Reviewerly/exHarmony huggingface dataset:
CASE 2021 Task 2 Socio-political Fine-grained Event Classification using Fine-tuned RoBERTa Document Embeddings

We present our submission to Task 2 of the Socio-political and Crisis Events Detection Shared Task at the CASE @ ACL-IJCNLP 2021 workshop.The task at hand aims at the fine-grained classification of socio-political events.Our best model was a fine-tuned RoBERTa transformer model using document embeddings.The corpus consisted of a balanced selection of sub-events extracted from the ACLED event dataset.We achieved a macro F-score of 0.923 and a micro F-score of 0.932 during our preliminary experiments on a held-out test set.The same model also performed best on the shared task test data (weighted F-score = 0.83).To analyze the results we calculated the topic compactness of the commonly misclassified events and conducted an error analysis.
Meta Learning and Its Applications to Natural Language Processing

Hung-yi Lee, Ngoc Thang Vu, Shang-Wen Li. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts. 2021.
DPF-ECC: A Framework for Efficient ECC With Double Precision Floating-Point Computing Power

Used ubiquitously in a huge amount of security protocols or applications, elliptic curve cryptography (ECC) is one of the most important cryptographic primitives, featuring efficiency and short key size compared with other public-key cryptosystems such as DSA and RSA. However, as a computation-intensive public-key cryptographic primitive, ECC arithmetic is still the bottleneck that restrains the overall performance of the end applications. In this paper, instead of the conventional and straightforward integer-based methods, we present a general framework to accelerate ECC schemes over prime field, called DPF-ECC, that deeply exploits double precision floating-point (DPF) computing power. The DPF-ECC framework finely manages each bit of the DPF numbers and minimizes the overhead brought by additional data format conversion, by making use of the DPF representation, the rounding operations, and fused multiply-add instruction supported by the IEEE 754 floating point standard. We also conduct two comprehensive case studies on Crandall primes and Solinas primes to demonstrate how the DPF-ECC framework is applied to the prevailing ECC schemes. To evaluate the proposed DPF-ECC framework in the real world, leveraging the floating-point computing power of GPUs, we implement Curve25519/448 and Edwards25519/448, the popular ECC schemes widely used in TLS 1.3, SSH, etc. The experimental result in Tesla P100 achieves a record-setting performance that outperforms the existing fastest integer work with 2x to 3x throughput. With dependency only on the very commonly supported IEEE 754 floating point standard, DPF-ECC framework can be a very competent and promising candidate for ECC implementation in most of general-purpose platforms.
DeepBlueAI at SemEval-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble Approach

Lexical complexity plays an important role in reading comprehension.lexical complexity prediction (LCP) can not only be used as a part of Lexical Simplification systems, but also as a stand-alone application to help people better reading.This paper presents the winning system we submitted to the LCP Shared Task of SemEval 2021 that capable of dealing with both two subtasks.We first perform fine-tuning on numbers of pre-trained language models (PLMs) with various hyperparameters and different training strategies such as pseudo-labelling and data augmentation.Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction.Experimental results on the Complex dataset show the validity of our method and we rank first and second for subtask 2 and 1.
Resilience of Islamic cryptocurrency markets to Covid-19 shocks and the Federal Reserve policy

Purpose After the COVID-19 outbreak, the Federal Reserve has undertaken several monetary policies to alleviate the pandemic consequences on the stock markets leading to a misunderstanding on the cryptocurrency market response. This paper aims to evaluate the effects of the Federal Reserve monetary policy on the Islamic and conventional cryptocurrency dynamics during the COVID-19 pandemic. We, specifically, examine the associate bubbles and feedbacks effects. Design/methodology/approach This paper developed a novel methodology that detects market bubbles using the statistical indicators defined by Psychological (PSY) tests. It also investigated the effect of the Federal Open Market Committee (FOMC) announcements on conventional and Islamic cryptocurrencies compatible with Islamic laws “Shari’ah” by using the event-driven regression. Findings The empirical results show that the FOMC announcements have a positive significant effect after one day of the event and a negative effect before two days of the announcement on the conventional cryptocurrency markets. However, the reaction of Islamic cryptocurrencies to these events is not significant except for Hello Gold after one day of the announcement. Besides, the Hello Gold and X8X cryptocurrencies present no bubbles during this period. However, Bitcoin and Ethereum markets have short-lived bubbles. Research limitations/implications The main contribution of this study is the investigation of the response and vulnerability to pandemic shocks of a new category of cryptocurrencies backed by tangible assets. This work has practical implications as it provides new insights into trading opportunities and market reactions. Originality/value To our knowledge, this work is the first study that compares the response of Islamic and conventional cryptocurrency markets to FOMC announcements during the COVID-19 pandemic and examines the presence of bubbles in these markets. Besides, the originality of this work is derived from the novelty of the data employed and the method used (PSY tests) in this study.
NPVec1: Word Embeddings for Nepali - Construction and Evaluation

