Reviewerly/exHarmony huggingface dataset:
Demodalizing Face Recognition with Synthetic Samples

Using data generated by generative adversarial networks or three-dimensional (3D) technology for face recognition training is a theoretically reasonable solution to the problems of unbalanced data distributions and data scarcity. However, due to the modal difference between synthetic data and real data, the direct use of data for training often leads to a decrease in the recognition performance, and the effect of synthetic data on recognition remains ambiguous. In this paper, after observing in experiments that modality information has a fixed form, we propose a demodalizing face recognition training architecture for the first time and provide a feasible method for recognition training using synthetic samples. Specifically, three different demodalizing training methods, from implicit to explicit, are proposed. These methods gradually reveal a generated modality that is difficult to quantify or describe. By removing the modalities of the synthetic data, the performance degradation is greatly alleviated. We validate the effectiveness of our approach on various benchmarks of large-scale face recognition and outperform the previous methods, especially in the low FAR range.
Three Steps to Improve Jellyfish Search Optimiser

This paper describes three different mechanisms used in Jellyfish Search (JS) optimiser. At first, an archive of good old solutions is used to prevent getting stuck in the local-optima area. Further, a distribution coefficient beta is adapted during the search process to control population diversity. Finally, an Eigen transformation of individuals in the reproduction process is used occasionally to cope with rotated functions. Three proposed variants of the JS optimiser are compared with the original JS algorithm and nine various well-known Nature-inspired optimisation methods when solving real-world problems of CEC 2011. Provided results achieved by statistical comparison show efficiency of the individual newly employed mechanisms.
Blockchain, Smart Contracts and Intellectual Property. Using distributed ledger technology to protect, license and enforce intellectual property rights

For several years, almost everyone has been talking about blockchain. The underlying distributed ledger technology has become (in)famous as the technology behind cryptocurrencies such as Bitcoin and Ether. But what about blockchain and intellectual property like patents and copyright? Could this technology be used for the protection and enforcement of such rights? Which role can smart contracts play in this regard? This article focuses on questions concerning the requirements for provingthe protection of technical inventions as well as on the administration and exploitation of intellectual property rights. The latter could play an important rolefor intellectual property, which has not been registered or is not subject to registration, such as copyright. For trade secrets, a blockchain could be a useful tool for providing appropriate confidentiality measures. Last but not least, smart contracts in particular could be involved in connection with the transfer and, even more importantly, the licensing of intellectual property and mainly of software.
Hierarchical Information Passing Based Noise-Tolerant Hybrid Learning for Semi-Supervised Human Parsing

Deep learning based human parsing methods usually require a large amount of training data to reach high performance. However, it is costly and time-consuming to obtain manually annotated high quality labels for a large scale dataset. To alleviate annotation efforts, we propose a new semi-supervised human parsing method for which we only need a small number of labels for training. First, we generate high quality pseudo labels on unlabeled images using a hierarchical information passing network (HIPN), which reasons human part segmentation in a coarse to fine manner. Furthermore, we develop a noise-tolerant hybrid learning method, which takes advantage of positive and negative learning to better handle noisy pseudo labels. When evaluated on standard human parsing benchmarks, our HIPN achieves a new state-of-the-art performance. Moreover, our noise-tolerant hybrid learning method further improves the performance and outperforms the state-of-the-art semi-supervised method (i.e. GRN) by 4.47 points w.r.t mIoU on the LIP dataset.
Systemic Mapping and Design Research: Towards Participatory Democratic Engagement

This article presents an argument to extend possibilities and discussions about the role of design in democratic participation. We ground this argument in case studies and observations of several grassroots experimental participatory design workshops run with the intention of producing spaces for community deliberation and a tangible transformation of these communities. These cases show how systemic mapping and prototyping are used to increase community understanding of how potential futures represent values systems that should correspond to the values the community would like to see in place. The methodologies used on these workshops are presented it here as an opportunity to question the role of design in democratic deliberation and policy making.
Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning.

