Reviewerly/exHarmony huggingface dataset:
Penerapan Extreme Programming dalam Pengembangan Sistem Informasi Manajemen Pelayanan Publik

Sukarame District serves public administration activities such as information on population data and mail administration services. The conventional process currently running in Sukarame District only uses Microsoft Office for data storage so that it cannot be accessed by people in real-time. As a solution to the problem, we need a system that can help public service management to manage population data, public complaints, and service letters such as environmental permits, electricity subsidy permits, and business permits. This system was developed with the extreme programming development method with system design using a unified modeling language, namely use case diagram, class diagram, and activity diagram. To assess the feasibility of the system, testing was carried out using the black-box method which resulted in an accuracy of 100%, so that this system was fit for use functionally.
Arabic Documents Information Retrieval for Printed, Handwritten, and Calligraphy Image

This paper presents a new computational backend model that supports Arabic document information retrieval (ADIR) as a dataset and OCR services. Therefore, different services that support document analysis, retrieving, processing including dataset preparation, and recognition will be discussed. Consequently, ADIR services provide general functions of the Arabic OCR to compose many other services in the OCR domain. Furthermore, the proposed work can provide accessing different methods of document layout analysis with a platform where they can share and handle such methods (services) without any setup requirements. One of the used datasets composed from 16,800 Arabic letters written by 60 writers. Each writer wrote each letter from Alif to Ya 10 times in two forms. The forms were scanned at 300 DPI resolution and are segmented in two sets: training set with 13,440 letters for 48 images per class label, and testing set with 3,360 letters to 120 images per class label Convolutional neural network (CNN) is used and adapted for Arabic handwritten letters classification. In an experimental test, we showed that our results outperform 100% classification accuracy rate on testing images. Therefore, the ADIR services provide a “service description”, which includes an interface and a server's URL. The interface allows communication process between clients and services. Although, in this article we evaluate IR results and compared them with respect to corrected equivalent.
Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks

Neural networks traditionally produce a scalar value for an activated neuron. Capsules, on the other hand, produce a vector of values, which has been shown to correspond to a single, composite feature wherein the values of the components of the vectors indicate properties of the feature such as transformation or contrast. We present a new way of parameterizing and training capsules that we refer to as homogeneous vector capsules (HVCs). We demonstrate, experimentally, that altering a convolutional neural network (CNN) to use HVCs can achieve superior classification accuracy without increasing the number of parameters or operations in its architecture as compared to a CNN using a single final fully connected layer. Additionally, the introduction of HVCs enables the use of adaptive gradient descent, reducing the dependence a model's achievable accuracy has on the finely tuned hyperparameters of a non-adaptive optimizer. We demonstrate our method and results using two neural network architectures. For the CNN architecture referred to as Inception v3, replacing the fully connected layers with HVCs increased the test accuracy by an average of 1.32% across all experiments conducted. For a simple monolithic CNN, we show HVCs improve test accuracy by an average of 19.16%.
Exploring Transitivity in Neural NLI Models through Veridicality

Despite the recent success of deep neural networks in natural language processing, the extent to which they can demonstrate human-like generalization capacities for natural language understanding remains unclear.We explore this issue in the domain of natural language inference (NLI), focusing on the transitivity of inference relations, a fundamental property for systematically drawing inferences.A model capturing transitivity can compose basic inference patterns and draw new inferences.We introduce an analysis method using synthetic and naturalistic NLI datasets involving clauseembedding verbs to evaluate whether models can perform transitivity inferences composed of veridical inferences and arbitrary inference types.We find that current NLI models do not perform consistently well on transitivity inference tasks, suggesting that they lack the generalization capacity for drawing composite inferences from provided training examples.The data and code for our analysis are publicly available at https://github.com/verypluming/transitivity.
Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic

Sarcasm detection is one of the top challenging tasks in text classification, particularly for informal Arabic with high syntactic and semantic ambiguity. We propose two systems that harness knowledge from multiple tasks to improve the performance of the classifier. This paper presents the systems used in our participation to the two sub-tasks of the Sixth Arabic Natural Language Processing Workshop (WANLP); Sarcasm Detection and Sentiment Analysis. Our methodology is driven by the hypothesis that tweets with negative sentiment and tweets with sarcasm content are more likely to have offensive content, thus, fine-tuning the classification model using large corpus of offensive language, supports the learning process of the model to effectively detect sentiment and sarcasm contents. Results demonstrate the effectiveness of our approach for sarcasm detection task over sentiment analysis task.
Improving Ki67 assessment concordance by the use of an artificial intelligence‐empowered microscope: a multi‐institutional ring study

