Reviewerly/exHarmony huggingface dataset:
Restoration of ancient temple murals using cGAN and PConv networks

India is a fountainhead of several art forms like paintings, inscriptions, sculptures, pottery, textile arts, and so on. Mural paintings are one in all of them and are usually seen on the walls of temples and caves. A redeeming feature of temple murals is that the architectural elements of the given space are simultaneously incorporated into the paintings. The majority of these paintings have faded with time, and a select handful have cracks and dirt stains on top of them. Understanding the nuances of the paintings become difficult as they deteriorate further. These paintings need to be restored by qualified craftsmen, who are difficult to find today. Therefore, a powerful image restoration method is needed to meet the requirements of the mural paintings. Thus an efficient inpainting technique for the reconstruction of ancient temple murals that ignores these multiple random irregularities is being proposed. The proposed method makes use of cGAN for both the automatic development of masks and the identification of degraded sections. A mask is an example of a black and white image where the white pixels require some editing while the black pixels do not. These masks are used by the algorithm as a hyper-parameter to forecast which patch has to be filled in next. The deteriorated murals are rebuilt using a sliding window-based Deep Convolutional Network, where the convolution is masked and renormalized to be conditioned on only valid pixels. As part of the forward pass, the proposed work automatically creates a changed mask for the following layer. The performance of our combined cGAN-Deepconv inpainting technique has been compared with six state-of-the-art inpainting methods. The experimental reconstruction results confirmed that our Sliding-window-Deepconv inpainting is more adaptable and better suited for mural restoration. Further, the proposed method achieved the best reconstruction results with the absolute best values of several performance parameters, i.e. Peak Signal to Noise Ratio, Mean Squared Error, and Structural Similarity Index. • This article introduces an efficient technique for the identification and restoration of damaged areas seen in ancient temple murals. • Manual identification of damaged areas and the creation of masked images are laborious tasks, so this work develops a model for the generation of masked images using cGAN. • This work uses a Partial Convolution based inpainting technique and results in better performance. • The performance of the proposed system is meassured by comparing the existing inpainting techniques.
The Bees Algorithm—A Gentle Introduction

The Bees AlgorithmBees Algorithm, THE is a popular optimisation method taking inspiration from the food foragingForaging point behaviour of honey bees. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This chapter describes in detail the Bees Algorithm and its variants. The description of the Bees AlgorithmBees Algorithm, THE is framed in the general context of parameter optimisation, highlighting the main issues and how the Bees algorithmBees Algorithm addresses them. The state-of-the-art of the empirical and theoretical understanding of the Bees Algorithm is discussed and areas of further work are suggested.
An Improved Particle-Swarm-Optimization Algorithm for a Prediction Model of Steel Slab Temperature

Aiming at the problem of the low accuracy of temperature prediction, a mathematical model for predicting the temperature of a steel billet is developed. For the process of temperature prediction, an improved particle-swarm-optimization algorithm (called XPSO) is developed. XPSO was designed based on a multiple swarm scheme to improve the global search capability and robustness; thus, it can improve the low accuracy of prediction and overcome the problem of easy entrapment into local optima. In the XPSO, the multiple swarm scheme comprises four modified components: (1) the strategy of improving the positional initialization; (2) the mutation strategy for particle swarms; (3) the adjustment strategy of inertia weights; (4) the strategy of jumping out local optima. Based on widely used unimodal, multimodal and composite benchmark functions, the effectiveness of the XPSO algorithm was verified by comparing it with some popular variant PSO algorithms (PSO, IPSO, IPSO2, HPSO, CPSO). Then, the XPSO was applied to predict the temperatures of steel billets based on simulation data sets and measured data sets. Finally, the obtained results show that the XPSO is more accurate than other PSO algorithms and other optimization approaches (WOA, IA, GWO, DE, ABC) for temperature prediction of steel billets.
Winograd schemata and other datasets for anaphora resolution in Hungarian

Abstract The Winograd Schema Challenge (WSC, proposed by Levesque, Davis &amp; Morgenstern 2012) is considered to be the novel Turing Test to examine machine intelligence. Winograd schema questions require the resolution of anaphora with the help of world knowledge and commonsense reasoning. Anaphora resolution is itself an important and difficult issue in natural language processing, therefore, many other datasets have been created to address this issue. In this paper we look into the Winograd schemata and other Winograd-like datasets and the translations of the schemata to other languages, such as Chinese, French and Portuguese. We present the Hungarian translation of the original Winograd schemata and a parallel corpus of all the translations of the schemata currently available. We also adapted some other anaphora resolution datasets to Hungarian. We aim to discuss the challenges we faced during the translation/adaption process.
Lexicon and attention-based named entity recognition for kiwifruit diseases and pests: A Deep learning approach

