Reviewerly/exHarmony huggingface dataset:
Distributionally Robust Learning With Stable Adversarial Training

Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data. There is an emerging literature on tackling this problem by minimizing the worst-case risk over an uncertainty set. However, existing methods mostly construct ambiguity sets by treating all variables equally regardless of the stability of their correlations with the target, resulting in the overwhelmingly-large uncertainty set and low confidence of the learner. In this paper, we propose a novel Stable Adversarial Learning (SAL) algorithm that leverages heterogeneous data sources to construct a more practical uncertainty set and conduct differentiated robustness optimization, where covariates are differentiated according to the stability of their correlations with the target. We theoretically show that our method is tractable for stochastic gradient-based optimization and provide the performance guarantees for our method. Empirical studies on both simulation and real datasets validate the effectiveness of our method in terms of uniformly good performance across unknown distributional shifts.
Enhanced Topic Representation by Ambiguity Handling

Most of the existing semantic-based topic models and topic generation approaches use external knowledgebases or ontology to interpret the meanings of the words. However, general ontologies do not cover many ambiguous or specific domain-related words in a text collection. Hence those ambiguous or domain-specific words are neglected in capturing the meanings in topic generation. In this paper, we introduce an approach to disambiguate the unmatched words in a text collection based on related and similar meaning words. Word embeddings are applied to discover similar or related words. We evaluated the topic generation approach with our ambiguity handling technique with a set of state-of-the-art systems which uses an external ontology. Our approach outperformed, and the generated topics were more meaningful. Our ambiguity handling approach interpreted all the important words and included them in the topic generation process.
Intelligent Artificial Agent for Information Retrieval

Throughout the day of the average employee at RS2, there will often be a need to search one of the company’s information repositories. Finding the information will often force employees to perform a context switch and search within the appropriate repository. We propose a system that will facilitate this process by creating a ChatBot that can perform the search within the company’s chat client by making use of the latest machine learning techniques, alongside several NLP techniques and established industry standard information retrieval technologies to allow for a single consolidated, optimised searching system. Results on benchmark datasets show that our system was able to achieve the best results when making use of a combination of traditional and modern techniques.
Introduction

In this chapter, we will first give an introduction to adversarial machine learning in Sect. 1.1. Then, we will introduce our motivation, the related works, and our contribution to linear regression, least absolute shrinkage and selection operator (LASSO) based feature selection and principal component analysis (PCA) based subspace learning in Sects. 1.2, 1.3 and 1.4, respectively.
Running Horses and Racing Stallions: Semantic Precision Signals Product Uniqueness

This research investigates semantic precision – the extent to which the use of language describes objects or subjects exactly as they are. Employing linguistic categorization theory, we predict that, because more precise words are cognitively farther from the top of their semantic category, when the words used to describe a product are more precise, the product is perceived as more unique. That is, more precise words are located deeper in their semantic categories and elicit greater distinctions from the corresponding category top, resulting in perceptions of greater distinctiveness and higher perceived uniqueness of the product. This effect occurs even when more precise words do not directly relate to product attributes. We also find that this precise-unique link is attenuated when the product is perceived as unique by default (e.g., herb icecream). Six experiments, including a field study with actual consumption experiences, support our predictions and provide insights into when and why the precise-unique link occurs. As consumers often value unique products, this research entails practical implications for product positioning and differentiation.
Crypto-Steganographic Validity for Additive Manufacturing (3D Printing) Design Files

Additive Manufacturing (AM) is an important up and coming manufacturing technology which creates three-dimensional objects based on digital design files. While these digital files simplify outsourcing, it also raises security concerns of technical data theft by malicious actors. We propose a novel approach for steganographically embedding validity marks to identify the design owner and outsourced manufacturer in the widely used STL (STereoLithography) file format. It exploits redundancies in STL file encoding and applies basic cryptography to generate a mark that is detectable if illegally modified. While deforming watermarks for 3D-printed objects have been explored, our approach is the first to watermark STL files without affecting the manufactured geometry – a prerequisite for safety-critical functional parts.
Research on application of mask wearing based on YOLOv4 model

