Reviewerly/exHarmony huggingface dataset:
DHG-GAN: Diverse Image Outpainting via Decoupled High Frequency Semantics

Diverse image outpainting aims to restore large missing regions surrounding a known region while generating multiple plausible results. Although existing outpainting methods have demonstrated promising quality of image reconstruction, they are ineffective for providing both diverse and realistic content. This paper proposes a Decoupled High-frequency semantic Guidance-based GAN (DHG-GAN) for diverse image outpainting with the following contributions. 1) We propose a two-stage method, in which the first stage generates high-frequency semantic images for guidance of structural and textural information in the outpainting region and the second stage is a semantic completion network for completing the image outpainting based on this semantic guidance. 2) We design spatially varying stylemaps to enable targeted editing of high-frequency semantics in the outpainting region to generate diverse and realistic results. We evaluate the photorealism and quality of the diverse results generated by our model on CelebA-HQ, Place2 and Oxford Flower102 datasets. The experimental results demonstrate large improvement over state-of-the-art approaches.
Exp-GAN: 3D-Aware Facial Image Generation with Expression Control

This paper introduces Exp-GAN, a 3D-aware facial image generator with explicit control of facial expressions. Unlike previous 3D-aware GANs, Exp-GAN supports fine-grained control over facial shapes and expressions disentangled from poses. To this ends, we propose a novel hybrid approach that adopts a 3D morphable model (3DMM) with neural textures for the facial region and a neural radiance field (NeRF) for non-facial regions with multi-view consistency. The 3DMM allows fine-grained control over facial expressions, whereas the NeRF contains volumetric features for the non-facial regions. The two features, generated separately, are combined seamlessly with our depth-based integration method that integrates the two complementary features through volume rendering. We also propose a training scheme that encourages generated images to reflect control over shapes and expressions faithfully. Experimental results show that the proposed approach successfully synthesizes realistic view-consistent face images with fine-grained controls. Code is available at https://github.com/kakaobrain/expgan .
Semi-supervised Semantic Segmentation with Uncertainty-Guided Self Cross Supervision

As a powerful way of realizing semi-supervised segmentation, the cross supervision method learns cross consistency based on independent ensemble models using abundant unlabeled images. In this work, we propose a novel cross supervision method, namely uncertainty-guided self cross supervision (USCS). To avoid multiplying the cost of computation resources caused by ensemble models, we first design a multi-input multi-output (MIMO) segmentation model which can generate multiple outputs with the shared model. The self cross supervision is imposed over the results from one MIMO model, heavily saving the cost of parameters and calculations. On the other hand, to further alleviate the large noise in pseudo labels caused by insufficient representation ability of the MIMO model, we employ uncertainty as guided information to encourage the model to focus on the high confident regions of pseudo labels and mitigate the effects of wrong pseudo labeling in self cross supervision, improving the performance of the segmentation model. Extensive experiments show that our method achieves state-of-the-art performance while saving 40.5% and 49.1% cost on parameters and calculations.
D$$^{{3}}$$: Duplicate Detection Decontaminator for Multi-Athlete Tracking in Sports Videos

Tracking multiple athletes in sports videos is a very challenging Multi-Object Tracking (MOT) task, since athletes often have the same appearance and are intimately covered with each other, making a common occlusion problem becomes an abhorrent duplicate detection. In this paper, the duplicate detection is newly and precisely defined as occlusion misreporting on the same athlete by multiple detection boxes in one frame. To address this problem, we meticulously design a novel transformer-based Duplicate Detection Decontaminator (D $$^3$$ ) for training, and a specific algorithm Rally-Hungarian (RH) for matching. Once duplicate detection occurs, D $$^3$$ immediately modifies the procedure by generating enhanced box losses. RH, triggered by the team sports substitution rules, is exceedingly suitable for sports videos. Moreover, to complement the tracking dataset that without shot changes, we release a new dataset based on sports video named RallyTrack. Extensive experiments on RallyTrack show that combining D $$^3$$ and RH can dramatically improve the tracking performance with 9.2 in MOTA and 4.5 in HOTA. Meanwhile, experiments on MOT-series and DanceTrack discover that D $$^3$$ can accelerate convergence during training, especially saving up to 80 $$\%$$ of the original training time on MOT17. Finally, our model, which is trained only with volleyball videos, can be applied directly to basketball and soccer videos, which shows the priority of our method. Our dataset is available at https://github.com/heruihr/rallytrack .
Learning Internal Semantics with Expanded Categories for Generative Zero-Shot Learning