Word Embedding maps words to vectors of real numbers.It is derived from a large corpus and is known to capture semantic knowledge from the corpus.Word Embedding is a critical component of many state-of-the-art Deep Learning techniques.However, generating good Word Embeddings is a special challenge for low-resource languages such as Nepali due to the unavailability of large text corpus.In this paper, we present NPVec1 which consists of 25 state-of-art Word Embeddings for Nepali that we have derived from a large corpus using GloVe, Word2Vec, fastText, and BERT.We further provide intrinsic and extrinsic evaluations of these Embeddings using well established metrics and methods.These models are trained using 279 million word tokens and are the largest Embeddings ever trained for Nepali language.Furthermore, we have made these Embeddings publicly available to accelerate the development of Natural Language Processing (NLP) applications in Nepali. Relatedness SetSentiment Set
The Impact of Blockchain Technology in Higher Education Quality Improvement

The times have made technology increasingly developed that there are innovations as new media to make it easier for humans in everyday life. Blockchain technology is an innovation that has been applied in various fields such as education, health, economy, and other areas. In this study, researchers want to see the impact that blockchain technology has had on universities in technology and information to improve universities' quality. The methodology used is by reviewing previous research papers related to the research. So that this research is expected to contribute, which can overcome the problems being faced, such as the application of blockchain in universities, namely how to use the blockchain system, what blockchain is, and how to change existing technology into disruptive technology. The experimental results state that by using data from the previous paper, the aim is to reveal the impact that blockchain technology has had on technology and information that is useful for improving the quality of universities in encouraging human potential and improving quality so that they can compete both domestically and abroad.
Estimation of sex through morphometric landmark indices in facial images with strength of evidence in logistic regression analysis

We report an investigation into the application of the logistic regression classifier for estimation of sex in facial images. We used 2000 images, 1000 each of both sexes from a publicly available database and automatically detected facial landmarks and derived some morphometric facial indices. These indices were used as predictors for the classification. As the traditional manual extraction of facial landmarks is time consuming, automatic detection of the landmarks improves the efficiency. The logistic regression classification is also compared with two other classification methods, the likelihood-ratio (LR) based method where the features of a face are evaluated in terms of the probability distribution of these features in both the sexes, and the Convolutional Neural Networks (CNN) methods. While is former is desirable from the viewpoint of interpretability and to assess the strength of evidence, the latter is sophisticated. We report an AUC of 0.94 with true positive (TP) rate of 88.4% for males and 87.9% for females for logistic regression-based classification. This performance is better than the likelihood ratio classifier with TP rate of 79.6% for males and 82.2% for females. The overall performance of logistics regression is slightly less than the CNN classifier that has 89.3% TP rate for males and 92.6% for females. We have extended these models to a CCTV image database, more representative of the forensic scenario and found the logistic regression performing better than the CNN method on an average for 8 different types of cameras. We conclude that as a trade-off between simplicity and sophistication, the logistic regression classifier can be used for a two-class problem like classification of sex from facial morphometric indices, and that the likelihood ratio approach can assess the strength of the classification, in conformance with the requirements of evidence interpretation.
An Iterative Scheme of Safe Reinforcement Learning for Nonlinear Systems via Barrier Certificate Generation

Abstract In this paper, we propose a safe reinforcement learning approach to synthesize deep neural network (DNN) controllers for nonlinear systems subject to safety constraints. The proposed approach employs an iterative scheme where a learner and a verifier interact to synthesize safe DNN controllers. The learner trains a DNN controller via deep reinforcement learning, and the verifier certifies the learned controller through computing a maximal safe initial region and its corresponding barrier certificate, based on polynomial abstraction and bilinear matrix inequalities solving. Compared with the existing verification-in-the-loop synthesis methods, our iterative framework is a sequential synthesis scheme of controllers and barrier certificates, which can learn safe controllers with adaptive barrier certificates rather than user-defined ones. We implement the tool SRLBC and evaluate its performance over a set of benchmark examples. The experimental results demonstrate that our approach efficiently synthesizes safe DNN controllers even for a nonlinear system with dimension up to 12.
Identity Management with Hybrid Blockchain Approach: A Deliberate Extension with Federated-Inverse-Reinforcement Learning