Aiming at the problem of low detection accuracy due to the difficulty of small target detection in the mask wearing detection task in public places, this paper proposes a mask wearing detection algorithm based on improved YOLOv4. Firstly, based on the original 3-scale feature map of YOLOv4, the feature map obtained by four times downsampling is introduced to add small target semantic information to the feature fusion module. Secondly, combined with the advantages of DenseNet to enhance the transmission of features, the feature fusion module is improved by using the idea of dense connection, so that each output feature layer can be fully integrated into the semantic information of small targets. The comparison results show that the average accuracy of the improved algorithm is as high as 94.77%, which is 1.25% higher than that of the original YOLOv4 network. Compared with other mainstream algorithms, it also has better detection results in mask wearing detection.
Investigation on Linguistics Learners and Functionality Requirements for Computerized Linguistics Learning Web-Based System

The purpose of this research is to include investigation in developing a user friendly web-based system that provides information on linguistics while highlighting the science behind languages. Currently, the available sites on the web are focusing on languages, where there is very limited opportunity for students to learn linguistics especially when they do not have specific background in the area. The current interface for linguistic learners through existing sites are not as user friendly and attractive with very less HCI concepts applied. This investigation will improve the knowledge delivery through virtual service and by creating the system it creates an opportunity for anyone in tertiary education with interest in linguistics to acquire knowledge whereby linguistics benefits the learners to develop analytical skills, improve creativity and information literacy. The methods used in this investigation are questionnaires and interviews to capture user requirements and functionalities in the system. Results and analysis manage to identify the challenges in learning and teaching linguistics, the difficult aspect of it is to cater for different user learning styles. Linguistics is known as a complicated field; this study aims to improve the flexibility of learning materials for users to learn in a casual way.
Adversarial Robustness of Flow-based Image Super-Resolution

This paper investigates the robustness of deep image super-resolution models using normalizing flow against adversarial attacks. Attack methods specific to flow-based super-resolution models are formulated, and the performance and influences of the attacks are analyzed. We show that flow-based super-resolution models are highly vulnerable to attacks, which are even more serious than other super-resolution models. Potential remedies to the vulnerability are also evaluated.
Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis

Motion capture data has been largely needed in the movie and game industry in recent years. Since the motion capture system is expensive and requires manual post-processing, motion synthesis is a plausible solution to acquire more motion data. However, generating the action-conditioned, realistic, and diverse 3D human motions given the semantic action labels is still challenging because the mapping from semantic labels to real motion sequences is hard to depict. Previous work made some positive attempts like appending label tokens to pose encoding and performing action bias on latent space. However, how to synthesize diverse motions that accurately match the given label is still not fully explored. In this paper, we propose the Uncoupled-Modulation Conditional Variational AutoEncoder (UM-CVAE) to generate action-conditioned motions from scratch in an uncoupled manner. The main idea is twofold: (i) training an action-agnostic encoder to weaken the action-related information to learn the easy-modulated latent representation; (ii) strengthening the action-conditioned process with FiLM-based action-aware modulation. We conduct extensive experiments on the HumanAct12, UESTC, and BABEL datasets, demonstrating that our method achieves state-of-the-art performance both qualitatively and quantitatively with potential applications.
Visual Comfort Classification for Stereoscopic Videos Based on Two-Stream Recurrent Neural Network with Multi-level Attention

Due to the differences in visual systems between children and adults, a professional stereoscopic 3D video may not be comfortable for children. In this paper, we aim to answer whether a stereoscopic video is comfortable for children to watch by solving the visual comfort classification for stereoscopic videos. In particular, we propose a two-stream recurrent neural network (RNN) with multi-level attention for the visual comfort classification for stereoscopic videos. Firstly, we propose a two-stream RNN to extract and fuse spatial and temporal features from video frames and disparity maps. Furthermore, we propose using multi-level attention to effectively enhance the features in frame level, shot level, and finally video level. In addition, to our best knowledge, we establish the first high-definition stereoscopic 3D video dataset for performance evaluation. Experimental results show that our proposed model can effectively classify professional stereoscopic videos into visually comfortable for children or adults only.
Ceramic Type Recognition Algorithm Based on Ontology Modeling and Transfer Learning

Image classification is the key research in the field of computer vision. Based on real data, this paper adopts the method of image ontology modeling and image transfer learning. In this paper, the image knowledge is transferred to the experimental data, and the neural network is trained by knowledge verification. This paper proposes a ceramic type recognition algorithm based on ontology modeling and transfer learning(ICOT) for image classification. Experimental results show that the proposed algorithm is better than the traditional algorithm. This paper provides a general idea for similar problems.
A Robust Text Image Recognition Model with Domain Adaptation and Attention Mechanisms

