure, of the most productive stage of development of the territory – the Mesozoic tectonic-magmatic activation of the Aldan-Stanovoy shield. The main purpose of this work was to identify qualitative quantitative-mineralogical characteristics of the massif rocks, potential for gold mineralization, using crystal-optical methods. As a result of the field works and petrographic studies of the Mesozoic alkaline magmatic rocks, it was determined that, The Medvedev massif has a zonal structure and is represented by three phases of intrusion with distinct contacts between the latter, which rocks differ in structural and material features. The rocks of the first phase of the massif are composed considerably of leucocrate (the content of dark-colored minerals is up to 9%) syenite-porphyry with elements of the monzonite structure. Magmatic formations of the second phase of the massif differ from those of the first phase primarily in structural features and are represented by hypidiomorphic granular differences with an increased content of dark-colored minerals in the rocks up to 15%. The rocks of the final phase have a noticeable difference from the first two phases and are represented by mesocratic monzonite porphyry with a clearly expressed monzonite structure with a dark-colored mineral content up to 20%. Within the Central Aldan ore region, the large Samolazovskoye gold deposit is associated with similar multiphase massifs, in particular with the Yukhtin massif. During field works, it was found that gold-ore mineralization is associated with the massif, which formation is related to an intensively occurred contact-metasomatic effect on the host rocks. Hydrothermal-metasomatic transformations of the latter are the products of multi-stage alkali-silicate metasomatism, associated with the second and especially the first phases of intrusion of the massif, as well as with further weathering processes, disintegration of gold-ore metasomatites and formation of an oxidation zone. Based on the petrographic study of the material composition of the rocks of the massif, it is concluded that gold ore occurrences of commercial scale can be associated with such massifs of multi-phase intrusion.
Score Combination for Improved Parallel Corpus Filtering for Low Resource Conditions

This paper presents the description of our submission to WMT20 sentence filtering task. We combine scores from custom LASER built for each source language, a classifier built to distinguish positive and negative pairs and the original scores provided with the task. For the mBART setup, provided by the organizers, our method shows 7% and 5% relative improvement, over the baseline, in sacreBLEU score on the test set for Pashto and Khmer respectively.
DNLP@FinTOC’20: Table of Contents Detection in Financial Documents

Title Detection and Table of Contents Generation are important components in detecting document structure. In particular, these two elements serve to provide the skeleton of the document, providing users with an understanding of organization, as well as the relevance of information, and where to find information within the document. Here, we show that using tesseract with Levenstein distance, a feature set inspired by Alk et al., we were able to correctly classify the title to an F1 measure 0.73 and 0.87, and the table-of-contents to a harmonic mean of 0.36 and 0.39, in English and French respectively. Our methodology works with both PDF and scanned documents, giving it a wide range of applicability within the document engineering and storage domains.
Hybrid Particle Swarm Optimization and Pegasos Algorithm for Spam Email Detection

Email is one of the most popular communication tools for most internet usersnowadays. It has become fast and an effective method to share and exchange information allover the world. Despite the great advantages of emails, its usage is facing problem which is spamemails. Spam emails are the huge presence of bulk and unsolicited emails which are expensive forthe companies, consume a huge amount of mail servers, network bandwidth and waste of time.Isolating and detecting these emails is known as spam detection. Many spam detection methodshave been proposed but there is still need to detect the email spam effectively with high accuracy.In this paper, hybrid particle swarm optimization and Pegasos algorithm, which is called (PSOPegasos) is proposed for spam email detection. Particle swarm optimization is employed as asearch strategy to determine the optimal parameters for Pegasos algorithm in order to achievehigher performance. The proposed algorithm has been applied on spambase dataset downloadedfrom UCI Machine Learning Repository. Experimental results demonstrate that the proposedalgorithm outperforms the performance of all the earlier proposed algorithms, considering theaccuracy, recall, precision and F-measure on the same dataset.
Detecting Tweets Reporting Birth Defect Pregnancy Outcome Using Two-View CNN RNN Based Architecture

This research work addresses a new multi-class classification task (fifth task) provided at the fifth Social Media Mining for Health Applications (SMM4H) workshop. This automatic tweet classification task involves distinguishing three classes of tweets that mention birth defects. We propose a novel two view based CNN-BiGRU based architectures for this task. Experimental evaluation of our proposed architecture over the validation set gives encouraging results as it improves by approximately 7% over our single view model for the fifth task. Code of our proposed framework is made available on Github
An Error-Based Addressing Architecture for Dynamic Model Learning