The widespread decentralized applications and Blockchain components significantly boost the security frameworks in many vertical applications and use-cases including different secured payment methods and smart contracts. The integral part of any smart contract is the validation of the stake-holder identity, in general, while ideally being achieved without the third-party involvement. Recent industrial research works introduce the sovereign-identity system, where Blockchain becomes a decentralized component to establish a self-certified identity and to avoid a centralized trust third party. Hence, the classification of distributed transactions with respect to identity validation across several users becomes more challenging, especially because of the massive and sensitive identities that are issued through many users and IoT devices and that are used to validate transactions. In this context, it is important to identify and classify the malicious and non-malicious types of transactions. Our proposed method achieves the target of identity classifications from variety of transaction data. Since different users may have different device usage patterns, the data samples and labels located on any individual device may follow a different distribution, which cannot represent the global data distribution. Therefore, the solution could be bi-focal to compensate the gap. This paper coins the approach of hybridizing the consensus where as to initiate a machine learning mechanism to collect the local data globally through a permission driven and a federated approach. We introduce here a Federated Reinforcement learning to be improvised for distributed independent data as a policy of consortium while binding the proof of consensus more centrally authenticated.
Predictability as a quantum resource

Just recently, complementarity relations (CRs) have been derived from the basic rules of Quantum Mechanics. The complete CRs are equalities involving quantum coherence, C, quantum entanglement, and predictability, P. While the first two are already quantified in the resource theory framework, such a characterization lacks for the last. In this article, we start showing that, for a system prepared in a state $$\rho $$ , P of $$\rho $$ , with reference to an observable X, is equal to C, with reference to observables mutually unbiased (MU) to X, of the state $$\Phi _{X}(\rho )$$ , which is obtained from a non-revealing von Neumann measurement (NRvNM) of X. We also show that $$P^X(\rho )>C^{Y}(\Phi _{X}(\rho ))$$ for observables X, Y not MU. Afterwards, we provide quantum circuits for implementing NRvNMs and use these circuits to experimentally test these (in)equalities using the IBM’s quantum computers. Furthermore, we give a resource theory for predictability, identifying its free quantum states and free quantum operations and discussing some predictability monotones. Besides, after applying one of these predictability monotones to study bipartite systems, we discuss the relation among the resource theories of quantum coherence, predictability, and purity.
Weighted Intersection over Union (wIoU): A New Evaluation Metric for Image Segmentation

In recent years, many semantic segmentation methods have been proposed to predict label of pixels in the scene. In general, we measure area prediction errors or boundary prediction errors for comparing methods. However, there is no intuitive evaluation metric that evaluates both aspects. In this work, we propose a new evaluation measure called weighted Intersection over Union (wIoU) for semantic segmentation. First, it build a weight map generated from a boundary distance map, allowing weighted evaluation for each pixel based on a boundary importance factor. The proposed wIoU can evaluate both contour and region by setting a boundary importance factor. We validated the effectiveness of wIoU on a dataset of 33 scenes and demonstrated its flexibility. Using the proposed metric, we expect more flexible and intuitive evaluation in semantic segmentation filed are possible.
Geometric Deep Learning for Multi-Object Tracking: A Brief Review

Graphs frequently appear as a type of data structure that efficiently models a set of interrelated objects as nodes and their relations as edges between them. Geometric deep learning emerged as a promising field for modeling non-Euclidean geometric data as graphs, Riemannian manifolds and meshes. In this paper, we review geometric deep learning based state-of-the-art approaches for multi-object tracking. We illustrate the main concept, the model architecture, loss functions, and optimization strategies of the main algorithms. Moreover, algorithms are quantitatively evaluated on standard performance metrics using the standard dataset. The potential of geometric deep learning in tracking is discussed and future directions are proposed.
The affiliative use of emoji and hashtags in the Black Lives Matter movement: A Twitter case study