Named Entity Recognition (NER) is a crucial step in mining information from massive agricultural texts, which is required in the construction of many knowledge-based agricultural support systems, such as agricultural technology question answering systems. The vital domain characteristics of Chinese agricultural text cause the Chinese NER (CNER) in kiwifruit diseases and pests to suffer from the insensitivity of common word segmentation tools to kiwifruit-related texts and the feature extraction capability of the sequence encoding layer being challenged. In order to alleviate the above problems, effectively mine information from kiwifruit-related texts to provide support for agricultural support systems such as agricultural question answering systems, this study constructed a novel Chinese agricultural NER (CANER) model KIWINER by statistics-based new word detection and two novel modules, AttSoftlexicon (Criss-cross attention-based Softlexicon) and PCAT (Parallel connection criss-cross attention), proposed in this paper. Specifically, new words were detected to improve the adaptability of word segmentation tools to kiwifruit-related texts, thereby constructing a kiwifruit lexicon. The AttSoftlexicon integrates word information into the model and makes full use of the word information with the help of Criss-cross attention network (CCNet). And the PCAT improves the feature extraction ability of sequence encoding layer through CCNet and parallel connection structure. The performance of KIWINER was evaluated on four datasets, namely KIWID (Self-annotated), Boson, ClueNER, and People's Daily, which achieved optimal F1-scores of 88.94%, 85.13%, 80.52%, and 92.82%, respectively. Experimental results in many aspects illustrated that methods proposed in this paper can effectively improve the recognition effect of kiwifruit diseases and pests named entities, especially for diseases and pests with strong domain characteristics.
Optimization: A comparison of recent meta-heuristic optimization algorithms using benchmark function

Meta-heuristic optimization algorithms are used in many application areas to solve optimization problems. In recent years, meta-heuristic optimization algorithms have gained importance over deterministic search algorithms in solving optimization problems. However, none of the techniques are equally effective in solving all optimization problems. Therefore, researchers have focused on either improving current meta-heuristic optimization techniques or developing new ones. Many alternative meta-heuristic algorithms inspired by nature have been developed to solve complex optimization problems. It is important to compare the performances of the developed algorithms through statistical analysis and determine the better algorithm. This paper compares the performances of sixteen meta-heuristic optimization algorithms (AWDA, MAO, TSA, TSO, ESMA, DOA, LHHO, DSSA, LSMA, AOSMA, AGWOCS, CDDO, GEO, BES, LFD, HHO) presented in the literature between 2021 and 2022. In this context, various test functions, including single-mode, multi-mode, and fixed-size multi-mode benchmark functions, were used to evaluate the efficiency of the algorithms used.
How Partisanship Matters: A Panel Study on the Democratic Outcomes of Perceived Dirty Campaigning

Uncivil campaigning and deceitful campaign techniques are increasingly relevant phenomena in politics. However, it remains unclear how they share an underlying component and how partisanship can influence their associations with democratic outcomes. We introduce the concept of dirty campaigning, which is situated at the intersection of research on negative campaigning and political scandals. Dirty campaigning involves violations of social norms and liberal-democratic values between elite political actors in terms of style and practices, such as uncivil campaigning and deceitful campaign techniques. In a two-wave panel study (&lt;em&gt;N&lt;/em&gt; = 634) during the 2021 German federal election campaign, we investigate the associations of perceived dirty campaigning by the least and most favorite party with distrust in politicians, trust in democracy, attitudes toward dirty campaigning regulation, as well as perceived harmful consequences of dirty campaigning for democracy. We find that perceived dirty campaigning by the least favorite party increases perceptions of harmful consequences of dirty campaigning for democracy over time. In contrast, perceived dirty campaigning by the most favorite party decreases perceptions of harmful consequences of dirty campaigning for democracy as well as attitudes toward dirty campaigning regulation over time. Perceptions of harmful consequences of dirty campaigning for democracy increase distrust in politicians over time and vice versa. Our findings suggest that the outcomes of dirty campaigning can depend on partisanship and can have important implications for the quality of democracy.
Towards More Gender-Inclusive Q&amp;As

Online Question and Answer communities (Q&amp;As) are popular spaces for learning and sharing knowledge. However, prior research suggests that Q&amp;As may not be appealing to and inclusive of men and women, with absent social considerations listed as a potential contributing factor. We investigate how additional community presence information can affect users' perceptions of and engagement with a Q&amp;A for graphic design software. Through a 10-day task-based field study with 30 participants (14 women, 14 men, 2 non-binary), we uncover how community presence information can humanize the Q&amp;A and play a role in promoting an inclusive environment. On the other hand, some participants question if community presence information belongs in a Q&amp;A and describe some privacy implications. The women in our sample also talked about the importance of diverse community demographics, while we did not observe this sentiment expressed by the men. Our findings contribute an understanding of how users perceive the role of community presence information within a Q&amp;A. We also discuss how this information might impact women's future participation and engagement.
Tourgether360: Collaborative Exploration of 360° Videos using Pseudo-Spatial Navigation

Collaborative exploration of 360 videos with contemporary interfaces is challenging because collaborators do not have awareness of one another's viewing activities. Tourgether360 enhances social exploration of 360° tour videos using a pseudo-spatial navigation technique that provides both an overhead "context" view of the environment as a minimap, as well as a shared pseudo-3D environment for exploring the video. Collaborators are embodied as avatars along a track depending on their position in the video timeline and can point and synchronize their playback. We evaluated the Tourgether360 concept through two studies: first, a comparative study with a simplified version of Tourgether360 with collaborator embodiments and a minimap versus a conventional interface; second, an exploratory study where we studied how collaborators used Tourgether360 to navigate and explore 360° environments together. We found that participants adopted the Tourgether360 approach with ease and enjoyed the shared social aspects of the experience. Participants reported finding the experience similar to an interactive social video game.
User Perception of Recommendation Explanation: Are Your Explanations What Users Need?

