Reviewerly/exHarmony huggingface dataset:
SAFA: Structure Aware Face Animation

Recent success of generative adversarial networks (GAN) has made great progress on the face animation task. However, the complex scene structure of a face image still makes it a challenge to generate videos with face poses significantly deviating from the source image. On one hand, without knowing the facial geometric structure, generated face images might be improperly distorted. On the other hand, some area of the generated image might be occluded in the source image, which makes it difficult for GAN to generate realistic appearance. To address these problems, we propose a structure aware face animation (SAFA) method which constructs specific geometric structures to model different components of a face image. Following the well recognized motion based face animation technique, we use a 3D morphable model (3DMM) to model the face, multiple affine transforms to model the other foreground components like hair and beard, and an identity transform to model the background. The 3DMM geometric embedding not only helps generate realistic structure for the driving scene, but also contributes to better perception of occluded area in the generated image. Besides, we further propose to exploit the widely studied inpainting technique to faithfully recover the occluded image area. Both quantitative and qualitative experiment results have shown the superiority of our method. Code is available at https://github.com/Qiulin-W/SAFA.
Detecting spam messages using the naive Bayes algorithm of basic machine learning

In this article, we consider the definition of spam messages using the naive Bayes algorithm based on machine learning and study the algorithm of inclusion. A table has been compiled for determining spam/non-spam by example and calculating using the naive Bayes algorithm based on machine learning.
Dual-stream collaborative tracking algorithm combined with reliable memory based update

Most visual tracking algorithms based on Siamese network adopt a fixed template in the whole tracking process, which sacrifices the available target appearance information. To address this issue, we propose a dual-stream collaborative tracking algorithm combined with reliable memory based update in this paper. Specifically, we first conduct a multi-task convolutional network consisting of convolutional kernel fusion and semantic integration, hence enhancing the discriminability of the convolutional model. Secondly, a reliable template storage policy is developed to establish a dynamic memory queue that carries available appearance information. In addition, to improve the template adaptability against appearance variation, we train a dynamic update network by serving the memory queue as samples, thereby realizes the adaptive update of the object template. Finally, a dual-stream collaborative tracking framework is proposed to integrate the matching results of the initial branch and the updated branch in the response layer, which alleviates the limited representation problems with the fixed template. We perform a thorough performance evaluation on the OTB2013, OTB2015, DTB70, Temple-Color-128, VOT2016, VOT2018, UAV20L and LaSOT benchmarks. Extensive experimental results show that our proposed method outperforms several state-of-the-art algorithms and achieves real-time operations.
Transferable discriminative non-negative matrix factorization for cross-database facial expression recognition

Traditional facial expression recognition (FER) systems often have an ideal assumption that the training and testing facial images are collected from the same or similar databases. However, it cannot always be satisfied in real applications, and the recognition accuracy would decrease greatly. To solve this shortcoming, in this paper, we present a novel transferable discriminative non-negative matrix factorization (TDNMF) approach for cross-database FER. In TDNMF, we use the similarity and dissimilarity information obtained from labels to guide matrix factorization and feature transfer learning. Meanwhile, we devise a graph regularization, which considers the inter- and intra-database similarity, to further mitigate the distribution shift across databases. At last, we conduct extensive experiments on several benchmarks for evaluation. The results demonstrate the effectiveness of the proposed method in comparison with some state-of-the-art transfer learning algorithms.
An Analysis of Machine Learning Algorithms and Deep Neural Networks for Email Spam Classification using Natural Language Processing

Due to the extensive use of technology in our daily lives, email has become essential for online correspondence between individuals from all walks of life. As such certain individuals have weaponized this service by bulk mailing malicious emails to recipients with the goal of retrieving some form of classified information. Thus, Email classification has become a major area of research as it enables identification and isolation of such malicious emails. The objectives of this paper include a robust comparison of several traditional machine learning (ML) algorithms, exploring transfer learning with static (non-trainable) pretrained GLOVE (Global word vector representation) embedding, comparison of several deep learning models trained with GLOVE and keras embedding separately. Among ML classifiers, XGBoost achieved the highest evaluation scores. Among deep learning algorithms, keras embedding based models outperformed GLOVE embedding based models by a small margin which shows the efficiency of transfer learning in downstream NLP tasks (parts of speech tagging).
Smart Home Care: Towards Supporting Elderlies in the Comfort and Safety of their (Smart) Homes