We present a distributed supervised learning architecture, which can generate trajectory data conditioned by control commands and learned from demonstrations. The architecture consists of an ensemble of neural networks (NNs) which learns the dynamic model and a separate addressing NN that decides from which NN to draw a prediction. We introduce an error-based method for automatic assignment of data subsets to the ensemble NNs for training using the loss profile of the ensemble. Our code is publicly available (Code: https://github.com/NicoBach/distributed-dynamics-model ).
Privacy-Preserving Block-chain Framework Based on Ring Signatures (RSs) and Zero-Knowledge Proofs(ZKPs)

Block-chain emerged as the technology of choice for most applications because of its immutability property. Block-chain's immutability property promotes network resilience against any removal or modification of data stored on the ledger. However, large scale networks such as the Internet of Things (IoT) that may contain millions of nodes can significantly increase Block-chain size and raise serious privacy challenges. Moreover, the General Data Protection Regulations (GDPRs) that were enforced in May 2018 by European countries allows individuals and organizations to take control of their data by establishing stringent rules which includes “the right to be forgotten”. In cases where there are ethical or legal reasons individuals and companies might want their personal data stored to be erased whether such data is stored locally or on the ledger. The philosophy underpinning Block-chains is that forcing nodes to erase data is akin to denying them a role to play in the Block-chain ecosystems as full nodes. In this paper, we attempt to challenge this notion by proposing a model framework for an enhanced privacy that is akin to Block-chain erasure. Our approaches combine ring signatures (that have long been used to generate anonymous signatures) and Zero-Knowledge Proofs (ZKPs) that can help to disguise user's wallet addresses.
On the Importance of Tokenization in Arabic Embedding Models

Arabic, like other highly inflected languages, encodes a large amount of information in its morphology and word structure. In this work, we propose two embedding strategies that modify the tokenization phase of traditional word embedding models (Word2Vec) and contextual word embedding models (BERT) to take into account Arabic’s relatively complex morphology. In Word2Vec, we segment words into subwords during training time and then compose word-level representations from the subwords during test time. We train our embeddings on Arabic Wikipedia and show that they perform better than a Word2Vec model on multiple Arabic natural language processing datasets while being approximately 60% smaller in size. Moreover, we showcase our embeddings’ ability to produce accurate representations of some out-of-vocabulary words that were not encountered before. In BERT, we modify the tokenization layer of Google’s pretrained multilingual BERT model by incorporating information on morphology. By doing so, we achieve state of the art performance on two Arabic NLP datasets without pretraining.
Designing Data Governance for Data Sharing: Lessons from Sidewalk Toronto

This paper considers data governance for data sharing through the lens of the data governance scheme proposed by Sidewalk Labs as part of its Master Innovation Development Plan (MIDP) for a ‘smart city’ development on the waterfront of Toronto, Canada. Recognizing the diverse interests in the data that might be collected in the development, the MIDP called for the creation of an Urban Data Trust (UDT) as a data governance body to address both the collection and the sharing of the novel category of ‘urban data’. This paper uses the example of urban data and the UDT to illustrate some of the challenges that are central to data governance for data sharing and offers a critique that draws upon the idea of governance of a knowledge commons. The paper identifies some of the issues that led to the failure of the UDT, and extracts key lessons.
An Apriori-Based Learning Scheme Towards Intelligent Mining of Association Rules for Geological Big Data

The past decade has witnessed the rapid advancements of geological data analysis techniques, which facilitates the development of modern agricultural systems.However, there remains some technical challenges that should be addressed to fully exploit the potential of those geological big data, while gathering massive amounts of data in this application field.Generally, a good representation of correlation in the geological big data is critical to making full use of multi-source geological data, while discovering the relationship in data and mining mineral prediction information.Then, in this article, a scheme is proposed towards intelligent mining of association rules for geological big data.Firstly, we achieve word embedding via word2vec technique in geological data.Secondly, through the use of self-organizing map (SOM) and K-means algorithm, the word embedding data is clustered to serve the purpose of improving the performance of analysis and mining.On the basis of it, the unsupervised Apriori learning algorithm is developed to analyze and mine these association rules in data.Finally, some experiments are conducted to verify that our scheme can effectively mine the potential relationships and rules in the mineral deposit data.
Evaluating Bias In Dutch Word Embeddings