Imagine you are in a supermarket.You have two bananas in your basket and want to buy four apples.How many fruits do you have in total?This seemingly straightforward question can be challenging for data-driven language models, even if trained at scale.However, we would expect such generic language models to possess some mathematical abilities in addition to typical linguistic competence.Towards this goal, we investigate if a commonly used language model, BERT, possesses such mathematical abilities and, if so, to what degree.For that, we fine-tune BERT on a popular dataset for word math problems, AQuA-RAT, and conduct several tests to understand learned representations better.Since we teach models trained on natural language to do formal mathematics, we hypothesize that such models would benefit from training on semi-formal steps that explain how math results are derived.To better accommodate such training, we also propose new pretext tasks for learning mathematical rules.We call them (Neighbor) Reasoning Order Prediction (ROP or NROP).With this new model, we achieve significantly better outcomes than data-driven baselines and even on-par with more tailored models.We also show how to reduce positional bias in such models.
3D SURFACE RECONSTRUCTION FROM MULTI-DATE SATELLITE IMAGES

Abstract. The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point clouds for satellite image pairs. Recently, a Structure from Motion (SfM) based approach has been proposed, which allows to reconstruct point clouds from multiple satellite images. In this work, we propose an extension of this SfM based pipeline that allows us to reconstruct not only point clouds but watertight meshes including texture information. We provide a detailed description of several steps that are mandatory to exploit state-of-the-art mesh reconstruction algorithms in the context of satellite imagery. This includes a decomposition of finite projective camera calibration matrices, a skew correction of corresponding depth maps and input images as well as the recovery of real-world depth maps from reparameterized depth values. The paper presents an extensive quantitative evaluation on multi-date satellite images demonstrating that the proposed pipeline combined with current meshing algorithms outperforms state-of-the-art point cloud reconstruction algorithms in terms of completeness and median error. We make the source code of our pipeline publicly available.
Democratizing AI in biomedical image classification using virtual reality

Virtual reality (VR) offers immersive visualization and intuitive interaction. We leverage VR to enable any biomedical professional to deploy a deep learning (DL) model for image classification. While DL models can be powerful tools for data analysis, they are also challenging to understand and develop. To make deep learning more accessible and intuitive, we have built a virtual reality-based DL development environment. Within our environment, the user can move tangible objects to construct a neural network only using their hands. Our software automatically translates these configurations into a trainable model and then reports its resulting accuracy on a test dataset in real-time. Furthermore, we have enriched the virtual objects with visualizations of the model's components such that users can achieve insight about the DL models that they are developing. With this approach, we bridge the gap between professionals in different fields of expertise while offering a novel perspective for model analysis and data interaction. We further suggest that techniques of development and visualization in deep learning can benefit by integrating virtual reality.
Towards a more Robust Evaluation for Conversational Question Answering

With the explosion of chatbot applications, Conversational Question Answering (CQA) has generated a lot of interest in recent years.Among proposals, reading comprehension models which take advantage of the conversation history (previous QA) seem to answer better than those which only consider the current question.Nevertheless, we note that the CQA evaluation protocol has a major limitation.In particular, models are allowed, at each turn of the conversation, to access the ground truth answers of the previous turns.Not only does this severely prevent their applications in fully autonomous chatbots, it also leads to unsuspected biases in their behavior.In this paper, we highlight this effect and propose new tools for evaluation and training in order to guard against the noted issues.The new results that we bring come to reinforce methods of the current state of the art.
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering

For stability and reliability of real-world applications, the robustness of DNNs in unimodal tasks has been evaluated. However, few studies consider abnormal situations that a visual question answering (VQA) model might encounter at test time after deployment in the real-world. In this study, we evaluate the robustness of state-of-the-art VQA models to five different anomalies, including worst-case scenarios, the most frequent scenarios, and the current limitation of VQA models. Different from the results in unimodal tasks, the maximum confidence of answers in VQA models cannot detect anomalous inputs, and post-training of the outputs, such as outlier exposure, is ineffective for VQA models. Thus, we propose an attention-based method, which uses confidence of reasoning between input images and questions and shows much more promising results than the previous methods in unimodal tasks. In addition, we show that a maximum entropy regularization of attention networks can significantly improve the attention-based anomaly detection of the VQA models. Thanks to the simplicity, attention-based anomaly detection and the regularization are model-agnostic methods, which can be used for various cross-modal attentions in the state-of-the-art VQA models. The results imply that cross-modal attention in VQA is important to improve not only VQA accuracy, but also the robustness to various anomalies.
Static-Dynamic Interaction Networks for Offline Signature Verification

Offline signature verification is a challenging issue that is widely used in various fields. Previous approaches model this task as a static feature matching or distance metric problem of two images. In this paper, we propose a novel Static-Dynamic Interaction Network (SDINet) model which introduces sequential representation into static signature images. A static signature image is converted to sequences by assuming pseudo dynamic processes in the static image. A static representation extracting deep features from signature images describes the global information of signatures. A dynamic representation extracting sequential features with LSTM networks characterizes the local information of signatures. A dynamic-to-static attention is learned from the sequences to refine the static features. Through the static-to-dynamic conversion and the dynamic-to-static attention, the static representation and dynamic representation are unified into a compact framework. The proposed method was evaluated on four popular datasets of different languages. The extensive experimental results manifest the strength of our model.
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation

Due to the great potential in facilitating software development, code generation has attracted increasing attention recently.Generally, dominant models are Seq2Tree models, which convert the input natural language description into a sequence of tree-construction actions corresponding to the pre-order traversal of an Abstract Syntax Tree (AST).However, such a traversal order may not be suitable for handling all multi-branch nodes.In this paper, we propose to equip the Seq2Tree model with a context-based Branch Selector, which is able to dynamically determine optimal expansion orders of branches for multibranch nodes.Particularly, since the selection of expansion orders is a non-differentiable multi-step operation, we optimize the selector through reinforcement learning, and formulate the reward function as the difference of model losses obtained through different expansion orders.Experimental results and in-depth analysis on several commonly-used datasets demonstrate the effectiveness and generality of our approach.We have released our code at https: //github.com/DeepLearnXMU/CG-RL.
Automatic Language Identification in Code-Switched Hindi-English Social Media Text

Natural Language Processing (NLP) tools typically struggle to process code-switched data and so linguists are commonly forced to annotate such data manually. As this data becomes more readily available, automatic tools are increasingly needed to help speed up the annotation process and improve consistency. Last year, such a toolkit was developed to semi-automatically annotate transcribed bilingual code-switched Vietnamese-English speech data with token-based language information and POS tags (hereafter the CanVEC toolkit, L. Nguyen &amp; Bryant, 2020). In this work, we extend this methodology to another language pair, Hindi-English, to explore the extent to which we can standardise the automation process. Specifically, we applied the principles behind the CanVEC toolkit to data from the International Conference on Natural Language Processing (ICON) 2016 shared task, which consists of social media posts (Facebook, Twitter and WhatsApp) that have been annotated with language and POS tags (Molina et al., 2016). We used the ICON-2016 annotations as the gold-standard labels in the language identification task. Ultimately, our tool achieved an F1 score of 87.99% on the ICON-2016 data. We then evaluated the first 500 tokens of each social media subset manually, and found almost 40% of all errors were caused entirely by problems with the gold-standard, i.e., our system was correct. It is thus likely that the overall accuracy of our system is higher than reported. This shows great potential for effectively automating the annotation of code-switched corpora, on different language combinations, and in different genres. We finally discuss some limitations of our approach and release our code and human evaluation together with this paper.
Analisa Pemanfaatan E-Learning Menggunakan Data Statistik Pengguna Aplikasi Startup Pendidikan Selama Wabah Pandemi Covid-19

The changes that happened during the epidemic due to the Central Government's and the Ministry of Education and Culture's (Kemendikbud) closure of schools had a considerable impact on the learning models and methods followed by all Indonesian students. As a result, this policy implements a digital education or e-learning system implemented during the Covid-19 outbreak, requiring students to use the internet to supplement their home education. However, the public is not well informed about the use of free learning support applications. As a result, information from online learning applications and digital education is required, as these are used as educational support tools for all students. The purpose of this study is to examine how digital learning systems, or e-learning, are used by examining the statistical data of users of a widely used educational startup application. The Teacher Room, Zenius, and Quipper applications will be examined. The research method compares three educational applications used in Indonesia that rely on secondary data from www.similiarweb.com to ascertain the number of website visits and ranking. These findings contribute to the body of knowledge regarding digital learning systems during the Covid-19 outbreak
POLICY CHANGE IMPLEMENTATION: THE CASE OF THE CEFR IN MALAYSIAN ESL CLASSROOMS

Background and purpose: With rapid global development happening in the world today, the field of education has been awash with various change forces. In an effort to ensure its system is globally competitive, Malaysia has taken a step to align its English language education system to the Common European Framework of Reference (CEFR). However, any change in policy is not without problems and challenges as studies have shown that challenges were inevitable and stakeholders’ buy-in on the new change is necessary. Hence, this study was undertaken to investigate the challenges faced by teachers in implementing the CEFR in Malaysian ESL classrooms, their belief on this new reform and their readiness to implement the CEFR.&#x0D; &#x0D; Methodology: A mixed-method design was utilized with the use of questionnaire and semi structured interview as means for data collection. 365 English language teachers responded to the questionnaire while 15 English language teachers participated in interview sessions.&#x0D; &#x0D; Findings: The data revealed five challenges namely teachers’ motivation, materials, time, students’ proficiency level and facilities. Despite facing multiple challenges, all teachers exhibited positive belief towards the adoption of the CEFR. The study also showed that although teachers were emotionally ready to accept the change, their cognitive readiness for change however is dependent upon three important facets namely time, collective effort and adequate materials.&#x0D; &#x0D; Contributions: The study provides insights into the change in policy involving the CEFR implementation in the Malaysian English language education system which contributes to the scarce literature on the implementation of CEFR.&#x0D; &#x0D; Keywords: Policy implementation, challenges in policy change, teachers’ belief, Common European Framework of Reference (CEFR), readiness for change.&#x0D; &#x0D; Cite as: C. Alih, N. A., Abdul Raof, A. H., &amp; Md. Yusof, M. A. (2021). Policy change implementation: The case of the CEFR in Malaysian ESL classrooms. Journal of Nusantara Studies, 6(2), 296-317. http://dx.doi.org/10.24200/jonus.vol6iss2pp296-317
Optimising monthly tilt angles of solar panels using particle swarm optimisation algorithm