The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article was to report a solution utilising an artificial intelligence (AI)-empowered microscope to improve Ki67 scoring concordance.We developed an AI-empowered microscope in which the conventional microscope was equipped with AI algorithms, and AI results were provided to pathologists in real time through augmented reality. We recruited 30 pathologists with various experience levels from five institutes to assess the Ki67 labelling index on 100 Ki67-stained slides from invasive breast cancer patients. In the first round, pathologists conducted visual assessment on a conventional microscope; in the second round, they were assisted with reference cards; and in the third round, they were assisted with an AI-empowered microscope. Experienced pathologists had better reproducibility and accuracy [intraclass correlation coefficient (ICC) = 0.864, mean error = 8.25%] than inexperienced pathologists (ICC = 0.807, mean error = 11.0%) in visual assessment. Moreover, with reference cards, inexperienced pathologists (ICC = 0.836, mean error = 10.7%) and experienced pathologists (ICC = 0.875, mean error = 7.56%) improved their reproducibility and accuracy. Finally, both experienced pathologists (ICC = 0.937, mean error = 4.36%) and inexperienced pathologists (ICC = 0.923, mean error = 4.71%) improved the reproducibility and accuracy significantly with the AI-empowered microscope.The AI-empowered microscope allows seamless integration of the AI solution into the clinical workflow, and helps pathologists to obtain higher consistency and accuracy for Ki67 assessment.
Blockchain Solutions for Agency Problems in Corporate Governance

As a foundational technology, blockchain technology creates the infrastructure for decentralized networked governance that, over time, creates the environment which enables the removal of internal and external monitoring mechanisms previously necessitated by agency problems in corporate governance. Blockchain technology facilitates a substantial increase in efficiency in the agency relationship and lowers agency costs in orders of magnitude.
SarcasmDet at Sarcasm Detection Task 2021 in Arabic using AraBERT Pretrained Model

This paper presents one of the top five winning solutions for the Shared Task on Sarcasm and Sentiment Detection in Arabic (Subtask-1 Sarcasm Detection). The goal of the task is to identify whether a tweet is sarcastic or not. Our solution has been developed using ensemble technique with AraBERT pre-trained model. We describe the architecture of the submitted solution in the shared task. We also provide the experiments and the hyperparameter tuning that lead to this result. Besides, we discuss and analyze the results by comparing all the models that we trained or tested to achieve a better score in a table design. Our model is ranked fifth out of 27 teams with an F1 score of 0.5985. It is worth mentioning that our model achieved the highest accuracy score of 0.7830
A Controlled Vocabulary and Metadata Schema for Materials Science Data Discovery

The International Materials Resource Registries (IMRR) working group of the Research Data Alliance (RDA) was created to spur initial development of a federated registry system to allow for easier discovery and access to materials data. As part of this effort, a controlled vocabulary and metadata schema were developed with contributions from members of the working group and other experts. Here we describe the process, the resulting vocabulary and XML schema, and lessons learned in the development and use of the schema.
A Blockchain-Based Editorial Management System

Research publications are reaching a stunning growth rate. Therefore, new challenges regarding managing the peer-review activities are presented, such as data security, privacy, integrity, fragmentation, and isolation. Further, because of the emergence of predatory journals and research fraud, there is a need to assess the quality of the peer-review process. This research proposes a fully functional blockchain-based editorial management system, namely, TimedChain, for managing the peer-review process from submission to publication. TimedChain provides secure, interoperable, transparent, and efficient access to manuscripts by publishers, authors, readers, and other third parties. Time-based smart contracts and advanced encryption techniques are employed for governing transactions, controlling access, and providing further security. An incentive mechanism that evaluates publishers’ value respecting their efforts at managing and maintaining research data and creating new blocks is introduced. Extensive experiments were conducted for performance evaluation. Results demonstrate the efficiency of the proposed system in governing a large set of data at low latency.
Explainable Machine Learning for Default Privacy Setting Prediction