Recent research in Natural Language Processing has revealed that word embeddings can encode social biases present in the training data which can affect minorities in real world applications. This paper explores the gender bias implicit in Dutch embeddings while investigating whether English language based approaches can also be used in Dutch. We implement the Word Embeddings Association Test (WEAT), Clustering and Sentence Embeddings Association Test (SEAT) methods to quantify the gender bias in Dutch word embeddings, then we proceed to reduce the bias with Hard-Debias and Sent-Debias mitigation methods and finally we evaluate the performance of the debiased embeddings in downstream tasks. The results suggest that, among others, gender bias is present in traditional and contextualized Dutch word embeddings. We highlight how techniques used to measure and reduce bias created for English can be used in Dutch embeddings by adequately translating the data and taking into account the unique characteristics of the language. Furthermore, we analyze the effect of the debiasing techniques on downstream tasks which show a negligible impact on traditional embeddings and a 2% decrease in performance in contextualized embeddings. Finally, we release the translated Dutch datasets to the public along with the traditional embeddings with mitigated bias.
Reducing Locking Issues

Remember that the locking in MySQL and InnoDB is a means to provide concurrent access, and in general the fine-grained locking of InnoDB allows for a highly concurrent workload. Yet, if you have excessive locking, it will cause reduced concurrency and query pileups, and in the worst case, it can cause an application to come to a grinding halt and cause a poor user experience.
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?

We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a large margin. When multimodality is not required, our surprising finding is that we do not need probabilistic posterior predictives: deterministic models may perform optimally but only if they are trained with a probabilistic goal, allowing heteroscedasticity at training time. Our hypothesis is that heteroscedasticity somehow alleviates long-term error accumulation which often hinders the performance of MBRL. At the methodological side, we design metrics and an experimental protocol which can be used to evaluate the various models, predicting their asymptotic performance when using them on the control problem. Using this framework, we improve the state-of-the-art sample complexity of MBRL on Acrobot by two to four folds, using an aggressive training schedule which is outside of the hyperparameter interval usually considered.
Physical and Chemical Characteristics of Oil Palm Plantation Soil: A New Lead in Forensic Investigation

Soil sample is one of the important evidence that can be found in crime scene. Unknown soil sample can be analysed and compared with reference sample in order to determine the origin as its physical and chemical components possess unique characteristics. The purpose of this study is to determine the physical and chemical characteristics of soil from oil palm plantations in Perak, Malaysia to assist forensic investigation. Total of 97 topsoil samples were collected from three different oil palm plantations in Perak. Particle size distribution was obtained using dry sieving technique and colour of soil sample was examined under three conditions that are dry, moist and ashed. Soil pH was measured using pH meter and percentage of composition of soil organic matter (SOM) was determined by weighing the sample before and after ignition. Result showed that the composition of particle size &lt;0.18mm was within the range of 5.57-21.11% whereas for particle size between 0.18mm - 0.6mm was within 31.62 - 52.96% and 25.78-66.86% for particle size &gt;0.6mm. The color mode of soil after oven dried, moistened and ashed was greyish brown (10YR 5/2), very dark greyish brown (10YR 3/2) and light yellowish brown (10YR 6/4) respectively. Soil pH was in the range of 5.79 – 6.70. The percentage of SOM was between 3.29 - 20.48%. The physical and chemical characteristics of soil analysed in this study from three different locations of oil palm plantations varied and it is possible to discriminate these locations based on the analysis highlighted in this study.
Exploration and Practice of Improving Programming Ability for the Undergraduates

Programming ability is one of the most important abilities for the undergraduates majoring in computer science. Taking Yunnan University as an example, the necessity and importance of improving the ability of programming is analyzed in this paper. The exploration and practice of improving students' ability of programming are discussed from four aspects: arrangement and reform of programming curriculums, construction of online programming practice innovation platform, certification of programming ability and organization of programming competitions. These reforms have achieved good results in recent years, which can provide reference for the practical teaching reform of computer specialty in relevant universities.
THE APPROACH TO MEASUREMENT OF REQUIREMENT QUALITY BY APPLICATION OF GENERALIZED PRIORITIZED FUZZY CONSTRAINT SATISFACTION PROBLEM

