Reviewerly/exHarmony huggingface dataset:
Effective Few-Shot Classification with Transfer Learning

Few-shot learning addresses the the problem of learning based on a small amount of training data. Although more well-studied in the domain of computer vision, recent work has adapted the Amazon Review Sentiment Classification (ARSC) text dataset for use in the few-shot setting. In this work, we use the ARSC dataset to study a simple application of transfer learning approaches to few-shot classification. We train a single binary classifier to learn all few-shot classes jointly by prefixing class identifiers to the input text. Given the text and class, the model then makes a binary prediction for that text/class pair. Our results show that this simple approach can outperform most published results on this dataset. Surprisingly, we also show that including domain information as part of the task definition only leads to a modest improvement in model accuracy, and zero-shot classification, without further fine-tuning on few-shot domains, performs equivalently to few-shot classification. These results suggest that the classes in the ARSC few-shot task, which are defined by the intersection of domain and rating, are actually very similar to each other, and that a more suitable dataset is needed for the study of few-shot text classification.
BRUMS at SemEval-2020 Task 12: Transformer Based Multilingual Offensive Language Identification in Social Media

In this paper, we describe the team BRUMS entry to OffensEval 2: Multilingual Offensive Language Identification in Social Media in SemEval-2020.The OffensEval organizers provided participants with annotated datasets containing posts from social media in Arabic, Danish, English, Greek and Turkish.We present a multilingual deep learning model to identify offensive language in social media.Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility between languages. IntroductionSocial media has become a normal medium of communication for people these days as it provides the convenience of sending messages fast from a variety of devices.Unfortunately, social networks also provide the means for distributing abusive and aggressive content.Given the amount of information generated every day on social media, it is not possible for humans to identify and remove such messages manually, instead it is necessary to employ automatic methods.As offensive language becomes pervasive in social media, scholars and companies have been working on developing systems capable of identifying offensive posts, which can be set aside for human moderation or permanently deleted (Risch and Krestel, 2018).Along with these studies, a few shared tasks have been organized on detecting offense in social media, such as HatEval (Basile et al., 2019), HASOC (Mandl et al., 2019), TRAC (Kumar et al., 2018) and OffensEval (Zampieri et al., 2019b) co-located with SemEval 2019.In semeval 2020, OffensEval returns for the second time with multilingual offensive language identification in social media.OffensEval 2 focuses on several languages Arabic, Danish, English, Greek and Turkish motivating participants to submit multilingual offensive language identification systems.This paper revisits the problem of offensive language identification describing our submission to the SemEval-2020 Task 1 (Zampieri et al., 2020).The remainder of this paper is structured as follows: Section 2 describes the related work done in the field of aggression detection, Section 3 has a description of the dataset.Section 4 describes the system that was submitted, split into a description of how the data was processed and the architectures of the classifiers that were used.Section 5 presents an analysis of the results of our evaluation of the different architectures, as well as of the final submission.Finally, Section 6 offers some final remarks and a conclusion. Relevant WorkThe majority of text classification approaches in early research work were supported by traditional or feature-based supervised learning methods.Following this tradition, Malmasi and Zampieri (2017) proposed a linear Support Vector Machine (SVM) classifier for hate speech detection in social media.The best accuracy of this approach was obtained by using character n-grams as model features.Another research suggested stacked ensemble system using logistic regression and random forest-based classifiers
Impact of teachers’ grading policy on the identification of at-risk students in learning analytics

The purpose of learning analytics is to promote student success in the classroom. To implement the framework of learning analytics, researchers have adopted machine learning methodologies to identify at-risk students at an early stage. In theory, machine learning is a mathematical algorithm that improves automation through experience. The experience is the data collected from online learning platforms, and in general, the data contain various features such as the number of times that a student accesses the learning material each week. Relevant studies have demonstrated extremely high accuracy in identifying at-risk students using identification models trained by machine learning. However, numerous details and data challenges have been overlooked in prior studies, calling into question the accuracy of past contributions. In this study, we focused on one type of data challenge: data imbalance. The data imbalance problems in education are usually the result of teachers’ grading policy. To highlight the seriousness of this issue, we collected data from 12 blended learning courses and summarized 3 types of grading policies: discrimination, stringency, and leniency. We then provided evidence that the leniency strategy causes the illusion of high accuracy of at-risk student identification. Finally, we verified a robust method to address the effectiveness of the leniency strategy, and using these results, we summarized the characteristics of students who tend to be misidentified by machine learning methodology.
Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval

It is very pleasing for human health that medical knowledge has increased and the technological infrastructure improves medical systems. The widespread use of medical imaging devices has been instrumental in saving lives by allowing early diagnosis of many diseases. These medical images are stored in large databases for many purposes. These datasets are used when a suspicious diagnostic case is encountered or to gain experience for inexperienced radiologists. To fulfill these tasks, images similar to one query image are searched from within the large dataset. Accuracy and speed are vital for this process, which is called content-based image retrieval (CBIR). In the literature, the best way to perform a CBIR system is by using hash codes. This study provides an effective hash code generation method based on feature selection-based downsampling of deep features extracted from medical images. Firstly, pre-hash codes of 256-bit length for each image are generated using a pairwise siamese network architecture that works based on the similarity of two images. Having a pre-hash code between -1 and 1 makes it very easy to generate hash code in hashing algorithms. For this reason, all activation functions of the proposed convolutional neural network (CNN) architecture are selected as hyperbolic tanh. Finally, neighborhood component analysis (NCA) feature selection methods are used to convert pre-hash code to binary hash code. This also downsamples the hash code length to 32-bit, 64-bit, or 96-bit levels. The performance of the proposed method is evaluated using NEMA MRI and NEMA CT datasets.
Findings of the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment

Following two preceding WMT Shared Task on Parallel Corpus Filtering (Koehn et al., 2018, 2019), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train ma-chine translation systems. This year, the task tackled the low resource condition of Pashto–English and Khmer–English and also included the challenge of sentence alignment from document pairs.
A Blockchain-Driven Electronic Contract Management System for Commodity Procurement in Electronic Power Industry

In the era of paper-based contract, a lot of time, human labor and expenses are required to handle the process of contract drafting, contract signing, contract execution, and payment settlement. The emerging of electronic contract enhances the tedious signing process of paper-based contract and improves the efficiency of contract management. However, due to the centralized system architecture and the database-based storage schema, the stored contract data is at high risk of information leaking, data tampering and hacker attacks. In this study, we introduce Blockchain technology to the contract management, and develop a process-oriented contract management system (BEcontractor) for a Hangzhou-located power grid enterprise X, aiming to solve a series of security issues existing in the traditional electronic contract system. By deploying BEcontractor, procurement activities could be resumed online among X and its nation-widely commodity suppliers during COVID-19 epidemic. Up to September 2020, 6336 electronic contracts have been signed, with an accumulated amount of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\yen ~6.5$ </tex-math></inline-formula> billion. It is showed that the cost for accomplishing the contract signing process was significantly reduced, and the payment period was shortened from three months to around one month.
Teachers' and Students' Perceptions of Virtual Classes and the effectiveness of Virtual Classes in Enhancing Communication Skills

The use of the Internet is increasing the possibilities for language learning and teaching (Sallbego & Tumolo, 2015).Virtual classes use computer-mediated technology to facilitate learning, teaching, and communication processes.The purpose of this study is to investigate teachers' and students' perceptions of English as a foreign language (EFL) virtual classes.Moreover, it also aims to highlight the effect of this kind of class on enhancing communication skills.Therefore, the main research question is about the perceptions that students and teachers have of EFL virtual classes.This study conducted in the Female English Department at King Khalid University in Abha.The participants were thirty teachers and students who were divided into two equal groups (fifteen in each group).Regarding that, participants answered a particular questionnaire.The results show that the majority of the students and teachers possess positive attitudes toward teaching and learning through EFL virtual classes.Moreover, they also agree with the significant role of virtual courses in enhancing communication skills.Thus, implementing EFL virtual classes can be recommended as beneficial.This study states some recommendations in light of its findings.
Vocabulary-Wide Credit Assignment for Training Image Captioning Models

Reinforcement learning (RL) algorithms have been shown to be efficient in training image captioning models. A critical step in RL algorithms is to assign credits to appropriate actions. There are mainly two classes of credit assignment methods in existing RL methods for image captioning, assigning a single credit for the whole sentence and assigning a credit to every word in the sentence. In this article, we propose a new credit assignment method which is orthogonal to the above two. It assigns every word in vocabulary an appropriate credit at each generation step. It is called vocabulary-wide credit assignment. Based on this we propose a Vocabulary-Critical Sequence Training (VCST). VCST can be incorporated into existing RL methods for training image captioning models to achieve better results. Extensive experiments with many popular models validated the effectiveness of VCST.
Mobile App for Science Education: Designing the Learning Approach

