Reviewerly/exHarmony huggingface dataset:
Label Estimation of Data Using the Modified Fisher Criterion

In supervised learning, the amount of work required to assign labels to data becomes enormous as the input data increases. To reduce this work, we propose a method for label estimation using only a small amount of labeled data. The SVM separation hyperplane is moved by iterating label assignment and classification, and the results are evaluated using a new cluster evaluation criterion. This criterion is based on the Fisher criterion used for linearly separable data and applied to nonlinear data. Some semi-supervised learning methods, which are related to the proposed method, are also explained. We show that the proposed method is effective for linearly inseparable data through experiments. In addition, we compared the performance of the proposed method with some semi-supervised learning methods. The contributions of this study are the classification of data in situations where labeled data is scarce and the development of new criteria for evaluating non-linear classification results.
Deep Reinforcement Learning for Object Detection with the Updatable Target Network

We propose a reinforcement learning method for setting up the game of detecting objects within an image. Unlike some traditional image detection methods, which produce a large number of candidate boxes to detect objects, our approach allows an agent to use a small set of image locations to detect a visual object effectively. We train the agent to identify the useless information in the four edges of the image and discard them by representing predefined candidate areas in a tree-like hierarchy. Such a series of procedures that build the game environment is designed to detect locations of target objects. We also show that the updatable target network can make the agent reach stability faster and improve training results prominently, which utilizes good samples. Extensive comparison experiments on the benchmark dataset of Pascal VOC verify the outperformance of the proposed method.
Source Data-less Efficient Transfer Learning Based on Layer Importance Index Using Activated Feature Map

Transfer learning performance in convolutional neural networks (CNNs) depends on the selection of retraining and fixation layers. The conventional method requires source data, which increases the cost proportionally to the data’s size. To solve this problem, this study proposed a source-data-less layer importance index that indicates the layers that should be retrained. Consequently, using the maximum activated feature maps generated by the model itself instead of the activated feature maps, the proposed method eliminates the need for any source data. Further, the experimental results on the five image classification datasets demonstrated a strong positive correlation of the proposed layer importance index compared with the conventional method. In addition, the transfer learning method using the layer importance index significantly improved the average classification accuracy compared with fine-tuning. CNNs have become indispensable owing to various applications, and thus, this study aimed at further enhancing its capabilities is relevant to the present and coming times.
Bootstrapping IoT authentication using aggregated local knowledge and novel self-contained triangulation methodologies

We discuss the theoretical model underlying 2BPA (two-band peer authentication), a practical alternative to conventional authentication of entities and data in IoT. In essence this involves assembling a virtual map of authentication assets in the network, typically leading to many paths of confirmation between any pair of entities. This map is continuously updated, confirmed and evaluated. The value of authentication along multiple disjoint paths becomes very clear. We discover that if an attacker wants to make an honest node falsely believe she has authenticated another then the length of the authentication paths is of little importance.
Improvement of Unconstrained Appearance-Based Gaze Tracking with LSTM

Gaze tracking is not only an important research direction in computer vision but also an important non-verbal clue in human life. What is important is that the direction of gaze can be used as a reference for judging a person’s intentions. In order to improve the accuracy of predicting gaze direction, a model of 3D gaze tracking based on bidirectional Long Short-Term Memory (LSTM) is proposed in this paper. The backbone network of the model is ResNet and its variants. The output of the model is the angular error of gaze direction. To improve the accuracy of the model prediction, the attention mechanism is adopted in this work. The ablation experiments are conducted on the selected Gaze360, which is a dataset with sufficiently large and diverse data. The angular error of the proposed model decreases from 13.5° to 12.6°.
A Representation Learning Method of Knowledge Graph Integrating Ordered Relation Path and Entity Description Information

Knowledge graph representation learning aims to obtain its vector representation by mapping entities and relations in knowledge graphs to a continuous low-dimensional vector space by learning methods. Most of the existing knowledge graph representation learning methods only consider the single-step relation between entities from the perspective of triples and fail to effectively utilize important information such as ordered multi-step relation paths and entity descriptions, thus affecting the ability of knowledge representation learning. We propose a knowledge graph representation learning model that integrates ordered relation paths and entity descriptions in response to the above problems. The model can integrate the triple representation in the knowledge graph, the semantic representation of entity description, and the representation of ordered relation paths for training. On the FB15K, WN18, FB15K-237, and WN18RR datasets, the proposed model and baselines are run on the link prediction task. Experimental results show that the model has higher accuracy than existing baselines, demonstrating the effectiveness and superiority of the method.
Non-Standard Vietnamese Word Detection and Normalization for Text–to–Speech