Abstract Protests and counter-protests seek to draw and direct attention and concern with confronting images and slogans. In recent years, as protests and counter-protests have partially migrated to the digital space, such images and slogans have also gone online. Two main ways in which these images and slogans are translated to the online space is through the use of emoji and hashtags. Despite sustained academic interest in online protests, hashtag activism and the use of emoji across social media platforms, little is known about the specific functional role that emoji and hashtags play in online social movements. In an effort to fill this gap, the current paper studies both hashtags and emoji in the context of the Twitter discourse around the Black Lives Matter movement.
Facial Expression Recognition using Residual Convnet with Image Augmentations

During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings to prevent the spread of the COVID 19 virus. In the online video meeting, some micro-interactions are missing when compared to direct social interactions. The use of machines to assist facial expression recognition in online video meetings is expected to increase understanding of the interactions among users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image classification. In this study, some open facial expression datasets were used to train CNN-based neural networks with a total number of training data of 342,497 images. This study gets the best results using ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972, FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning, and there is a potential for better performance with the pre-training model. The code is available at https://github.com/yusufrahadika-facial-expressions-essay.
Academic expertise for the structure and content of professional competences for future preschool teachers

The Federal State Educational Standard for a new generation (FSES of HE 3++) sets out universal and general professional competences for university graduates. Educational structures can define professional competences independently and describe them as professional standards. A means of solving this challenge is to use a group of assessment experts – one of the core academic qualitative methods. This collective expert opinion helps to develop an algorithm for pedagogical competence expertise and for generalizing their quantitative representation and content. The article presents methods for the structure and content of professional competences for undergraduate students majoring in preschool education. These include the group expert assessment method and the procedure for reconciling the determined competences with current legal documents for preschool education. The research focused on competence-based and qualimetric approaches aimed at modeling the outcomes of education. The “framework” sets out the structure of competences that act as the professional quality standards for higher education. The group expert assessment made it possible to identify groups of professional competences and elaborate their content. The authors have determined the academic expertise for the structure and content of professional competences for students who are the future preschool teachers. This involved the examination and harmonization of the requirements of the Professional Standards for “Teachers (teaching practice in the field of preschool, primary, lower secondary, upper secondary education) (a pre-school teacher, a teacher),” and the Federal State Educational Standard for preschool education. These stages can be implemented in developing the structure and content of professional competencies for students majoring in other areas and profiles of training, which adds to their practical value and usefulness. The findings can be used by university instructors to identify and develop professional competences for undergraduate students following their future job functions and actions as outlined by professional standards.
Structure-aware Sentence Encoder in Bert-Based Siamese Network

Recently, impressive performance on various natural language understanding tasks has been achieved by explicitly incorporating syntax and semantic information into pre-trained models, such as BERT and RoBERTa.However, this approach depends on problemspecific fine-tuning, and as widely noted, BERT-like models exhibit weak performance, and are inefficient, when applied to unsupervised similarity comparison tasks.Sentence-BERT (SBERT) has been proposed as a general-purpose sentence embedding method, suited to both similarity comparison and downstream tasks.In this work, we show that by incorporating structural information into SBERT, the resulting model outperforms SBERT and previous general sentence encoders on unsupervised semantic textual similarity (STS) datasets and transfer classification tasks.
Design and implementation of Threefish cipher algorithm in PNG file

This paper is presenting design and implementation of Threefish block cipher on grayscale images. Despite the fact that Threefish block cipher is one of the best secure algorithms, most studies concerning Threefish have focused on hardware implementation and have not commonly been applied on image encryption due to huge amount of data. The main contribution here was to reduce the time and the amount of data to be encrypted while maintaining encryption performance. This objective was achieved by encrypting just the most significant bits of image pixels. A 256-bit plain text blocks of the Threefish was constructed from 2n most significant bits of the pixels, where 0&lt;n&lt;3. Furthermore, Threefish block cipher was applied when n=3 to analyze the impact of uninvolving some bits in encryption process on the encryption performance. The results indicated that the encryption achieved good encryption quality when n=1, but it might cause some loss in decryption. In contrast, the encryption achieved high encryption quality when n=2, almost as good as the encryption of the whole pixel bits. Furthermore, the encryption time and the amount of data to be encrypted were decreased 50% as n decreased by 1. It was concluded that encrypting half of the pixel bits reduces both time and data, as well as significantly preserves the encryption quality. Finally, although the proposed method passed the statistical analysis, further work is needed to find a method resistant to the differential analysis.