The aging of the population will be a major challenge in the coming years and will lead to massive changes related to aging peoples' safety, social inclusion, and well-being. The nature of the aid provided to elderly people is already changing, with a shift towards home care (assisted living) as compared to traditional care homes. In this position paper, we highlight the need for appropriate home automation and support systems that ensure the safety and well-being of aging occupants and maintain their social links with various people around them. Existing smart home systems do not meet all the requirements and safety features required for this purpose, in particular in terms of usability, privacy, and security. We describe the challenges the research community and the industry will need to address when designing smart home care systems and lay out a research agenda that we hope will stir more work in this important emerging area.
Transaction Confirmation Time Estimation in the Bitcoin Blockchain

As Bitcoin is universally recognized as the most popular cryptocurrency, more and more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. However, transactions cannot be confirmed altogether into the next block due to the limited block capacity. One of the most demanding requirements for users to use Bitcoin is to estimate the confirmation time of a newly submitted transaction. In this paper, we propose two approaches for estimating the confirmation time for a single transaction. The first approach DcyMean makes the estimation based on the historical confirmation time of transactions included in the blockchain. The second approach CTEN is built on neural networks to estimate based on a variety of factors including the transaction itself, block states and mempool states. Finally, we conduct experiments on real Bitcoin blockchain datasets to demonstrate the effectiveness and efficiency of our proposed approaches. Each of our approaches can finish training and estimation within one block interval, demonstrating that our approaches can process real-time cases.
A Bidirectional LSTM Model for Classifying Chatbot Messages

Online channels, e.g., Facebook Messenger and Line, are widely used especially in COVID-19 pandemic. To quickly respond to their customer, chatbot system are implemented in many companies or organizations, connected to those channels. The Office of Registrar, Thammasat University also implements a chatbot to answer questions from students. An important step in the chatbot system is to know an intention of a question message. A bidirectional LSTM model is employed to classify a question message from the chatbot system into five intention classes. The experimental results shows that the obtained model yields an accuracy of 0.80 on our validation dataset.
Modelling Surround‐aware Contrast Sensitivity for HDR Displays

Abstract Despite advances in display technology, many existing applications rely on psychophysical datasets of human perception gathered using older, sometimes outdated displays. As a result, there exists the underlying assumption that such measurements can be carried over to the new viewing conditions of more modern technology. We have conducted a series of psychophysical experiments to explore contrast sensitivity using a state‐of‐the‐art HDR display, taking into account not only the spatial frequency and luminance of the stimuli but also their surrounding luminance levels. From our data, we have derived a novel surround‐aware contrast sensitivity function (CSF), which predicts human contrast sensitivity more accurately. We additionally provide a practical version that retains the benefits of our full model, while enabling easy backward compatibility and consistently producing good results across many existing applications that make use of CSF models. We show examples of effective HDR video compression using a transfer function derived from our CSF, tone‐mapping and improved accuracy in visual difference prediction.
Anonymous Privacy-Preserving Consensus via Mixed Encryption Communication

This brief introduces the concept of anonymous communication into multi-agent systems and proposes a new solution to deal with the trade-off between privacy and accuracy in privacy-preserving consensus. Furthermore, mixed encryption is applied into multi-agent systems for the first time which provides a safe and high-efficient communication. Specifically, asymmetric encryption is used at the beginning of communication to guarantee the secure exchange of initial values and a new symmetric encryption approach, called state difference, is designed and used in the following process to reduce the high cost of computing and communicating in asymmetric encryption. Combining the above methods, anonymous mixed encryption communication protocol for multi-agent systems to reach privacy-preserving consensus is finally proposed. The numerical simulation shows the effectiveness of the proposed communication protocol.
Tracing the footsteps of open research data in China

Abstract While the scientific research value, economic value and social value of research data have become increasingly apparent, the significance of open research data has reached a consensus. This article gives an introduction to open research data policies and measures in China, and reports on the status of constructing necessary infrastructure, specifically open data repositories. We compare open data repositories in China and Western countries in terms of scale, subject distribution, data policies, service and content operations. In addition, this article summarizes methods and motivations for data sharing among researchers in China. Finally, the paper discusses the characteristics, potential problems and challenges of China's open research data practices. We conclude with some suggestions for the future development of open research data in China from data policy, infrastructure construction, compliance with international standards and norms, credibility and influence improvement, incentives for data sharing and encouraging data sharing research practices.
Novel Hiring Process using Machine Learning and Natural Language Processing

