Reviewerly/exHarmony huggingface dataset:
A nonconvex hybrid regularization model for restoring blurred images with mixed noises

Nonconvex regularization has advantages over convex regularization for preserving the edges of images, and hybrid regularization method based on total variation and wavelet frames is also highly effective for improving the quality of restoring images. Therefore, this paper proposes a nonconvex hybrid regularization model for restoring blurred images with additive noises and multiplicative noises, which is a quite challenging problem compared with the problem of restoring blurred images with a single type of noise. Although the proposed model is jointly nonconvex, every subproblem is convex under some mild conditions when the alternating minimization method is used to solve the proposed model. At the same time, the convergence property of the corresponding algorithm is discussed. Numerical experiments show that the proposed model is effective in edge-preserving, staircase-reduction and detail-reserving in image restoration and it outperforms the state-of-the-art model in terms of the PSNR, ReErr and MSSIM values.
Multi-Semantic Alignment Graph Convolutional Network

Graph Convolutional Network (GCN) is a powerful emerging deep learning technique for learning graph data.However, there are still some challenges for GCN.For example, the model is shallow; the performance is poor when labelled nodes are severely scarce.In this paper, we propose a Multi-Semantic Aligned Graph Convolutional Network (MSAGCN), which contains two fundamental operations: multi-angle aggregation and semantic alignment, to resolve two challenges simultaneously.The core of MSAGCN is the aggregation of nodes that belong to the same class from three perspectives: nodes, features, and graph structure, and expects the obtained node features to be mapped nearby.Specifically, multi-angle aggregation is applied to extract features from three angles of the labelled nodes, and semantic alignment is utilised to align the semantics in the extracted features to enhance the similar content from different angles.In this way, the problem of over-smoothing and over-fitting for GCN can be alleviated.We perform the node clustering task on three citation datasets, and the experimental results demonstrate that our method outperforms the state-of-the-art (SOTA) baselines.
Exploring reinforcement learning approaches for drafting in collectible card games

Collectible card games (CCGs), such as Magic: the Gathering and Hearthstone, are played by tens of millions worldwide and are known for their usually large and intricate rules. From an artificial intelligence (AI) standpoint, playing CCGs consists of two interdependent tasks: deck-building and battling. This paper presents three deep reinforcement learning approaches for deck-building in the arena mode of CCGs. Our approaches are trained in self-play and differ in the handling of past information when drafting new cards for a deck. We formulate the problem in a game-agnostic manner and perform experiments on Legends of Code and Magic, a CCG designed for AI research. Considering the win rate of the decks when used by fixed battling agents, the results show that our trained drafting agents outperform the best ones available for the game and do so by building very different decks. We also reenact the Strategy Card Game AI competition and show that our best drafting strategy improves the win rate of a baseline competitor by 14.9 and 12.7 percentage points in the 2019 and 2020 editions.
Evolution of COVID-19 tweets about Southeast Asian Countries: topic modelling and sentiment analyses

Despite the global scale of this pandemic, comparison and contrast of topics, sentiment and emotions of tweets among countries are limited. Further, most previous studies covered a short timeframe due to the recency of the event and the large volume of tweets. The purposes of this research were to (1) identify the multiplicity of public discourse about countries during the COVID-19 pandemic and how they evolved, (2) compare and contrast sentiment levels and (3) compare emotions about countries over time. The research scope covered 115,553 tweets that mentioned ten countries in Southeast Asia (SEA) from 22 January 2020 to 31 July 2021. This research presents the infoveillance methods—using a topic modelling algorithm (LDA), VADER and NRC sentiment analyses—that elucidated the evolution and the emergence of public narratives and sentiment affecting country brands during the pandemic. Results also shed light on the role of word-of-mouth (WOM) communications in the place branding process.
DeepLabel: Automated Issue Classification for Issue Tracking Systems

With the growth of Issue Tracking Systems, issue reports have become an important data to aid software maintenance and evaluation. Issue classification is one of the most important methods for such purpose, which aims to automatically distinguish issues related to bugs from other issues via machine learning algorithm. However, existing issue classification approaches are still inadequate due to either the incorrect usages of the textual fields of the issues or the ineffective feature representation methods. In this paper, we propose a novel issue classification approach named DeepLabel for achieving advanced issue classification. DeepLabel predicts the issue types by the ensemble of field-specific models that are applied on different textual fields, so as to make the maximum use of the information contained in the textual fields. In addition, DeepLabel adopts Word2Vec combined with attention-based Bi-directional Long Short-Term Memory (ABLSTM) as the feature extractor for the field-specific models in order to effectively extract the semantic information from the textual fields. We conduct an empirical study to evaluate the effectiveness of DeepLabel based on a widely used issue dataset. The results demonstrate that DeepLabel can significantly outperform the state-of-the-art approaches, in which DeepLabel correctly identifies more bug issues (160.1 vs. 140.1) and more non-bug issues (345.7 vs. 325.4) on average compared to the best one existing approach.
GenEthos: A Synthetic Data Generation System With Bias Detection And Mitigation