This paper presents an intelligent computational method using the PSO (particle swarm optimisation) algorithm to determine the optimum tilt angle of solar panels in PV systems. The objective of the paper is to assess the performance of this method against conventional methods of determining the optimum tilt angle. The method presented here can be used to determine the optimum tilt angle at any location around the world. In this paper, it was applied to Brunei Darussalam, and succeeded in computing monthly optimum tilt angles, ranging from 34.7ᵒ in December to -26.7ᵒ in September. Results showed that changing the tilt angle every month, as determined by the PSO algorithm, increased annual yield by: (i) 5.94%, compared to keeping it fixed at 0ᵒ, (ii) 8.65%, compared to Lunde’s method and (iii) 17.31%, compared to Duffie and Beckman’s method. Benchmark test functions were used to compare and evaluate the performance of the PSO algorithm with the artificial bee colony (ABC) algorithm, another metaheuristic algorithm. The tests revealed that the PSO algorithm outperformed the ABC algorithm, exhibiting lower root mean square error and standard deviation, better convergence to the global minimum, more accurate location of the global minimum, and faster execution times.
On the Interaction of Belief Bias and Explanations

A myriad of explainability methods have been proposed in recent years, but there is little consensus on how to evaluate them.While automatic metrics allow for quick benchmarking, it isn't clear how such metrics reflect human interaction with explanations.Human evaluation is of paramount importance, but previous protocols fail to account for belief biases affecting human performance, which may lead to misleading conclusions.We provide an overview of belief bias, its role in human evaluation, and ideas for NLP practitioners on how to account for it.For two experimental paradigms, we present a case study of gradientbased explainability introducing simple ways to account for humans' prior beliefs: models of varying quality and adversarial examples.We show that conclusions about the highest performing methods change when introducing such controls, pointing to the importance of accounting for belief bias in evaluation.
AggGen: Ordering and Aggregating while Generating

Xinnuo Xu, Ondřej Dušek, Verena Rieser, Ioannis Konstas. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis.

Today's Internet is awash in memes as they are humorous, satirical, or ironic which make people laugh. According to a survey, 33% of social media users in age bracket [13-35] send memes every day, whereas more than 50% send every week. Some of these memes spread rapidly within a very short time-frame, and their virality depends on the novelty of their (textual and visual) content. A few of them convey positive messages, such as funny or motivational quotes; while others are meant to mock/hurt someone's feelings through sarcastic or offensive messages. Despite the appealing nature of memes and their rapid emergence on social media, effective analysis of memes has not been adequately attempted to the extent it deserves. Recently, in SemEval'20, a pioneering attempt has been made in this direction by organizing a shared task on `Memotion Analysis' (meme emotion analysis). As expected, the competition attracted more than 500 participants with the final submission of [23-32] systems across three sub-tasks. In this paper, we attempt to solve the same set of tasks suggested in the SemEval'20 - Memotion Analysis competition. We propose a multi-hop attention-based deep neural network framework, called MHA-Meme, whose prime objective is to leverage the spatial-domain correspondence between the visual modality (an image) and various textual segments to extract fine-grained feature representations for classification. We evaluate MHA-Meme on the `Memotion Analysis' dataset for all three sub-tasks - sentiment classification, affect classification, and affect class quantification. Our comparative study shows state-of-the-art performances of MHA-Meme for all three tasks compared to the top systems that participated in the competition. Unlike all the baselines which perform inconsistently across all three tasks, MHA-Meme outperforms baselines in all the tasks on average. Moreover, we validate the generalization of MHA-Meme on another set of manually annotated test samples and observe it to be consistent. Finally, we establish the interpretability of MHA-Meme.