There is an ever-increasing number of students graduating from colleges every year and entering the workforce. With their primary means of securing employment being through campus hiring and online portals, and the hiring process being automated to a large extent with more and more employers willing to using use automation and other processes such as psychometric tests for job judging suitability of a candidate, it leads to the candidate feeling overwhelmed and confused regarding job applications and the employers may not be not be able to get an accurate assessment during the hiring process. This work seeks to apply domain classification on user uploaded resumes and map user profiles into distinct clusters based on their performance on an aptitude/technical test. The user will be provided with hybrid recommendations based on association rule mining. This proposed hiring process aims to eliminate employee attrition and newly passed out students can get employment based on their interests and abilities.
Towards Synthetic AI Training Data for Image Classification in Intralogistic Settings

Abstract Obtaining annotated data for proper training of AI image classifiers remains a challenge for successful deployment in industrial settings. As a promising alternative to handcrafted annotations, synthetic training data generation has grown in popularity. However, in most cases the pipelines used to generate this data are not of universal nature and have to be redesigned for different domain applications. This requires a detailed formulation of the domain through a semantic scene grammar. We aim to present such a grammar that is based on domain knowledge for the production-supplying transport of components in intralogistic settings. We present a use-case analysis for the domain of production supplying logistics and derive a scene grammar, which can be used to formulate similar problem statements in the domain for the purpose of data generation. We demonstrate the use of this grammar to feed a scene generation pipeline and obtain training data for an AI based image classifier.
Vision-and-Language Navigation Based on Cross-Modal Feature Fusion in Indoor Environment

It is challenging for an agent to simultaneously decipher visual and language information and make decisions to perform corresponding actions. Recently, the vision-and-language navigation task has been proposed to allow the agent to navigate based on a language instruction and the currently visible visual point information in a 3-D indoor real environment. The key to this task is that the agent needs to understand the information of the two models of vision and language in an unknown environment to navigate effectively. In this study, we capture the alignment relationship between visual features and language features using a cross-modal feature fusion method. Attention is used to set up the cross-modal fusion module so that visual features contain language information and language features contain visual information, thereby allowing the model to learn more feature relationships and improving the success rate (SR) of agent navigation. Considering the practical significance of the navigation of the agent, we aim to shorten the trajectory length of the agent as much as possible while ensuring that the agent reaches the target position successfully. We employ a reinforcement learning algorithm based on the advantage actor critic to constrain the action selection of the agent to shorten the trajectory length. In order to further improve the performance of the model and reduce the difference between the performance of the agent in known environments and unknown environments, we propose the data augmentation method Cro-Speaker, and the three training methods Speaker data augmentation (SD), Cro-Speaker data augmentation (CSD), and Speaker and Cro-Speaker data augmentation (SCSD) based on this method. We evaluate the proposed method based on the Room-to-Room data set. The results show that the proposed method improves the SR of the agent navigation, shortens the length of the navigation trajectory, and exhibits a good generalization performance in known and unknown environments.
Elementary discourse units with sparse attention for multi-label emotion classification

Emotion classification is an important task in natural language processing. Existing studies usually regard it as a multi-label classification task. However, they fail to effectively capture clause information and highlight weak (low-content) emotions that tend to be overwhelmed in co-existing emotions. To tackle these limitations, we propose a novel network “EduEmo”, which contains three parts: BERT-based encoder, Word-level attention layer, and RealFormer-based encoder. Specifically, BERT-based encoder models the associations between labels and words; Word-level attention layer captures the elementary discourse units (EDUs) representations that commonly contain single-emotion; RealFormer-based encoder leverages sparse attention to highlight the weak emotions and model the associations between labels and EDUs. In addition, we propose auxiliary-adversarial training algorithm, which adds perturbations to hard samples along the direction of gradient descent opposite to standard adversarial training. Experimental results on two benchmark datasets show that the proposed model outperforms favorably previous state-of-the-art methods. Experimental results on auxiliary-adversarial training indicate that the proposed training algorithm can further improve the generalization performance of adversarial training on emotion classification.
Application Specific Instrumentation (ASIN)

Nature has, consistently, been an inspiration for scientists and engineers. In this paper, I present a novel paradigm to instrumentation where we co-develop the sensor and machine learning algorithms to bolster the sensor. This is what happens in most species of animals through generations of coevolution. Such sensor and AI (SensAI) empowered instruments are expected to be inexpensive as well as more efficient (in the applications for which they have been developed). In addition to presenting the modus operandi of this application specific instrumentation (ASIN) paradigm this paper shall also, briefly, discuss some of the successful demonstrations of the ASIN paradigm.
Postgraduate Education in a Postcurriculum Context