Converting written texts into their spoken forms is an essential problem in any text-to-speech (TTS) systems. However, building an effective text normalization solution for a real-world TTS system face two main challenges: (1) the semantic ambiguity of non-standard words (NSWs), e.g., numbers, dates, ranges, scores, abbreviations, and (2) transforming NSWs into pronounceable syllables, such as URL, email address, hashtag, and contact name. In this paper, we propose a new two-phase normalization approach to deal with these challenges. First, a model-based tagger is designed to detect NSWs. Then, depending on NSW types, a rule-based normalizer expands those NSWs into their final verbal forms. We conducted three empirical experiments for NSW detection using Conditional Random Fields (CRFs), BiLSTM-CNN-CRF, and BERT-BiGRU-CRF models on a manually annotated dataset including 5819 sentences extracted from Vietnamese news articles. In the second phase, we propose a forward lexicon-based maximum matching algorithm to split down the hashtag, email, URL, and contact name. The experimental results of the tagging phase show that the average F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> scores of the BiLSTM-CNN-CRF and CRF models are above 90.00%, reaching the highest F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> of 95.00% with the BERT-BiGRU-CRF model. Overall, our approach has low sentence error rates, at 8.15% with CRF and 7.11% with BiLSTM-CNNCRF taggers, and only 6.67% with BERT-BiGRU-CRF tagger.
Learning and Estimation of Latent Structural Models Based on between-Data Metrics

With the development of information technology, a wide variety of data have been accumulated, and there are many methods for analyzing such data. In this study, we model the input data and the metrics between the data based on the assumption that each metric is generated from a continuous latent variable. Specifically, we assume that the input data are generated using low-dimensional latent variables and their projection matrices. We describe a method for estimating the latent variables. Because the generative model defined in this study cannot obtain the Q function analytically, we use the Monte Carlo EM algorithm to approximate the Q function and investigate an efficient parameter estimation method. Experiments using artificial data and the 20 newsgroups dataset demonstrate the effectiveness of the proposed method.
Computational Complexity Analysis of Ant Colony Clustering Algorithms: Application to Student’s Grouping Problem

The task of assessing, grouping and arranging data into meaningful groups or clusters based on their similarities/dissimilarities measures known as cluster analysis. Thereby, there are numerous clustering algorithms: hierarchical and partitional. In the last decade, clustering using bio-inspired algorithms received more attention, specifically the ant clustering algorithms. Regardless, they have required a lot of processing power due to the massive amount of data that has been generated during the last years. As a consequence, determining the computational cost of these algorithms is one of the most interesting tasks in the quest for optimal clustering solutions in a real-time system. This study presents a research guide for the researchers working in the same field. A series of experiments are elaborated to investigate the computational complexity of the most promising algorithms applied to students grouping problem. The results indicate two challenges that arise when using ant clustering algorithms: the difficulty in adjusting parameters and extended computation time.
Towards QoE Management for Post-Pandemic Online Learning : Invited Paper

During the Covid-19 pandemic, most schools had to adopt online learning, which is a special kind of e-learning that provides a virtual classroom via a live session for both teachers and learners. However, studies on education in the Covid-19 pandemic shows that there should be more efforts from researchers as well as governments to effectively support learners. In this paper, we focus on the problem of Quality of Experience in online learning. We discuss the enabling technologies of online learning. Also, we make an extensive review of QoE in video streaming, the key enabling technology of online leaning. Finally, the key challenges and potential solutions of QoE management for future online learning will be discussed.
PassAugment: Pass Nodes Importance in Graph Data Augmentation for Graph Classification

Data augmentation has been widely introduced into graph-based tasks to improve the generalizability of models. Based on empirical hypothesis, we show that the node in a graph has different importance, the important one is critical for classification task while the unimportant one hurts the performance. However, there are few works in data augmentation addressing the information propagation of nodes with different importance. In this work, we propose a novel graph data augmentation algorithm for graph classification task, called PassAugment, aiming to pass these importance in graph data augmentation. After distinguishing the importance of all nodes in each graph using the saliency map, we design a data augmentation approach including two strategies: (i) randomly adding edges between the important nodes and the other nodes to globally improve the effective information passing, and (ii) randomly removing edges between the unimportant nodes and their neighbors to locally reduce the ineffective information passing. More importantly, our proposed approach as a standalone module can be combined with many GNNs architectures. Experimental results on graph classification task show that our approach consistently improves the accuracy and achieves or closely matches the state-of-the-art performance.
Recognition of brain stroke shape using multiscale morphological image processing

