Reviewerly/exHarmony huggingface dataset:
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition

We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot.Our information-acquisitionoriented dialogue system employs a novel adaptation of reinforced self-play so that the system can be transferred to various domains without in-domain dialogue data, and can carry out conversations both informative and attentive to users.Our extensive subjective and objective evaluations on three large public data corpora demonstrate the effectiveness of our system to deliver knowledge-intensive and attentive conversations and help end users substantially gain knowledge without reading passages.Our code and datasets are publicly available 1 for follow-up research.
A Novel Lightweight Anonymous Proxy Traffic Detection Method Based on Spatio-Temporal Features

Anonymous proxies are used by criminals for illegal network activities due to their anonymity, such as data theft and cyber attacks. Therefore, anonymous proxy traffic detection is very essential for network security. In recent years, detection based on deep learning has become a hot research topic, since deep learning can automatically extract and select traffic features. To make (heterogeneous) network traffic adapt to the homogeneous input of typical deep learning algorithms, a major branch of existing studies convert network traffic into images for detection. However, such studies are commonly subject to the limitation of large-sized image representation of network traffic, resulting in very large storage and computational resource overhead. To address this limitation, a novel method for anonymous proxy traffic detection is proposed. The method is one of the solutions to reduce storage and computational resource overhead. Specifically, it converts the sequences of the size and inter-arrival time of the first N packets of a flow into images, and then categorizes the converted images using the one-dimensional convolutional neural network. Both proprietary and public datasets are used to validate the proposed method. The experimental results show that the converted images of the method are at least 90% smaller than that of existing image-based deep learning methods. With substantially smaller image sizes, the method can still achieve F1 scores up to 98.51% in Shadowsocks traffic detection and 99.8% in VPN traffic detection.
Semi-Supervised Hierarchical Graph Classification

Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world entity, e.g., a user in a social network, or a document in a document citation network. In this work, we consider a more challenging but practically useful setting, in which a node itself is a graph instance. This leads to a hierarchical graph perspective which arises in many domains such as social network, biological network and document collection. We study the node classification problem in the hierarchical graph where a "node" is a graph instance. As labels are usually limited, we design a novel semi-supervised solution named SEAL-CI. SEAL-CI adopts an iterative framework that takes turns to update two modules, one working at the graph instance level and the other at the hierarchical graph level. To enforce a consistency among different levels of hierarchical graph, we propose the Hierarchical Graph Mutual Information (HGMI) and further present a way to compute HGMI with theoretical guarantee. We demonstrate the effectiveness of this hierarchical graph modeling and the proposed SEAL-CI method on text and social network data.
Image restoration via exponential scale mixture‐based simultaneous sparse prior

Image prior plays a decisive role in the performance of widely studied model-based restoration methods. To further improve restoration performance, this paper proposes an exponential scale mixture-based simultaneous sparse prior (ESM-SSP) to accurately characterize image prior information. Specifically, first, two structured dictionaries are adaptively learned to explore the local and non-local sparsity of similar patch groups simultaneously. Then, the exponential scale mixture (ESM) is employed to model simultaneous sparse coefficients. The adoption of ESM enables us to accurately estimate simultaneous sparse coefficients by adaptively adjusting the regularization parameters. With the aid of ESM-SSP, an effective image restoration algorithm is developed to preserve more image details. Extensive experimental results on image denoising and deblocking demonstrate that compared with many state-of-the-art model-based methods, the proposed ESM-SSP-based restoration algorithm not only has competitive peak signal-to-noise ratio, but also produces higher structural similarity index and better visuals. More importantly, the proposed method can also compete favourably with the superior deep learning-based restoration methods.
Building towards Automated Cyberbullying Detection: A Comparative Analysis

The increased use of social media among digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data preprocessing, and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension leads us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using self-supervised learning (SSL) as an annotation technique for cyberbullying detection are explored.
End-to-End Text-to-Speech Based on Latent Representation of Speaking Styles Using Spontaneous Dialogue