In recent years, generative Zero-Shot Learning (ZSL) has attracted much attention due to its better performance than traditional embedding methods. Most generative ZSL methods exploit category semantic plus Gaussian noise to generate visual features. However, there is a contradiction between the unity of category semantic and the diversity of visual features. The semantic of a single category cannot accurately correspond to different individuals in the same category. This is due to the different visual expression of the same category. Therefore, to solve the above mentioned problem we propose a novel semantic augmentation method, which expands a single semantic to multiple internal sub-semantics by learning expanded categories, so that the generated visual features are more in line with the real visual feature distribution. At the same time, according to the theory of Convergent Evolution, the sub-semantics of unseen classes are obtained on the basis of the expanded semantic of their similar seen classes. Four benchmark datasets are employed to verify the effectiveness of the proposed method. In addition, the category expansion is also applied to three generative methods, and the results demonstrate that category expansion can improve the performance of other generative methods. Code is available at: https://github.com/njzxj/EC-GZSL .
Foreground-Specialized Model Imitation for Instance Segmentation

Instance segmentation is formulated as a multi-task learning problem. However, knowledge distillation is not well-suited to all sub-tasks except the multi-class object classification. Based on such a competence, we introduce a lightweight foreground-specialized (FS) teacher model, which is trained with foreground-only images and highly optimized for object classification. Yet, this leads to discrepancy between inputs to the teacher and student models. Thus, we introduce a novel Foreground-Specialized model Imitation (FSI) method with two complementary components. First, a reciprocal anchor box selection method is introduced to distill from the most informative output of the FS teacher. Second, we embed the foreground-awareness into student’s feature learning via either adding a co-learned foreground segmentation branch or applying a soft feature mask. We conducted an extensive evaluation against the others on COCO and Pascal VOC.
A new multi-objective point path planning algorithm for mobile robots

Aiming at the path planning problem of mobile robot passing through multiple target points with the shortest path under two-dimensional raster map, this paper proposes a method combining an improved A* algorithm with an annealing genetic algorithm.For the problem that A* algorithm will search adjacent nodes in turn, Jump Point Search (JPS) algorithm is introduced to avoid the search of surrounding nodes by searching the jump point of the current node and reduce the calculation time.In view of the prematurity problem of genetic algorithm, the simulated annealing algorithm is introduced to make the simulated annealing algorithm and genetic algorithm complement each other and overcome the prematurity problem of genetic algorithm effectively.By using TSPLIB dataset, the path planning of the improved genetic algorithm is obviously better than that of the original genetic algorithm.Finally, the feasibility and effectiveness of the method are verified by simulation, and the shortest safe path can be found quickly in the process of optimization.
Multi-view Comprehensive Graph Clustering

Multi-view clustering algorithms have been successfully used in different consumer electronic products, such as common digital cameras and unmanned vehicles. Currently, existing multi-view graph clustering (MVGC) methods learn the similarity of directly connected samples for clustering. However, these MVGC methods can not fully consider the indirect relation among samples and high-order relation across multi-view data. In this paper, a new multi-view comprehensive graph clustering (MCGC) method is devised, which can fully learn the similarity based on (1) first-order proximity (FOP) (i.e. the direct relation of pairwise samples); (2) second-order proximity (SOP) (i.e. the indirect relation of pairwise samples); and (3) third-order proximity (TOP) (i.e. the three-order relation of multiple views). Since the operations of these three components are iteratively carried out, the interaction between similarity learning can be encouraged and the comprehensive graph can be generated effectively for clustering. In-depth experiments on six commonly benchmark datasets show the superiority of the MCGC method.
Cybersecurity culture in the public and private sector area in the Central European region

Cyber security is a growing problem associated with everything a citizen or organisation does in cyberspace. The problem thus outlined fits into a multifaceted programme that can be addressed through cyber security management. The analysis in the article compares the level of cyber security awareness among the staff of public institutions and the private sector in four Central European countries, namely Poland, the Czech Republic, Slovakia and Hungary. Public institutions are by nature open, decentralised and rich in a wide range of data about the state, society, economy, economics and research and innovation. For this reason, they are often exposed to serious cyber threats. This study examines the relationship between cyber security culture and the urgent need for preventive action against possible cyber threats. The main thesis of the study is that an adequate understanding of cyber security culture and improved awareness among employees about digital threats is key to achieving cyber security growth. The research illustrates breaches in so-called digital hygiene, which are caused by a lack of knowledge, skills and errors in employee behaviour. Following a survey of staff employed in both public and private organisations, an identification of key cyber security risk factors is made. Increasing staff competencies can help improve cyber resilience.
Federated Knowledge Purification for Responsive Internet of Things