Brain haemorrhage is a form of stroke caused by the inflation in the cerebral artery ensuing confined bleeding in the inherent tissues. The MRI images (T1, T2 and FLAIR (fluid-attenuated inversion recovery)) are extracted from Siemen 3T scanner in which the ordinary and haemorrhage-affected strokes are effectively emphasized in this work. The existing such as CNN (convolutional neural network), residual CNN and MCDNN (mobile-cloud deep neural network) are compared from the perspective of PSNR (peak signal-to-noise ratio), SSIM (structural similarity index measure) and MSE (mean squared error), respectively. The PSNR value of this proposed MMNN (multiple nonotonic neural network) is increased by 0.23%, 0.087% and 0.613% compared to CNN, Residual CNN and MCDNN techniques, respectively. The SSIM value is increased by 0.57%, 0.322% and 0.027% compared to CNN, Residual CNN and MCDNN. MSE value is decreased by 8.93%, 2.1457% and 0.316% compared to CNN, Residual CNN and MCDNN, respectively.
Annotation computationnelle des réseaux endophoriques dans les textes traduits : corpus d'apprenants en traduction

The aim of this paper is to present the corpus of non-literary texts translated from Spanish into Czech; the translations being elaborated by our translation students.We propose to evaluate the functionalities of two computational tools (Analec and Sketch Engine) in the field of visualization and annotation of coreference elements in translated texts.The electronic corpus allows us to contrast the configurations of the coreference chains in the source and target texts and to analyse subsequently how the translator learners perceive and translate the relationships between the elements of these chains.
UFTDRDH: A novel user-oriented solution for full-text database retrieval in Chinese digital humanities

Information technology boosts the development of database retrieval in the Chinese digital humanities domain. However, most database providers adopt a system-oriented design pattern, which fails to handle the problem of query gaps in users’ retrieval process. This issue seriously hinders the effective use of database retrieval functionalities, peculiarly among those historical and humanities researchers. To address it, we propose UFTDRDH, a novel user-oriented solution based on automatic query formulation (AQF) technologies, which integrates a human–machine interactive module for the selection of new query-related expansion terms and a powerful query expansion algorithmic component (UFTDRDH-QEV) optimised by a topic-enhancing relevance feedback model approach (ToQE). To verify the effectiveness of UFTDRDH, several comparative experiments are conducted, including quantitative evaluation for retrieval efficiency and user satisfaction, as well as qualitative studies for interpretative traceability. The empirical results are multidimensional and robust, which not only shows the positive effects of different AQFs on gap reduction, especially the importance of query expansion as the most effective technology, but also underlines the remarkably advantageous performance of UFTDRDH compared with traditional system-oriented automatic query expansion in different task contexts. We believe the application of UFTDRDH can further strengthen the research focus on user-centred design and improve the level of current full-text database retrieval in the field of Chinese digital humanities. Broadly speaking, this solution can be also extended to the full-text database retrieval in other languages and digital humanities domains.
Contactless Lift System using Vision Counter and Speech Command Recognition

The current pandemic has brought about change to every aspect of our lives in the recent years. Adaptation to the pandemic demands change to ensure less congestion in public places and more hygienic operation of facilities in public. Lifts are no exception to the changed norms because they are found in multi-storey buildings in the hearts of busy cities. With hundreds of passengers using a lift every day, the buttons of the lift will carry a potential risk, as well as the environment of the lift. The proposed project aims to change the method of operation of lifts making it operate without physical key presses of lift buttons. The project proposes a new method of lift system using vision counter system and speech command recognition. The system operates the lift by interpreting speech commands which are to be used to control the lift. The method of speech recognition used in this work, utilizes artificial neural networks. The model used for the prediction is a Recurrent neural network-based model, that is trained on a dataset by Speech Commands v0.02. The people counter in this report performs image processing on camera live feed to detect movement of passengers in and out of the lift. The smart counter system is followed by object detection algorithm using a MobileNetCaffe model for the purpose of confirmation of the number, by identifying the human features in the detected images.
Tracking of Hardware Development Schedule based on Software Effort Estimation