Data-driven models function admirably in solving real-world problems. However, obtaining relevant data is difficult. Also, sometimes more diverse data is needed to identify the limitations of trained Machine Learning models. Creating such data samples based on earlier known metadata is a common practice. However, this process can induce bias in the dataset unknowingly. Generative Adversarial Networks (GAN) based data generation models generate more data based on initial data distribution. Thus, data generation models may reflect bias in the generated synthetic data. In this study, the authors have proposed an interactive synthetic data generation Graphical User Interface (GUI) tool. The tool is equipped with Bias detection and mitigation algorithms which will notify users about the pre-existing bias and provide methods to mitigate it. Similarly, this tool can be used to evaluate synthetic data generated using GAN-based models against fairness metrics. The authors have found that Learning Fair Representation (LFR) bias mitigation method has performed 62% & 17.5% better than Prejudice remover and Disparate impact remover for German Credit & Adult original datasets. These results were concluded based on bias detection metrics such as Statistical Parity Difference (SPD) and Disparate Impact (DI). The proposed data generation tool used with LFR method can reduced SPD metric by 93% on original German Credit data. The authors conclude that both original and synthetic datasets had a bias. Therefore, the fairness level of any dataset should be checked vigilantly.
A Pilot Study for Understanding Users’ Attitudes Towards a Conversational Agent for News Recommendation

Conversational recommender agents have been rapidly developed and applied in various domains (e.g., amusement, e-commerce, tourism) in recent years, to allow users to easily access information or service through natural communication with the system. However, little attention has been paid to the news domain, though some news organizations (e.g., ABC, BBC) have started to deploy news chatbots to engage with audiences. In this work, we performed a pilot study in form of a semi-structured interview for the purpose of knowing important features of recommendations users expect when they interact with a news conversational agent. In particular, in order to acquire users' thoughtful feedback, we implemented a prototype system based on a taxonomy that covers all of the major recommendation-seeking and information-searching goals according to related literature. The interview results reveal users' opinions on various aspects of a conversational agent for news recommendation, including the condition under which they may request/accept the news recommendation by a conversational agent, important features of the conversational news recommendation they expect, and their preferred preference elicitation strategy. Several practical implications are concluded at the end, which might inspire the design and development of effective conversational agents in the news domain.
Multimodal Conversational Fashion Recommendation with Positive and Negative Natural-Language Feedback

In a real-world shopping scenario, users can express their natural-language feedback when communicating with a shopping assistant by stating their satisfactions positively with "I like" or negatively with "I dislike" according to the quality of the recommended fashion products. A multimodal conversational recommender system (using text and images in particular) aims to replicate this process by eliciting the dynamic preferences of users from their natural-language feedback and updating the visual recommendations so as to satisfy the users' current needs through multi-turn interactions. However, the impact of positive and negative natural-language feedback on the effectiveness of multimodal conversational recommendation has not yet been fully explored.Since there are no datasets of conversational recommendation with both positive and negative natural-language feedback, the existing research on multimodal conversational recommendation imposed several constraints on the users' natural-language expressions (i.e. either only describing their preferred attributes as positive feedback or rejecting the undesired recommendations without any natural-language critiques) to simplify the multimodal conversational recommendation task. To further explore the multimodal conversational recommendation with positive and negative natural-language feedback, we investigate the effectiveness of the recent multimodal conversational recommendation models for effectively incorporating the users' preferences over time from both positively and negatively natural-language oriented feedback corresponding to the visual recommendations. We also propose an approach to generate both positive and negative natural-language critiques about the recommendations within an existing user simulator. Following previous work, we train and evaluate the two existing conversational recommendation models by using the user simulator with positive and negative feedback as a surrogate for real human users. Extensive experiments conducted on a well-known fashion dataset demonstrate that positive natural-language feedback is more informative relating to the users' preferences in comparison to negative natural-language feedback.
Keep on Smiling: An Investigation of the Influence of the Use of Emoticons by Chatbots on User Satisfaction

Most of us have chatted with a chatbot before and remember frustrating experiences. Research shows that friendly and empathetic communication can compensate for existing communication problems. We therefore conducted a study to investigate whether the use of emoticons leads to higher user satisfaction when communicating with a chatbot. The use case was a chatbot recommending courses on a German e-learning platform. The results did not reach significance, but show a positive trend, which leads to indications of what future research should investigate.
A Blueprint for Integrating Task-Oriented Conversational Agents in Education