Text Image Recognition (TIR) has recently achieved many re-markable successes with methods using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Among them, the encoder-decoder (ED) attention with RNN s outperforms the others. This attention-based model, however, learns sequentially along text-line directions as the characteristic of RNNs. As a result, parallelable learning of the model is reduced, and learning errors are accumulated. It is still struggling for recognizing long text-lines. In this paper, we propose a TIR model of multi-head self-attention (MSA)that does not rely on RNNs. During the training stage, the model learns parallelly the corresponding positions of characters in output sequences and visual features extracted by CNNs. Besides, we employ domain adaptation (DA) at character-level features for the proposed model so that it is robust to the various character styles in testing data. We perform experiments with English, Japanese, and Chinese text image datasets. The experimental results show that our proposed method increases the recognition rates by 10.01~15.76% on each dataset in comparison with the earliest RNN based method.
A Secure Authentication at Remote Real-Time Data Access in IWSN-Based Healthcare Environment

The fundamental concept in an IWSN-based Healthcare Environment is that doctors' devices can directly access the data from a patient's sensor in real-time. Unfortunately, wireless-based communication between the doctor's device and the patient's sensor is vulnerable to attacks such as impersonation, tracking, DoS, and cloning attacks. Therefore, this article tries to resolve the security problems in an IWSN-based Healthcare Environment by developing a secure authentication protocol using biometric and PUF -based. In addition, the informal use of solid reasoning analysis and formal analysis using the scyther tool proves that our protocol achieves security features and withstanding well-known attacks.
Towards Sparsified Federated Neuroimaging Models via Weight Pruning

Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model pruning techniques have been designed in centralized settings to reduce inference times. Combining centralized pruning techniques with federated training seems intuitive for reducing communication costs—by pruning the model parameters right before the communication step. Moreover, such a progressive model pruning approach during training can also reduce training times/costs. To this end, we propose FedSparsify, which performs model pruning during federated training. In our experiments in centralized and federated settings on the brain age prediction task (estimating a person’s age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions. One surprising benefit of model pruning is improved model privacy. We demonstrate that models with high sparsity are less susceptible to membership inference attacks, a type of privacy attack.
Precision Public Health and the Role of mHealth: The Use of Smartphone Applications Worldwide in Mitigating the COVID-19 Pandemic and Their Integration as Components of Public Health Policies. A Focus on the French Example

The COVID-19 pandemic was an opportunity to observe how, in practice, the means of precision public health could materialize, be mobilized, and with what results. Among the various measures required to control the pandemic, we can highlight the use of smartphones and applications, in particular, contact tracing applications. Their deployment has been almost synchronous all over the world, and their use called for by the public sector as well as the private sector. It is an innovation that has not encountered major problems in terms of regulation, in part due to exceptional regimes justified by the pandemic, such as a state of a health emergency. However, privacy issues do exist, as do issues relating to the social implementation of these services. Here, we examine in several countries what has been the place of the use of smartphones in the context of the COVID-19 pandemic, and what seems to have been the use of it as a prototype on a global scale of a precision public health tool. A particular focus is made on Europe and France more specifically.
Effectiveness of 3D PCD Reconstruction using Orthogonal Multiple Sonar Fusion on Frame and Surface type Objects

Recent development of orthogonally-oriented multi-beam sonars fusion (OMSF) has enabled effective reconstruction of underwater 3D point cloud data (PCD) using commonly available acoustic imaging sonars. However, its effectiveness on various object shapes has not been carefully investigated. Addressing such gap is important as underwater structures have varying and complex configurations.This paper investigates the effectiveness of OMSF on three common types of shapes encountered underwater: flat ‘hollow’ frames, flat ‘solid’ surfaces, and curved ‘solid’ surfaces. In-depth analysis of OMSF indicates that its interpolation process tends to fit estimated PCD points onto large surface shapes, leading its PCD results to be more accurate on objects with solid surfaces and not as effective on hollow objects with frames. This analysis is supported by simulation and in-field experiments, with in-field results showing OMSF obtaining up to 60% higher reconstruction accuracy on flat solid surfaces compared to frames with similar outer dimensions.Overall, the analysis and results in this paper shows a limitation of OMSF method for frame-like structures.
The Comparison of Energy Consumption from Clay, Inorganic-waste, and Light Brick as Wall Material