The Internet of Things (IoT) has become a ubiquitous technology that enables the collection and analysis of large amounts of data. However, the limited resources of IoT devices pose challenges to enabling responsive decision-making. Many communications are required for network training, yet network updates can be very big if they include many parameters. Participants and the IoT ecosystem both bear the brunt of federated learning's high Latency due to the magnitude of its communications infrastructure requirements. In this paper, we propose a Federated Knowledge Purification (FKP) approach based on dynamic reciprocal knowledge purification and adaptive gradient compression, two strategies that allow for low-latency communication without sacrificing effectiveness, which enables responsive IoT devices with limited resources. The FKP approach leverages a collaborative learning approach to enable IoT devices to learn from each other's experiences while preserving the privacy of their data. A smaller model is trained on the aggregated knowledge of a larger model trained on a centralized server, and this smaller model can be deployed on IoT devices to enable responsive decision-making with limited computational resources. Experimental results demonstrate the effectiveness of the proposed approach in improving the performance of IoT devices while maintaining the privacy of their data. The proposed approach also outperforms existing federated learning methods in terms of communication efficiency and convergence speed.
Federated Learning for Intelligent Resources Allocation in Internet of Things

By using federated learning (FL), multiple Internet-of-Things (IoT) devices can construct a shared learning model without sending raw data to a centralized server. While FL has come a long way, it still has a ways to go. Issues such as heterogeneous user equipment (UEs) and data that is not independently and uniformly distributed are still obstacles. Facilitating a numerous UEs to participate in the learning in each cycle poses a possible problem of the huge communication budget. A weighted adjoining factor is presented to the localized gradient descent, generalizing the present FedAvg to solve these concerns. At the start of each global round, the proposed FL method randomly selects a fraction of the UEs to perform stochastic gradient descent in parallel. Then, we utilize the suggested FL method in cellular IoT to reduce either total power usage or execution duration of FL, in which a straightforward but effective path-following method is constructed for its explanations. At last, obtained simulations on poorly balanced data are presented to show that the presented FL algorithm is superior to FedAvg in terms of performance with respect to fast convergence. Moreover, they show that the suggested algorithm needs significantly less time and energy to train than the FL algorithm does when users contribute heavily to the learning process. These findings provide strong support for the suggested FL algorithm as a potential paradigm change for training mobile IoT networks with limited bandwidth.
Information Communication Technologies (ICTs) and Disaster Risk Management (DRM): Systematic Literature Review

Disasters are considered a major problem worldwide and a significant threat to sustainable development, causing, among others, the loss of lives, the destruction of infrastructure, economic disruption. The implementation of policies and strategies that will prevent future disaster risk, reduce existing disaster risk, and manage residual risk has become more vital than ever. It requires multi-sector collaboration to achieve enhanced resilience to the multiple hazards to prevent and/or reduce the potential losses, assure prompt assistance to victims of disasters, and achieve rapid and effective recovery. Such an approach demands collaboration between scientists and authorities and proper use of the available information so that the steps to be taken can be understood and processed by all the stakeholders involved. The utilization of disaster risk information, along with the use of more sophisticated ICTs, will provide policymakers with a holistic DRM, enabling decision support for a natural and/or man-made crisis. To that end, the key objective of this study was to conduct a Systematic Literature Review to highlight the existing research approaches in ICT for DRM. To analyze and evaluate the findings, the selected studies were classified according to four areas: (1) Stakeholders, (2) Disaster phase, (3) Disaster type, and (4) ICT. Additionally, a SWOT analysis was conducted to provide a holistic overview of ICTs and their applications in the DRM sector. Hence, our work attempts to present a comprehensive analysis of the research approaches and determines the arising opportunities and shortcomings that require the attention of the research community.
Optimizing Urban Resilience via FCM and Participatory Modeling: The Case of Joensuu Finland

Improving community urban resilience necessitates a comprehensive awareness of all possible hazards and resilience choices, as well as the interests and objectives of many stakeholders, in order to create and implement inclusive and proactive interventions. For such complex urban ecosystems, we may utilize soft computing knowledge such as Fuzzy Theory and Decision Support Systems (DSS) to provide an innovative participatory model that identifies and quantifies urban resilience tradeoffs. We concentrate on the design of a Fuzzy Cognitive Map (FCM) that interlinks several participant perspectives in the form of semi-quantitative cause-and-effect linguistic responses from experts and stakeholders. More specifically, we analyze how this explainable Artificial Intelligence (XAI) cognitive model may assist decision-makers and competent authorities in developing urban resilience solutions. Based on stakeholders’ collective expertise the aggregated FCM determines the most advantageous initiatives in terms of their direct and indirect implications on urban resilience. We apply this model for the case of the city of Joensuu, Finland. For the use case, steady state analysis as well as best and worst-case scenarios are produced which are based on environmental, economic, social, and technological criteria. The prototype DSS is evaluated with both sigmoidal and hyperbolic tangent activations producing comparative results.
A Study on Education Contents of Jinju Sword Dance for Free Semester System