The recent text-to-speech (TTS) has achieved quality comparable to that of humans; however, its application in spoken dialogue has not been widely studied. This study aims to realize a TTS that closely resembles human dialogue. First, we record and transcribe actual spontaneous dialogues. Then, the proposed dialogue TTS is trained in two stages: first stage, variational autoencoder (VAE)-VITS or Gaussian mixture variational autoencoder (GMVAE)-VITS is trained, which introduces an utterance-level latent variable into variational inference with adversarial learning for end-to-end text-to-speech (VITS), a recently proposed end-to-end TTS model. A style encoder that extracts a latent speaking style representation from speech is trained jointly with TTS. In the second stage, a style predictor is trained to predict the speaking style to be synthesized from dialogue history. During inference, by passing the speaking style representation predicted by the style predictor to VAE/GMVAE-VITS, speech can be synthesized in a style appropriate to the context of the dialogue. Subjective evaluation results demonstrate that the proposed method outperforms the original VITS in terms of dialogue-level naturalness.
Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding

Weakly supervised Referring Expression Grounding (REG) aims to ground a particular target in an image described by a language expression while lacking the correspondence between target and expression. Two main problems exist in weakly supervised REG. First, the lack of region-level annotations introduces ambiguities between proposals and queries. Second, most previous weakly supervised REG methods ignore the discriminative location and context of the referent, causing difficulties in distinguishing the target from other same-category objects. To address the above challenges, we design an entity-enhanced adaptive reconstruction network (EARN). Specifically, EARN includes three modules: entity enhancement, adaptive grounding, and collaborative reconstruction. In entity enhancement, we calculate semantic similarity as supervision to select the candidate proposals. Adaptive grounding calculates the ranking score of candidate proposals upon subject, location and context with hierarchical attention. Collaborative reconstruction measures the ranking result from three perspectives: adaptive reconstruction, language reconstruction and attribute classification. The adaptive mechanism helps to alleviate the variance of different referring expressions. Experiments on five datasets show EARN outperforms existing state-of-the-art methods. Qualitative results demonstrate that the proposed EARN can better handle the situation where multiple objects of a particular category are situated together.
Natural language processing and discourse complexity studies

The study presents an overview of discursive complexology, an integral paradigm of linguistics, cognitive studies and computer linguistics aimed at defining discourse complexity. The article comprises three main parts, which successively outline views on the category of linguistic complexity, history of discursive complexology and modern methods of text complexity assessment. Distinguishing the concepts of linguistic complexity, text and discourse complexity, we recognize an absolute nature of text complexity assessment and relative nature of discourse complexity, determined by linguistic and cognitive abilities of a recipient. Founded in the 19th century, text complexity theory is still focused on defining and validating complexity predictors and criteria for text perception difficulty. We briefly characterize the five previous stages of discursive complexology: formative, classical, period of closed tests, constructive-cognitive and period of natural language processing. We also present the theoretical foundations of Coh-Metrix, an automatic analyzer, based on a five-level cognitive model of perception. Computing not only lexical and syntactic parameters, but also text level parameters, situational models and rhetorical structures, Coh-Metrix provides a high level of accuracy of discourse complexity assessment. We also show the benefits of natural language processing models and a wide range of application areas of text profilers and digital platforms such as LEXILE and ReaderBench. We view parametrization and development of complexity matrix of texts of various genres as the nearest prospect for the development of discursive complexology which may enable a higher accuracy of inter- and intra-linguistic contrastive studies, as well as automating selection and modification of texts for various pragmatic purposes.
Asymmetric image encryption algorithm based on a new three-dimensional improved logistic chaotic map

Based on some analyses of existing chaotic image encryption frameworks and a new designed three-dimensional improved logistic chaotic map (3D-ILM), an asymmetric image encryption algorithm using public-key Rivest–Shamir–Adleman (RSA) is presented in this paper. In the first stage, a new 3D-ILM is proposed to enhance the chaotic behavior considering analysis of time sequence, Lyapunov exponent, and Shannon entropy. In the second stage, combined with the public key RSA algorithm, a new key acquisition mathematical model (MKA) is constructed to obtain the initial keys for the 3D-ILM. Consequently, the key stream can be produced depending on the plain image for a higher security. Moreover, a novel process model (NPM) for the input of the 3D-ILM is built, which is built to improve the distribution uniformity of the chaotic sequence. In the third stage, to encrypt the plain image, a pre-process by exclusive OR (XOR) operation with a random matrix is applied. Then, the pre-processed image is performed by a permutation for rows, a downward modulo function for adjacent pixels, a permutation for columns, a forward direction XOR addition-modulo diffusion, and a backward direction XOR addition-modulo diffusion to achieve the final cipher image. Moreover, experiments show that the the proposed algorithm has a better performance. Especially, the number of pixels change rate (NPCR) is close to ideal case 99.6094%, with the unified average changing intensity (UACI) close to 33.4634%, and the information entropy (IE) close to 8.
Linearity-Aware Subspace Clustering