'Curriculum' is a ubiquitous term despite widespread recognition that there is no consensus on a definition or understanding of what it means. Although it may not be possible to escape the word, it is possible to draw attention to the tensions and debates it provokes. In this chapter, I suggest conceptualising the curriculum in terms of 'ideal types' (abstract analytical constructs): the modernist, postmodernist, new modernist, and postcurriculum. I then propose that contemporary higher education happens in a predominantly 'postcurriculum' context characterised by co-existing features of previous phases. Despite the challenges that exist for postgraduate teachers (particularly those employed to 'teach online'), it is possible to find ways to survive the alienating features of postcurriculum contexts and exploit available opportunities.
Pricing Cyber Security Insurance

Cybersecurity breaches may be correlated due to geography, similar infrastructure, or use of a third-party contractor.We show how a logistic regression may be used to estimate the probability of an attack where breaches may be correlated among firms up and down the supply chain.We also show how a Poisson regression may be used to estimate the number of records breached.Losses arising from cybersecurity breaches have an unknown distribution.We propose the stock price reaction to a breach as an objective measure of the loss in wealth sustained by the firm due to a breach.This loss measure reflects the immediate and long-term effects of a breach, including reputational effects and other intangible impacts that are otherwise more difficult to quantify.We examine stock returns for 258 cybersecurity breach announcements over 2011-2016 in order to obtain the empirical loss distribution.We find a five-day abnormal return of -1.44%.Seventy-one percent of these 258 announcements result in a negative abnormal return, and a gamma distribution provides an excellent fit to these losses.In addition to introducing a predictive model for correlated losses, our study shows how insurers can use either the empirical stock return distribution of losses or the per record cost of a breach in the pricing of cyberinsurance.
CollaborEM: A Self-Supervised Entity Matching Framework Using Multi-Features Collaboration

Entity Matching (EM) aims to identify whether two tuples refer to the same real-world entity and is well-known to be labor-intensive. It is a prerequisite to anomaly detection, as comparing the attribute values of two matched tuples from two different datasets provides one effective way to detect anomalies. Existing EM approaches, due to insufficient feature discovery or error-prone inherent characteristics, are not able to achieve stable performance. In this paper, we present <inline-formula><tex-math notation="LaTeX">${{\sf CollaborEM}}$</tex-math></inline-formula> , a self-supervised entity matching framework via multi-features collaboration. It is capable of (i) obtaining reliable EM results with zero human annotations and (ii) discovering adequate tuples’ features in a fault-tolerant manner. <inline-formula><tex-math notation="LaTeX">${{\sf CollaborEM}}$</tex-math></inline-formula> consists of two phases, i.e., automatic label generation (ALG) and collaborative EM training (CEMT). In the first phase, ALG is proposed to generate a set of positive tuple pairs and a set of negative tuple pairs. ALG guarantees the high quality of the generated tuples, and hence ensures the training quality of the subsequent CEMT. In the second phase, CEMT is introduced to learn the matching signals by discovering graph features and sentence features of tuples collaboratively. Extensive experimental results over eight real-world EM benchmarks show that <inline-formula><tex-math notation="LaTeX">${{\sf CollaborEM}}$</tex-math></inline-formula> outperforms all the existing unsupervised EM approaches and is comparable or even superior to the state-of-the-art supervised EM methods.
AimeLaw at ALQAC 2021: Enriching Neural Network Models with Legal-Domain Knowledge

In this paper, we present our participated systems for three Vietnamese legal text processing tasks at Automated Legal Question Answering Competition (ALQAC 2021). In our systems, we leverage the strength of traditional information retrieval methods (BM25), pre-trained masked language models (BERT), and legal domain knowledge. Our proposed methods help to overcome the shortage of training data. Especially, in the legal textual entailment task, we propose a novel data augmentation method that is based on legal domain knowledge. Evaluation results show the effectiveness of our proposed methods. Our team (AimeLaw) obtained the first prize in Task 2 (legal textual entailment) with 69.89&#x0025; of accuracy; ranked second in Task 1 (legal document retrieval) with 80.61&#x0025; of F2 and in Task 3 (legal question answering) with 64.77&#x0025; of accuracy. We even improved the result on Task 2 to 72.16&#x0025; in an extra experiment.