Over the past few years, there has been an increase in the use of chatbots for educational purposes. Nevertheless, the chatbot technologies and architectures that are often applied to educational contexts are not necessarily designed for such contexts. While general-purpose chatbot technologies can be used in educational contexts, there are some challenges specific to these contexts that need to be taken into consideration. Namely, chatbot technologies intended for education should, by design, integrate directly within online learning applications and focus on achieving learning goals by supporting learners with the task at hand. In this paper, we propose a blueprint for an architecture specifically aimed at integrating task-oriented chatbots to support learners in educational contexts. We then present a proof-of-concept implementation of our blueprint as a part of a code review application designed to teach programming best practices. Our blueprint could serve as a starting point for developers in education looking to build chatbot technologies targeting educational contexts and is a first step toward an open chatbot architecture explicitly tailored for learning applications.
Rules Of Engagement: Levelling Up To Combat Unethical CUI Design

While a central goal of HCI has always been to create and develop interfaces that are easy to use, a deeper focus has been set more recently on designing interfaces more ethically. However, the exact meaning and measurement of ethical design has yet to be established both within the CUI community and among HCI researchers more broadly. In this provocation paper we propose a simplified methodology to assess interfaces based on five dimensions taken from prior research on so-called dark patterns. As a result, our approach offers a numeric score to its users representing the manipulative nature of evaluated interfaces. It is hoped that the approach - which draws a distinction between persuasion and manipulative design, and focuses on how the latter functions rather than how it manifests - will provide a viable way for quantifying instances of unethical interface design that will prove useful to researchers, regulators and potentially even users.
CANDY: a framework to design Conversational AgeNts for Domestic sustainabilitY

In the 2020s, world countries are called to take action to solve global issues, as defined in the Sustainable Development Goals (SDG). In our research, we are interested in exploring how Conversational Agents can be exploited to pursue the above goals, particularly in domestic spaces where CAs are becoming more and more popular. As a preliminary step in this research work, we organized a focus group with seven participants aimed at: i) investigating the potential of Conversational Agents - integrated with digital devices - to promote a more sustainable behavior at home; ii) eliciting the requirements on conversational interaction that such CAs should meet for this purpose. From the experience and findings of the focus group, we distilled a conceptual framework called CANDY, which highlights the core design dimension of Conversational Agents for Sustainability, and can be used to guide the processes of requirements elicitation and design for this category of CAs.
Effects of Wording and Gendered Voices on Acceptability of Voice Assistants in Future Autonomous Vehicles

Voice assistants in future autonomous vehicles may play a major role in supporting the driver during periods of a transfer of control with the vehicle (handover and handback). However, little is known about the effects of different qualities of the voice assistant on its perceived acceptability, and thus its potential to support the driver's trust in the vehicle. A desktop study was carried out with 18 participants, investigating the effects of three gendered voices and different wording of prompts during handover and handback driving scenarios on measures of acceptability. Participants rated prompts by the voice assistant in nine different driving scenarios, using 5-point Likert style items in a during and post-study questionnaire as well as a short interview at the end. A commanding/formally worded prompt was rated higher on most of the desirable measures of acceptability as compared to an informally worded prompt. The 'Matthew' voice used was perceived to be less artificial and more desirable than the 'Joanna' voice and the gender-ambiguous 'Jordan' voice; however, we caution against interpreting these results as indicative of a general preference of gender, and instead discuss our results to throw light on the complex socio-phonetic nature of voices (including gender) and wording of voice assistants, and the need for careful consideration while designing the same. Results gained facilitate the drawing of insights needed to take better care when designing the voice and wording for voice assistants in future autonomous vehicles.
A Comparative Study of Bayesian and Dempster-Shafer Fusion on Image Forgery Detection