The free semester system was operated by the Ministry of Education-designated test operation school in 2013, and as of 2016, this system got to be implemented at the middle schools around the country. The free semester system aims to build up one’s dream and talent through career exploration activity and diverse experience activity away from the burdens of taking an examination for one semester among the middle school curriculum.&#x0D; With the addition of ‘Creative Experience Activity’ to the 2009 curriculum, the code ‘Creativity’ was highlighted, but practical creative education wasn’t realized. To overcome such a limit, what was suggested at a national level was the very “Free Semester System.”&#x0D; In case of a school which put the free semester system into practice in 2015, unlike the fundamental purpose of this system, there were many cases where the programs, which were conducted in a club activity and after-school activity, were extended as the free semester system.&#x0D; The education which can serve to develop a potential talent through the free semester system is no other than arts education, and among others, the educational field stands in need of experience and education of the national intangible cultural heritages.&#x0D; It’s because the national intangible cultural heritages provide diverse elements which can be used as educational contents as they cherish our people’s identity, historicity and cultural prototype. At present, the transmission of intangible cultural heritages is in good progress, but they have not been properly used as cultural heritages which arouse sympathy between generations, and have the property of realism(presentness). In order for intangible cultural heritages not to be the heritages in the past, the existing cultural heritages should be recreated as the present ones through education contents. This researcher is intending to propose Jinju Sword Dance education contents which are suited for the free semester system on the basis of the experience in teaching the National Intangible Cultural Property No. 12, Jinju Sword Dance.
Labelled Sequent Calculi for Conditional Logics: Conditional Excluded Middle and Conditional Modus Ponens Finally Together

We introduce labelled sequent calculi for Conditional Logics with a selection function semantics. Conditional Logics are a sort of generalization of multimodal logics where modalities are labelled by formulas of the same language. Recently, they received a renewed attention and have found several applications in knowledge representation and artificial intelligence. In a previous work, we have considered the basic system CK and extensions with well known conditions ID, MP, CS and CEM, with the exception of those admitting both conditions CEM and MP, obtaining labelled sequent calculi called SeqS. Here we provide calculi for the whole cube of the extensions of CK generated by the above axioms, including also those with both CEM and MP: the basic idea is that of replacing the rule dealing with CEM in SeqS, which performs a label substitutions in both its premises, by a new one that avoids such a substitution and adopts a conditional formula on the right-hand side of a sequent as its principal formula. We have also implemented the proposed calculi in Prolog following the “lean” methodology, then we have tested the performances of the new prover, called CondLean2022, and compared them with those of CondLean, an implementation of SeqS, on the common systems. The performances of CondLean2022 are promising and seem to be better than those of CondLean, witnessing that the proposed calculi also provide a more efficient theorem prover for Conditional Logics.
Topic Modelling and Frame Identification for Political Arguments

Presidential debates are one of the most salient moments of a presidential campaign, where candidates are challenged to discuss the main contemporary and historical issues in a country. These debates represent a natural ground for argumentative analysis, which has been always employed to investigate political discourse structure in philosophy and linguistics. In this paper, we take the challenge to analyse these debates from the topic modeling and framing perspective, to enrich the investigation of these data. Our contribution is threefold: first, we apply transformer-based language models (i.e., BERT and RoBERTa) to the classification of generic frames showing that these models improve the results presented in the literature for frame identification; second, we investigate the task of topic modelling in political arguments from the U.S. presidential campaign debates, applying an unsupervised machine learning approach; and finally, we discuss various visualisations of the identified topics and frames from these U.S. presidential election debates to allow a further interpretation of such data.
Embedding Contextual Information in Seq2seq Models for Grounded Semantic Role Labeling

Natural Language interactions between humans and robots are meant to be situated, in the sense that both the user and the robot can access and make reference to the shared environment. Contextual knowledge plays thus a key role in the solution of inherent ambiguities in interpretation tasks, such as Grounded Semantic Role Labeling (GSRL). Explicit representations for the context (i.e. the map description of the surroundings) are crucial and the possibility of injecting such information in the training stages of semantic interpreters is very appealing. In this paper, we propose to make a sequence-to-sequence model for GSRL, thus eliminating the traditional cascade of tasks and effectively linking real-world entities with their identifiers, that is sensitive to map information in form of linguistic descriptions. The corresponding generation process, based on BART, achieves results competitive with the state-of-the-art on the GSRL task.