This study aims to compare energy consumption of clay brick, light brick, Styrofoam-based brick and ecobrick production as wall material. The increase of materials needed on construction development in Indonesia become one of the reasons why sustainable architecture through the renewable material needs to be further considered from the perspective of life cycle. The result shows after all materials implemented in units of 1 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , clay brick precisely has the highest energy consumption with amount of 129.5 MJ/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and ecobrick has the lowest energy consumption with amount of 0.012 MJ/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . While light brick and styrofoam brick have a slightly different value. Energy consumption of light brick is about 51.6 MJ/m2 and Styrofoam-based brick is about 52.5 MJ/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . When viewed from the potential for inorganic waste recycling, clay bricks and light bricks have absolutely no potential to recycle inorganic waste while Styrofoam brick could recycle about 49 Kg/m2 of inorganic waste and ecobrick could recycle about 36 Kg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of inorganic waste.
Masked Face Detection using Lightweight Deep Learning based Model for IoT based Healthcare applications

One of the best measures to enforce in epidemiological scenarios, such as the present COVID-19 epidemic, is the usage of masks. For a while, this will be a regular part of life, notably in public places. In order to deal with these unusual circumstances where people who wear mask are being watched, there is a need for an effective face identification technology. In order to precisely identify people wearing masks, we provide a deep learning algorithm based on YOLO architecture in this study. Unlike traditional CNNs, the proposed system uses a convergence layer to record numerous facial emotions while also using a number of convolutional filters to construct the faces for masked images. The presented design has numerous layers, including convolutional, max pooling, dropout, and softmax, and is both straightforward and effective. On the publicly accessible Real-World Masked Face Dataset, we assess the effectiveness of masked-faces detection (RWMFD). The investigational outcomes demonstrate an accurateness of 99.9%, demonstrating the effectiveness of our proposed methodology in classifying individuals wanting to wear facemasks.
Intelligent Face Recognition based attendance system

Student consistent performance is a challenge for educational institutions worldwide, especially in India. Inadequate attendance is one of the prime reasons that is often correlated with this drop in student performance. The method generally employed in academic institutions to register attendance is the manual record method by either signing or calling out the pupil's name. This takes a long time and is inefficient, e.g., missing out on names and proxy attendances. Professors are increasingly relying on a computer-based student attendance verification system to help them keep track of attendance. Our project deploys a facial recognition-based attendance system. Automated facial recognition systems have made tremendous improvements in today's environment. We found out that Real-Time Face Recognition is an appropriate system for recording and tracking daily attendance. This type of attendance system involves recording the attendance using high-definition (HD) monitor video and related technologies for facial recognition of students. Our facial recognition system does this by analyzing the image captured by the security cameras. Although we employed a range of algorithms and software to achieve this, the primary concept to be used here is Deep Learning. It facilitates the conversion of video frames to photographs, allowing the student's face to be easily recognized for attendance purposes and the attendance database to be automatically updated.
A Semantic-Based Approach for Keyphrase Extraction from Vietnamese Documents Using Thematic Vector

Keyphrase extraction plays an important role in many applications of Natural Language Processing. There are many effective proposals for English, but those approaches are not completely applicable for low resources languages such as Vietnamese. In this paper, we propose a Semantic-based Approach for Keyphrase Extraction (SAKE), which improved the TextRank algorithm [1]. In SAKE, we apply semantic to the phrases and incorporates the semantic to the ranking process. Technically, a document is represented as a graph, in which vertices are words and edges are relations among words. In each document, we get a representative thematic vector by computing the average of word embedding vectors. Each vertex has a similarity score to the thematic vector and this score will be involved to the scoring in the ranking process. The important vertices are highly weighted not only by their relationships to other vertices but also by the similarity to the document theme. We experimented our proposed method on Vietnamese news articles. The result shows that our SAKE improved TextRank for Vietnamese text by achieving 1.8% higher of F1-score.
Pose estimation and tracking for sign language recognition

In this work we combine several state-of-the-art algorithms into a single pose tracking pipeline.We recognize motion, relative position and pose of human hands and face as the main information carriers as we separate the problem into three main tasks: hand and face detection, hand and face pose estimation and tracking hands and face in dynamic environment.We also propose to use measurements from pose estimation steps as a visual information indicator to solve dynamic tracking assignment problem.