When requesting a web-based service, users often fail in setting the website's privacy settings according to their self privacy preferences.Being overwhelmed by the choice of preferences, a lack of knowledge of related technologies or unawareness of the own privacy preferences are just some reasons why users tend to struggle.To address all these problems, privacy setting prediction tools are particularly well-suited.Such tools aim to lower the burden to set privacy preferences according to owners' privacy preferences.To be in line with the increased demand for explainability and interpretability by regulatory obligations -such as the General Data Protection Regulation (GDPR) in Europe -in this paper an explainable model for default privacy setting prediction is introduced.Compared to the previous work we present an improved feature selection, increased interpretability of each step in model design and enhanced evaluation metrics to better identify weaknesses in the model's design before it goes into production.As a result, we aim to provide an explainable and transparent tool for default privacy setting prediction which users easily understand and are therefore more likely to use.
The Coronavirus Corpus

Abstract This paper discusses the creation and use of the Coronavirus Corpus, which is currently (March 2021) 900 million words in size, and which will probably be about one billion words in size by May–June 2021. The Coronavirus Corpus is a subset of the NOW Corpus (News on the Web), which is currently about 12.1 billion words in size and which grows by about two billion words each year. These two corpora are updated every night, with about 6–10 million words for NOW and 2–3 million words for the Coronavirus Corpus. The Coronavirus Corpus allows users to see the frequency of words and phrases over time (even by individual day), and users can find all words that are more frequent in one time period than another. Users can also see the collocates for words and phrases, and compare the collocates to see what is being said about particular topics over time.
Two-Level Attention-based Fusion Learning for RGB-D Face Recognition

With recent advances in RGB-D sensing technologies as well as improvements in machine learning and fusion techniques, RGB-D facial recognition has become an active area of research. A novel attention aware method is proposed to fuse two image modalities, RGB and depth, for enhanced RGB-D facial recognition. The proposed method first extracts features from both modalities using a convolutional feature extractor. These features are then fused using a two layer attention mechanism. The first layer focuses on the fused feature maps generated by the feature extractor, exploiting the relationship between feature maps using LSTM recurrent learning. The second layer focuses on the spatial features of those maps using convolution. The training database is preprocessed and augmented through a set of geometric transformations, and the learning process is further aided using transfer learning from a pure 2D RGB image training process. Comparative evaluations demonstrate that the proposed method outperforms other state-of-the-art approaches, including both traditional and deep neural network-based methods, on the challenging CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism is also compared with other attention mechanisms, demonstrating more accurate results.
A comparative study on the effects of the COVID-19 pandemic on three different national university learning ecosystems as bases to derive a Model for the Attitude to get Engaged in Technological Innovation (MAETI)

This paper reports the outcomes of a unique investigation coordinated by ASLERD that had the aim of comparing the effects of the COVID-19 pandemic on three learning ecosystems with different cultural backgrounds and settings: Iraq, Italy and Mexico. Using the same questionnaire translated from Italian to English, Arabic and Spanish, the study has investigated these ecosystems through the lens of University teachers. Despite cultural and infrastructural differences that unavoidably influenced the outcomes, we have detected common effects in the three samples, such as an increase of the working load and a better organization of the time at individual level. Network analysis methods also allowed us to identify some relationships between variables common to the three contexts. In particular, some variables clustered in all samples: a cluster related to the setting (infrastructure and technologies, competences and readiness to respond), one related to the characteristics of the didactic activities, and one related to expectations for the future about use of and involvement in on-line learning. In addition, the reproducibility of classroom dynamics seems to have high betweenness centrality in all three networks. In one case, that of Iraq, we have been also able to include in the causal networks the factors considered by the TAM (Technology Acceptance Model), but the networks appear not to be substantially influenced by the inclusion of TAM variables. On the basis of these results, we propose a Model for the Attitude to get Engaged in Technological Innovation (MAETI), to be used as possible reference framework for future investigation on the effects induced by technologies on use intentions.
Assessment of the Amaruq gold deposit signature in glacial sediments using multivariate geochemical data analysis and indicator minerals

The varying nature and thickness of glacial sediments affect the characteristics of mineralized dispersal trains in the Amaruq gold deposit area in Nunavut, northern Canada. To help evaluate the deposit signature, glacial sediments were collected from frost boils along transects parallel to the main ice-flow direction. A complementary detailed geochemical drift exploration dataset and a new Quaternary map were also used to define and interpret the geometry of the mineralized debris dispersal trains. Principal Component Analysis identifies a gold mineralization signature associating As, Ag, Au, Cu, Pb, Sb, W, S, Fe, and Zn and its mafic-BIF (Banded Iron Formation) host rock. This signature, similar to other lode gold geochemical signatures, follows a general NNW dispersal trend, in agreement with the predominant regional and local ice-flow indicators. In a zone characterized by streamlined landforms composed of thick distally -derived till, the dispersal train shows a higher distance for the first indicator to appear at surface down-ice of the known mineralization (~1 km lag) when compared to other mineralized dispersal trains (~300 m lag). Results from this area also show a lower intensity of deposit signature compared to mineralized dispersal trains located in thin locally-derived till characterized by small transverse moraine ridges. Elevated counts of scheelite and pristine-shaped gold grains are found down-ice of mineralization and their distribution is in general agreement with the dispersal train patterns defined by the till-matrix geochemistry. Our results strengthen the use of PCA and indicator minerals combined with an understanding of ice flow dynamics to map deposit signatures at surface and efficiently guide mineral exploration during glacial sediment surveys at local scales.
Quantum entanglement and statistics of photons on a beam splitter in the form of coupled waveguides