Accurately predicting the time required for tasks in the development process can effectively manage resources and costs, which is crucial in project management. In 1960, Farr [3] and Nelson [6] proposed the concept of software effort estimation. Early research focused on building standardized estimation models to estimate the number of hours worked to complete tasks through statistical regression analysis or expert rules of thumb. Later, machine learning and deep learning were used to train models to estimate working hours to replace traditional estimation methods. This study proposes that software effort estimation can be extended to the hardware development industry. We use machine learning and deep learning to estimate the time required for tasks in the hardware development process and then accurately manage the product development time. This research uses semantic analysis to extract the keywords of the problems in the development process through NLP and use them as features for afterward analysis. We compare the accuracy, MMRE, and PRED(25) of the four models of machine learning's decision tree, random forest, XGBoost, and deep learning's RNN model in estimating the time required for tasks. The experimental results show that the decision tree has higher accuracy than the other three models. This study proves that the software effort estimation technique can be applied to task tracking in the hardware development process.
RU-Net: Solar Panel Detection From Remote Sensing Image

With increasing impact of global climate change, huge efforts are needed to reduce greenhouse gas emissions. The rooftop solar panels installation is one of the mechanism. In this paper, we focus on distribution and deployment degree of rooftop solar panels, and identify locations and total surface area of solar panels within a given geographic area in tackling the climate change. A comprehensive database of the location of solar panels on rooftops is important to assist analysts and policymakers in defining strategies for further expansion of solar energy. The deep learning method was used for the detection of solar panel location and their surface using the aerial imagery. While focusing on light weight image segmentation and low-resolution images, we proposed a two-branch solar panel detection framework consisting of classifier and segmentation branch, which was trained using the public data set of remote sensing images. This work provided an efficient and scalable method to detect solar panels, achieving an area under the curve (AUC) of 0.97 for classification and intersection over union (IOU) score of 0.84 for segmentation performance.
StegEdge: Privacy protection of unknown sensitive attributes in edge intelligence via deception

Due to the limited capabilities of user devices, such as smart phones, and the Internet of Things (IoT), edge intelligence is being recognized as a promising paradigm to enable effective analysis of the data generated by these devices with complex artificial intelligence (AI) models, and it often entails either fully or partially offloading the computation of neural networks from user devices to edge computing servers. To protect users’ data privacy in the process, most existing researches assume that the private (sensitive) attributes of user data are known in advance when designing privacy-protection measures. This assumption is restrictive in real life, and thus limits the application of these methods. Inspired by the research in image steganography and cyber deception, in this paper, we propose StegEdge, a conceptually novel approach to this challenge. StegEdge takes as input the user-generated image and a randomly selected “cover” image that does not pose any privacy concern (e.g., downloaded from the Internet), and extracts the features such that the utility tasks can still be conducted by the edge computing servers, while potential adversaries seeking to reconstruct/recover the original user data or analyze sensitive attributes from the extracted features sent from users to the server, will largely acquire information of the cover image. Thus, users’ data privacy is protected via a form of deception. Empirical results conducted on the CelebA and ImageNet datasets show that, at the same level of accuracy for utility tasks, StegEdge reduces the adversaries’ accuracy of predicting sensitive attributes by up to 38% compared with other methods, while also defending against adversaries seeking to reconstruct user data from the extracted features.
An efficient cross-domain few-shot website fingerprinting attack with Brownian distance covariance

Website fingerprinting (WF) attacks can infer the specific websites from Tor encrypted traffic. However, most existing WF attacks require a large number of training samples (e.g., hundreds) per website to achieve the desirable accuracy. To lessen the training data scale, the low-data WF (LDWF) attacks are proposed. Whereas existing LDWF attacks generally require gathering an auxiliary dataset, which increases the bootstrap time to launch a WF attack. To address this limitation, we present the cross-domain LDWF attack problem and put forward the first cross-domain LDWF attack, namely WFBDC (WF with Brownian Distance Covariance), which can use a historical gathered dataset to be the auxiliary dataset. The primary advantage of WFBDC is the introduction of the BDC metric to measure the similarity between two samples. The key to BDC lies in that it defines the similarity by measuring the discrepancy between joint characteristic functions of embedded features and the product of the marginals. To mitigate the domain deviation, the transfer learning and multi-similarity loss techniques are also adopted. We conduct 10 experiments based on 15 datasets to evaluate the performance and efficiency of WFBDC. Evaluation results show that WFBDC can improve the performance of the state-of-the art LDWF attacks by up to 9% and 19% in the closed-world and open-world scenarios, respectively. Meanwhile, WFBDC can significantly reduce the pre-training time of existing LDWF attacks.