With the advent of digital imaging, it has become fairly easy to modify the content of an image in many different ways while leaving no obvious visual clue. This has further challenged many existing image forensic techniques. The techniques which perform well with one specific kind of forgeries still suffer from strong limitations when dealing with realistic tampered images. Therefore, an effective strategy for tampering detection and localization requires the application of fusion technique. Although there have been extensive researches on fusion technique on different fields, there has never been a systematic study about fusion technique in image forensic domain. In this paper, we provide a thorough review on the state-of-the-art of fusion methods applied in tampering image detection and localization domain. We then present a practical comparison of two popular fusion techniques: Bayesian and Dempster-Shafer theory (DST) based fusion. The comparison relies on two applications which leverage the two aforementioned fusion techniques. In the first case, aggregating the decision maps of two forensic approaches: Photo Response Non Uniformity (PRNU) and statistical features based approaches has improved the forgery detection performance on saturated and dark regions of images. In the second case, integrating the decision maps of the forensic approach using demosaicing artifacts and the forensic approach using SIFT descriptors and local color dissimilarity maps has enhanced the detection performance on both copy-moved and copypasted forgeries images. Experiments show that the DST based fusion performs better in the first case while the Markov Random Field (MRF) based fusion performs better in the second case. It can be concluded that each technique has its own advantages and the best choice depends on each situation and users’ requirements.
Knowledge-grounded dialogue modelling with dialogue-state tracking, domain tracking, and entity extraction

As knowledge-grounded dialogue systems are attracting intense research interest, technology that facilitates reference to various types of external knowledge as dialogue-system data is also developing. A knowledge-grounded dialogue system must be capable of (1) accurately interpret the conversation content, (2) determining whether external knowledge should be referenced for the current turn, (3) identifying the external knowledge to be referenced, and (4) generating a response. If the referenced external knowledge is in the form of a question–answer pair, this pair is closely related to the domain to which the external knowledge belongs and the entity to which it pertains. This study proposes a knowledge-grounded dialogue system that predicts the domain and entity associated with a question–answer pair referenced by the dialogue system; the system then leverages this information to effectively implement the above three external-knowledge-based capabilities. As the dialogue data associated with external knowledge is diverse, adaptation to the slots and entities of different dialogue datasets is challenging. In this study, the Triple Copy (TripPy) model, which is one of the leading benchmark models for dialogue-state tracking with the Multi-domain Wizard-of-Oz (MultiWOZ) dataset, is further developed to adapt to DSTC10 data for the external knowledge-based dialogue system; hence, dialogue content is effectively interpreted. The developed knowledge-grounded dialogue system incorporates knowledge-seeking turn detection, knowledge selection, and knowledge-grounded response generation models. The model, in DSTC10 dataset, achieves 15.49%p, 12.54%p, and 36.45%p improvements over the baseline, the state-of-the-art model for the previous version of the data (DSTC9 dataset), in terms of the F1 score, [email protected] score, and BLEU-1 score, respectively. Moreover, in a dialogue-state tracking task for DSTC10 dataset, a 15.41%p improvement in joint goal accuracy score is achieved compared to the TripPy model.
A novel fast combine-and-conquer object detector based on only one-level feature map

In this paper, we present a conceptually simple, flexible, and efficient ”Combine-and-Conquer” detection framework. The proposed framework is composed of a very simple one-level detection pipeline . We modularized the proposed framework into four parts, Backbone, Neck, Feature Aggregation and Detection Head. To verify the performance of this framework, we design a simple yet strong detector, CC-Det. First CC-Det deploys a backbone network to encode the input image, and then uses a neck network to extract rich features and enlarge the receptive field. Next, a feature aggregation network is deployed to aggregate multi-scale features into one feature map. Finally, only one detection head is deployed on the one-level feature map to output a heatmap and bounding boxes . Compared with existing multi-level detectors such as RetinaNet and FCOS, CC-Det achieves excellent performance with much fewer parameters of model and much lower FLOPs. In addition, CC-Det also achieves better trade-off among speed, accuracy and model size without any elaborate special design, compared to other one-level detectors. Moreover, CC-Det is easy to generalize to other tasks with minor modifications and achieves state-of-the-art performance. Excellent results are presented on COCO, PASCAL VOC, WiderFace and CrowdHuman datasets. • We follow the proposed Combine-and-Conquer to design CC-Det detector with a simple network structure and anchor-free mechanism. Thanks to the one-level feature, the hassle of carefully designing multi-level label assignment like scale range of interest is unnecessary for our framework. • Without any bells and whistles, CC-Det achieves state-of-the-art performance with fewer model’s parameters and lower FLOPs, surpassing previous powerful baseline models of multi-level detection framework. • CC-Det is evaluated on multiple datasets, COCO, VOC, WiderFace and CrowdHuman of different detection tasks and achieves excellent performance, demonstrating the versatility of the propose detection framework. • Based on the propose framework, we also design a more efficient yet effective detector, CC-Det-E. Compared with CC-Det, CC-Det-E is almost twice as fast with less than 1% performance loss, achieving better trade-off between precision and speed. • To further demonstrate the powerful potential of Combine-and-Conquer framework, we design a strong yet efficient detector CC-Det-v2. On COCO benchmark, CC-Det-v2 shows excellent trade-off between precision and speed.