This paper reports research work related to a wider study, aimed at developing a mobile app for Science Education in primary-school. Several studies reveal that Science Education can be improved by using technology, namely educational software. However, to promote a structured use of technology, innovative learning approaches must be designed for educational software. This paper aims to answer how the interaction between students and a mobile app for Science Education can promote students’ scientific competences development and self-regulated learning. To achieve this, a learning approach was designed, combining the Universal Design for Learning principles, Inquiry-Based Science Education and the BSCS 5E – teaching model for Science Education designed by the Biological Sciences Curriculum Study, which results in the acronym of the model. The 5E is related to each phase of the model: Engagement; Exploration; Explanation; Elaboration; Evaluation. The proposed was based on a grounded, participatory, and user-centred approach, crossing literature contributions with data collected among primary-school teachers through the application of a questionnaire (n = 118). Data collected allowed deductions about the expected adequacy of the learning approach, according to Nieveen’s criteria for high quality educational interventions. This adequacy was revealed through the teachers’ conceptions about the potential impact of the conceptualized mobile app (i) to provide a comprehensive and practical Science Education learning; and (ii) to enhance students’ scientific competences development and self-regulated learning. The paper aims to contribute to the design of an innovative learning approach in Science Education and to share it with other researchers since it can be expanded to other educational software.
Review of micro-expression spotting and recognition in video sequences

Facial micro-expressions are short and imperceptible expressions that involuntarily reveal the true emotions that a person may be attempting to suppress, hide, disguise, or conceal. Such expressions can reflect a person's real emotions and have a wide range of application in public safety and clinical diagnosis. The analysis of facial micro-expressions in video sequences through computer vision is still relatively recent. In this research, a comprehensive review on the topic of spotting and recognition used in microexpression analysis databases and methods, is conducted, and advanced technologies in this area are summarized. In addition, we discuss challenges that remain unresolved alongside future work to be completed in the field of micro-expression analysis.
Meta-Learning With Latent Space Clustering in Generative Adversarial Network for Speaker Diarization

The performance of most speaker diarization systems with x-vector embeddings is both vulnerable to noisy environments and lacks domain robustness. Earlier work on speaker diarization using generative adversarial network (GAN) with an encoder network (ClusterGAN) to project input x-vectors into a latent space has shown promising performance on meeting data. In this paper, we extend the ClusterGAN network to improve diarization robustness and enable rapid generalization across various challenging domains. To this end, we fetch the pre-trained encoder from the ClusterGAN and fine tune it by using prototypical loss (meta-ClusterGAN or MCGAN) under the meta-learning paradigm. Experiments are conducted on CALLHOME telephonic conversations, AMI meeting data, DIHARD-II (dev set) which includes challenging multi-domain corpus, and two child-clinician interaction corpora (ADOS, BOSCC) related to the autism spectrum disorder domain. Extensive analyses of the experimental data are done to investigate the effectiveness of the proposed ClusterGAN and MCGAN embeddings over x-vectors. The results show that the proposed embeddings with normalized maximum eigengap spectral clustering (NME-SC) back-end consistently outperform the Kaldi state-of-the-art x-vector diarization system. Finally, we employ embedding fusion with x-vectors to provide further improvement in diarization performance. We achieve a relative diarization error rate (DER) improvement of 6.67% to 53.93% on the aforementioned datasets using the proposed fused embeddings over x-vectors. Besides, the MCGAN embeddings provide better performance in the number of speakers estimation and short speech segment diarization compared to x-vectors and ClusterGAN on telephonic conversations.
Traditional Bangladeshi Sports Video Classification Using Deep Learning Method