The requirement quality affects product development at all lifecycle stages, as well as the end product. Poorly defined requirements bring to extended deadlines, increased financial costs, even to project disruption. Current researches related to the good quality of requirements include characteristics of good requirements and the development of new elicitation techniques. Requirement quality evaluation should be tailored both to the professionals and users who defined requirements according to their needs. Therefore, the model is designed for requirement quality measurement based on the characteristics of good requirements by application of the Generalized Prioritized Fuzzy Constraint Satisfaction Problem. The model enables the participation of selected characteristics of good requirements in quality evaluation, according to priorities. The evaluator obtains information if the requirement satisfies the given quality satisfaction threshold based on the degree of fulfillment of selected characteristics of a good requirement. The model is applied to all types of requirements, as well as to the evaluation of requirements at all software development lifecycle stages.
Automatic quantification of lymphocyte vacuolization in peripheral blood smears of patients with Batten's disease (<scp>CLN3</scp> disease)

Abstract Quantifying lymphocyte vacuolization in peripheral blood smears (PBSs) serves as a measure for disease severity in CLN3 disease—a lysosomal storage disorder of childhood‐onset. However, thus far quantification methods are based on labor‐intensive manual assessment of PBSs. As machine learning techniques like convolutional neural networks (CNNs) have been deployed quite successfully in detecting pathological features in PBSs, we explored whether these techniques could be utilized to automate quantification of lymphocyte vacuolization. Here, we present and validate a deep learning pipeline that automates quantification of lymphocyte vacuolization. By using two CNNs in succession, trained for cytoplasm‐segmentation and vacuolization‐detection, respectively, we obtained an excellent correlation with manual quantification of lymphocyte vacuolization ( r = 0.98, n = 40). These results show that CNNs can be utilized to automate the otherwise cumbersome task of manually quantifying lymphocyte vacuolization, thereby aiding prompt clinical decisions in relation to CLN3 disease, and potentially beyond.
Dense Attention-Guided Network for Boundary-Aware Salient Object Detection

Recently, salient object detection methods have achieved significant performance with the development of deep supervised learning. However, most existing methods just simply combine low-level and high-level features, which do not consider that the features of each level should contribute to the features of other levels during learning. To overcome the situation, this paper presents a Dense Attention-guided Network (DANet), which builds dense attention-guided information flows to integrate multi-level features. Specifically, we propose a Residual Attention Module (RAM) to highlight important features and suppress unimportant ones or background noises. In the network, the attention-guided features are transferred to other levels through dense connections, and a Feature Aggregation Module (FAM) is employed to adaptively fuse the multi-level feature maps. For accurate boundary estimation, we further design a novel boundary loss function to preserve the edges of salient regions. The experiments show that the proposed DANet achieves state-of-the-art performance on six widely used salient object detection benchmarks under different evaluation metrics. Besides, Our method runs at a speed of 26 fps on a single GPU and does not need any pre-processing and post-processing.
TEACHER SUPPORT IN COVID-19 PANDEMIC TO DEVELOP BLENDED LEARNING DISRUPTIVE MODELS IN HIGHER EDUCATION

Covid-19 pandemic has created significant changes in higher education institutions.After university lockdown, a transition from face-to-face learning to distance learning was unavoidable and several teachers and students had to approach new technologies.The DELTA (Digital Education for Learning and Teaching Advances) Research Group provided support to six degree programs at the University of Turin: each professor received specific trainings and the group constantly helped and checked the implementation of the online courses.In this paper the support provided during the emergency period has been analysed in order to evaluate the improvements in teaching methodologies, and to assess professors' transition to future blended learning disruptive models.The results show an important change in methodology for some courses, aiming at improving the online learning processes.The research data analysis and qualitative study about the usage of the Digital Learning Environment describe the courses' disruptive models.They are useful to understand which elements of the emergency response turned out to be positive and which ones to be unfavourable, in order to be able to redesign post-Covid higher education.
Enhancing the Retailer Gift Card via Blockchain

Though the retailer gift card has been an ultra-practical marketing tactic to attract customers to spend more, it, on the contrary, also places a great number of customers in troublesome situations due to its current limitations. First, dealing with unwanted gift cards is often time-consuming, costly, or even risky due to the frequent occurrences of gift card resale frauds. Worse still, the issuance and redemption of gift cards happen inside the retailer as in a “black-box,” indicating that a compromised retailer can cheat customers (or even third-party auditors) to deny the issuances of some unredeemed gift cards. This paper proposes a practical middle-layer solution based on blockchain to address the fundamental issues of the existing gift card system, with incurring minimal changes to the current infrastructure.