Obtaining a good similarity matrix is extremely important in subspace clustering. Current state-of-the-art methods learn the similarity matrix through self-expressive strategy. However, these methods directly adopt original samples as a set of basis to represent itself linearly. It is difficult to accurately describe the linear relation between samples in the real-world applications, and thus is hard to find an ideal similarity matrix. To better represent the linear relation of samples, we present a subspace clustering model, Linearity-Aware Subspace Clustering (LASC), which can consciously learn the similarity matrix by employing a linearity-aware metric. This is a new subspace clustering method that combines metric learning and subspace clustering into a joint learning framework. In our model, we first utilize the self-expressive strategy to obtain an initial subspace structure and discover a low-dimensional representation of the original data. Subsequently, we use the proposed metric to learn an intrinsic similarity matrix with linearity-aware on the obtained subspace. Based on such a learned similarity matrix, the inter-cluster distance becomes larger than the intra-cluster distances, and thus successfully obtaining a good subspace cluster result. In addition, to enrich the similarity matrix with more consistent knowledge, we adopt a collaborative learning strategy for self-expressive subspace learning and linearity-aware subspace learning. Moreover, we provide detailed mathematical analysis to show that the metric can properly characterize the linear correlation between samples.
Sentiment and Emotion-Aware Multi-Modal Complaint Identification

The expression of displeasure on a consumer's behalf towards an organization, product, or event is denoted via the speech act known as complaint. Customers typically post reviews on retail websites and various social media platforms about the products or services they purchase, and the reviews may include complaints about the products or services. Automatic detection of consumers' complaints about items or services they buy can be critical for organizations and online merchants since they can use this insight to meet the customers' requirements, including handling and addressing the complaints. Previous studies on Complaint Identification (CI) are limited to text. Images posted with the reviews can provide cues to identify complaints better, thus emphasizing the importance of incorporating multi-modal inputs into the process. Furthermore, the customer's emotional state significantly impacts the complaint expression since emotions generally influence any speech act. As a result, the impact of emotion and sentiment on automatic complaint identification must also be investigated. One of the major contributions of this work is the creation of a new dataset- Complaint, Emotion, and Sentiment Annotated Multi-modal Amazon Reviews Dataset (CESAMARD), a collection of opinionated texts (reviews) and images of the products posted on the website of the retail giant Amazon. We present an attention-based multi-modal, adversarial multi-task deep neural network model for complaint detection to demonstrate the utility of the multi-modal dataset. Experimental results indicate that the multi-modality and multi-tasking complaint identification outperforms uni-modal and single-task variants.
SJDL-Vehicle: Semi-supervised Joint Defogging Learning for Foggy Vehicle Re-identification

Vehicle re-identification (ReID) has attracted considerable attention in computer vision. Although several methods have been proposed to achieve state-of-the-art performance on this topic, re-identifying vehicle in foggy scenes remains a great challenge due to the degradation of visibility. To our knowledge, this problem is still not well-addressed so far. In this paper, to address this problem, we propose a novel training framework called Semi-supervised Joint Defogging Learning (SJDL) framework. First, the fog removal branch and the re-identification branch are integrated to perform simultaneous training. With the collaborative training scheme, defogged features generated by the defogging branch from input images can be shared to learn better representation for the re-identification branch. However, since the fog-free image of real-world data is intractable, this architecture can only be trained on the synthetic data, which may cause the domain gap problem between real-world and synthetic scenarios. To solve this problem, we design a semi-supervised defogging training scheme that can train two kinds of data alternatively in each iteration. Due to the lack of a dataset specialized for vehicle ReID in the foggy weather, we construct a dataset called FVRID which consists of real-world and synthetic foggy images to train and evaluate the performance. Experimental results show that the proposed method is effective and outperforms other existing vehicle ReID methods in the foggy weather. The code and dataset are available in https://github.com/Cihsaing/SJDL-Foggy-Vehicle-Re-Identification--AAAI2022.
Rethinking Digital Competencies in the New Curriculum for Wales