Abstract It is well known that a beam splitter (BS) can be used as a source of photon quantum entanglement. This is due to the fact that the statistics of photons changes at the output ports of the BS. Usually, quantum entanglement and photon statistics take into account the constancy of the reflection coefficient R or the transmission coefficient T of the BS, where $$R + T = 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>R</mml:mi> <mml:mo>+</mml:mo> <mml:mi>T</mml:mi> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> . It has recently been shown that if BS is used in the form of coupled waveguides, the coefficients R and T will depend on the photon frequencies. In this paper, it is shown that the quantum entanglement and statistics of photons at the output ports of a BS can change significantly if a BS is used in the form of coupled waveguides, where the coefficients R and T are frequency-dependent.
Self-Supervised Domain Adaptation with Consistency Training

We consider the problem of unsupervised domain adaptation for image classification. To learn target-domain-aware features from the unlabeled data, we create a self-supervised pretext task by augmenting the unlabeled data with a certain type of transformation (specifically, image rotation) and ask the learner to predict the properties of the transformation. However, the obtained feature representation may contain a large amount of irrelevant information with respect to the main task. To provide further guidance, we force the feature representation of the augmented data to be consistent with that of the original data. Intuitively, the consistency introduces additional constraints to representation learning, therefore, the learned representation is more likely to focus on the right information about the main task. Our experimental results validate the proposed method and demonstrate state-of-the-art performance on classical domain adaptation benchmarks. Code is available at https://github.com/Jiaolong/ss-da-consistency.
Chatting with AI Bot: Vocabulary Learning Assistant for Saudi EFL Learners

In the AI field of language learning, chatterbots are an interesting area for language learning and practice. This research investigates Arabic EFL vocabulary learning using an interactive storytelling chatterbot. A chatterbot was created and equipped with four vocabulary tools: a dictionary, images, an L1 translation tool, and a concordancer. The target words were enhanced by these tools to provide the learners with interactive comprehensible input. This project seeks to identify which tools are mostly used when EFL learners are practicing English with a chatterbot. It also seeks to determine which tool could help most in vocabulary learning as well as retention. The results of the study indicate that the dictionary is the most favoured and effective tool for vocabulary learning. For retention, the findings uncover that L1 translation is slightly (but insignificantly) higher than the dictionary.&#x0D; &amp;nbsp;
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation

Neural machine translation (NMT) has recently gained widespread attention because of its high translation accuracy.However, it shows poor performance in the translation of long sentences, which is a major issue in low-resource languages.It is assumed that this issue is caused by insufficient number of long sentences in the training data.Therefore, this study proposes a simple data augmentation method to handle long sentences.In this method, we use only the given parallel corpora as the training data and generate long sentences by concatenating two sentences.Based on the experimental results, we confirm improvements in long sentence translation by the proposed data augmentation method, despite its simplicity.Moreover, the translation quality is further improved by the proposed method, when combined with backtranslation.
Reinforcement Learning with Prototypical Representations

Learning effective representations in image-based environments is crucial for sample efficient Reinforcement Learning (RL). Unfortunately, in RL, representation learning is confounded with the exploratory experience of the agent -- learning a useful representation requires diverse data, while effective exploration is only possible with coherent representations. Furthermore, we would like to learn representations that not only generalize across tasks but also accelerate downstream exploration for efficient task-specific training. To address these challenges we propose Proto-RL, a self-supervised framework that ties representation learning with exploration through prototypical representations. These prototypes simultaneously serve as a summarization of the exploratory experience of an agent as well as a basis for representing observations. We pre-train these task-agnostic representations and prototypes on environments without downstream task information. This enables state-of-the-art downstream policy learning on a set of difficult continuous control tasks.