Sports activities play a crucial role in preserving our health and mind. Due to the rapid growth of sports video repositories, automatized classification has become essential for easy access and retrieval, content-based recommendations, contextual advertising, etc. Traditional Bangladeshi sport is a genre of sports that bears the cultural significance of Bangladesh. Classification of this genre can act as a catalyst in reviving their lost dignity. In this paper, the Deep Learning method is utilized to classify traditional Bangladeshi sports videos by extracting both the spatial and temporal features from the videos. In this regard, a new Traditional Bangladeshi Sports Video (TBSV) dataset is constructed containing five classes: Boli Khela, Kabaddi, Lathi Khela, Kho Kho, and Nouka Baich. A key contribution of this paper is to develop a scratch model by incorporating the two most prominent deep learning algorithms: convolutional neural network (CNN) and long short term memory (LSTM). Moreover, the transfer learning approach with the fine-tuned VGG19 and LSTM is used for TBSV classification. Furthermore, the proposed model is assessed over four challenging datasets: KTH, UCF-11, UCF-101, and UCF Sports. This model outperforms some recent works on these datasets while showing 99% average accuracy on the TBSV dataset.
Human Motion Transfer from Poses in the Wild

In this paper, we tackle the problem of human motion transfer, where we synthesize novel motion video for a target person that imitates the movement from a reference video. It is a video-to-video translation task in which the estimated poses are used to bridge two domains. Despite substantial progress on the topic, there exist several problems with the previous methods. First, there is a domain gap between training and testing pose sequences–the model is tested on poses it has not seen during training, such as difficult dancing moves. Furthermore, pose detection errors are inevitable, making the job of the generator harder. Finally, generating realistic pixels from sparse poses is challenging in a single step. To address these challenges, we introduce a novel pose-to-video translation framework for generating high-quality videos that are temporally coherent even for in-the-wild pose sequences unseen during training. We propose a pose augmentation method to minimize the training-test gap, a unified paired and unpaired learning strategy to improve the robustness to detection errors, and two-stage network architecture to achieve superior texture quality. To further boost research on the topic, we build two human motion datasets. Finally, we show the superiority of our approach over the state-of-the-art studies through extensive experiments and evaluations on different datasets.
Unsupervised Feature Elimination via Generative Adversarial Networks: Application to Hair Removal in Melanoma Classification

Eliminating the undesirable features is crucial to computer vision applications since undesirable features degrade the visibility of images.For that purpose, denoising, dehazing and deraining have been actively studied in both traditional model-based approaches and modern deep learning methods.However, elimination of hair in dermoscopic images has not received sufficient attention despite its significance and potential.Meanwhile, hair removal algorithms remain within the classical morphological methodologies, while only a few attempts apply the latest data-driven techniques.Hair is desired to be removed in dermoscopy applications because it causes undesired effects such as occlusions in lesion areas.However, removing hair is challenging because of its inherent complex structure and variations.In this paper, we propose a new unsupervised algorithm for hair removal and evaluate it on a real-world melanoma dataset.The proposed method eliminates hair from dermoscopic images by inducing a reconstructed distribution of images with hair to resemble a hairless distribution using generative adversarial learning.In the generative adversarial learning framework, hair features are characterized with a coarse-grained label simply via a binary classifier.At the same time, the important features of the lesions are preserved by minimizing L 1 -norm reconstruction loss based on Laplace noise assumption.The qualitative evaluation of the hair-removed results show that the proposed algorithm is robust to hair variations, and the quantitative results demonstrate that applying our hair removal algorithm considerably improves the performance of melanoma classification, outperforming the benchmarks.
Multiparty weighted threshold quantum secret sharing based on the Chinese remainder theorem to share quantum information

Abstract Secret sharing is a widely-used security protocol and cryptographic primitive in which all people cooperate to restore encrypted information. The characteristics of a quantum field guarantee the security of information; therefore, many researchers are interested in quantum cryptography and quantum secret sharing (QSS) is an important research topic. However, most traditional QSS methods are complex and difficult to implement. In addition, most traditional QSS schemes share classical information, not quantum information which makes them inefficient to transfer and share information. In a weighted threshold QSS method, each participant has each own weight, but assigning weights usually costs multiple quantum states. Quantum state consumption will therefore increase with the weight. It is inefficient and difficult, and therefore not able to successfully build a suitable agreement. The proposed method is the first attempt to build multiparty weighted threshold QSS method using single quantum particles combine with the Chinese remainder theorem (CRT) and phase shift operation. The proposed scheme allows each participant has its own weight and the dealer can encode a quantum state with the phase shift operation. The dividing and recovery characteristics of CRT offer a simple approach to distribute partial keys. The reversibility of phase shift operation can encode and decode the secret. The proposed weighted threshold QSS scheme presents the security analysis of external attacks and internal attacks. Furthermore, the efficiency analysis shows that our method is more efficient, flexible, and simpler to implement than traditional methods.