We introduce the major changes to computer science (CS) education in Wales, UK, as part of wider school-level (K-12) curriculum, qualifications and system-level reforms. The new Curriculum for Wales (Cwricwlwm i Gymru), starting from September 2022, reinforces the societal importance of digital competence as a statutory cross-curricular skill alongside literacy and numeracy, for all learners aged 3-16. It also offers significant changes to the discipline of CS, as part of a new interdisciplinary Science & Technology "area of learning and experience". We introduce the high-level rationale and characteristics of this new bilingual national curriculum. Finally, we consider what this means for school-level digital competence and CS education, and especially how it can influence digital pedagogy and practice in Wales as the new curriculum is implemented.
Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs

Label assignment plays a significant role in modern object detection models. Detection models may yield totally different performances with different label assignment strategies. For anchor-based detection models, the IoU (Intersection over Union) threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold. Early object detectors simply utilize the fixed threshold for all training samples, while recent detection algorithms focus on adaptive thresholds based on the distribution of the IoUs to the ground truth boxes. In this paper, we introduce a simple while effective approach to perform label assignment dynamically based on the training status with predictions. By introducing the predictions in label assignment, more high-quality samples with higher IoUs to the ground truth objects are selected as the positive samples, which could reduce the discrepancy between the classification scores and the IoU scores, and generate more high-quality boundary boxes. Our approach shows improvements in the performance of the detection models with the adaptive label assignment algorithm and lower bounding box losses for those positive samples, indicating more samples with higher-quality predicted boxes are selected as positives.
DropAR: Enriching Exergaming Using Collaborative Augmented Reality Content

Exergames are video games that require physical movement to play with AR presenting many opportunities for promoting physical activities as it can be used to engage players and encourage exploration. Multiplayer Augmented Reality (AR) games allow players to occupy a shared physical environment populated with interactive digital 3D models. We propose the use of an AR exergame whereby players unlock and place 3D models while walking. We have designed, implemented, and tested a casual exergame that creates innovative collaborative environments where multiple users can interact with the same computer-generated 3D models. Our results show the game successfully engaged participants in creating and viewing the shared augmented environment and encouraged them to stay physically active in order to explore existing models and place new models. These technological advances such as cloud anchors help to promote the mass adoption of collaborative AR technologies.
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures

Abstract Motivation In this article, we consider how to evaluate survival distribution predictions with measures of discrimination. This is non-trivial as discrimination measures are the most commonly used in survival analysis and yet there is no clear method to derive a risk prediction from a distribution prediction. We survey methods proposed in literature and software and consider their respective advantages and disadvantages. Results Whilst distributions are frequently evaluated by discrimination measures, we find that the method for doing so is rarely described in the literature and often leads to unfair comparisons or ‘C-hacking’. We demonstrate by example how simple it can be to manipulate results and use this to argue for better reporting guidelines and transparency in the literature. We recommend that machine learning survival analysis software implements clear transformations between distribution and risk predictions in order to allow more transparent and accessible model evaluation. Availability and implementation The code used in the final experiment is available at https://github.com/RaphaelS1/distribution_discrimination.
Delay Analysis of Consensus Communication for Blockchain-Based Applications Using Network Calculus

Blockchain has been witnessed a growing application in mobile communication. The application field ranges from the Internet of Things (IoT), Vehicle to Everything (V2X), Mobile Edge Computing (MEC), etc. The conjunction with blockchain can significantly avoid data tempering and protect user privacy. Although blockchain guarantees the security of mobile communication, the operation cost is increasing. The consensus algorithm of blockchain drives multiple untrust parties to keep data consistency, and it generates a large number of communication overheads. In this letter, we focus on the Byzantine Fault Tolerance (BFT) type of consensus and conduct a performance model. By utilizing the deterministic network calculus method, we analyze the delay bound of the Practical Byzantine Fault Tolerance (PBFT) and HotStuff consensus algorithm. The result of the simulation provides a valuable guideline for delay-sensitive blockchain-based applications.