As recommender systems become increasingly important in daily human decision-making, users are demanding convincing explanations to understand why they get the specific recommendation results. Although a number of explainable recommender systems have recently been proposed, there still lacks an understanding of what users really need in a recommendation explanation. The actual reason behind users’ intention to examine and consume (e.g., click and watch a movie) can be the window to answer this question and is named as self-explanation in this work. In addition, humans usually make recommendations accompanied by explanations, but there remain fewer studies on how humans explain and what we can learn from human-generated explanations. To investigate these questions, we conduct a novel multi-role, multi-session user study in which users interact with multiple types of system-generated explanations as well as human-generated explanations, namely peer-explanation . During the study, users’ intentions, expectations, and experiences are tracked in several phases, including before and after the users are presented with an explanation and after the content is examined. Through comprehensive investigations, three main findings have been made: First, we observe not only the positive but also the negative effects of explanations, and the impact varies across different types of explanations. Moreover, human-generated explanation, peer-explanation , performs better in increasing user intentions and helping users to better construct preferences, which results in better user satisfaction. Second, based on users’ self-explanation , the information accuracy is measured and found to be a major factor associated with user satisfaction. Some other factors, such as unfamiliarity and similarity, are also discovered and summarized. Third, through annotations of the information aspects used in the human-generated self-explanation and peer-explanation , patterns of how humans explain are investigated, including what information and how much information is utilized. In addition, based on the findings, a human-inspired explanation approach is proposed and found to increase user satisfaction, revealing the potential improvement of further incorporating more human patterns in recommendation explanations. These findings have shed light on the deeper understanding of the recommendation explanation and further research on its evaluation and generation. Furthermore, the collected data, including human-generated explanations by both the external peers and the users’ selves, will be released to support future research works on explanation evaluation.
Building User-oriented Personalized Machine Translator based on User-Generated Textual Content

Machine Translation (MT) has been a very useful tool to assist multilingual communication and collaboration. In recent years, by taking advantage of the exciting developments of neural networks and deep learning, the accuracy and speed of machine translation have been continuously improved. However, most machine translation methods and systems are data-driven. They tend to select a consensus response represented in training data, while a user's preferred linguistic style, which is important for translation comprehension and user experience, is ignored. For this problem, we aim to build a user-oriented personalized machine translation model in this paper. The model aims to learn each user's linguistic style from the textual content that is generated by her/him (User-Generated Textual Content, UGTC) in social media context and generate personalized translation results utilizing several state-of-the-art deep learning techniques like Transformer and pre-training. We also implemented a user-oriented personalized machine translator using Weibo as a case of the source of UGTC to provide a systematical implementation scheme of a user-oriented personalized machine translation system based on our model. The translator was evaluated by automatic evaluation in combination with human evaluation. The results suggest that our model can generate more personalized, natural and lively translation results and enhance the comprehensibility of translation results, which makes its generations more preferred by users versus general translation results.
A Probabilistic Formalisation of Contextual Bias: from Forensic Analysis to Systemic Bias in the Criminal Justice System

Abstract Researchers have found evidence of contextual bias in forensic science, but the discussion of contextual bias is currently qualitative. We formalise existing empirical research and show quantitatively how biases can be propagated throughout the legal system, all the way up to the final determination of guilt in a criminal trial. We provide a probabilistic framework for describing how information is updated in a forensic analysis setting by using the ratio form of Bayes’ rule. We analyse results from empirical studies using this framework and employ simulations to demonstrate how bias can be compounded where experiments do not exist. We find that even minor biases in the earlier stages of forensic analysis can lead to large, compounded biases in the final determination of guilt in a criminal trial.
"With a hint she will remember": Collaborative Storytelling and Culture Sharing between Immigrant Grandparents and Grandchildren Via Magic Thing Designs

The timeless social activity of passing down oral stories preserves family memory, identity, values, and culture. Existing tools for family memories often take a techno-determinist approach by focusing on the mechanics of connecting families and the resulting documentation, rather than the social process of sharing stories and morals, and largely without considering the specific needs of immigrant families. For immigrant families, cultural exchange, particularly crucial across grandparent and grandchild generations, is threatened by the language and cultural barriers emerging from displacement and migration. As a result, immigrant grandparents and their young grandchildren struggle with fostering social kinship, leading to social disconnect and loss of cultural heritage. In our research, we collaborate with multi-generational and culturally-at-risk immigrant families through Participatory Design activities towards the design of reminiscence tools that support their needs focusing on language and cultural connection. We report on the designs created by families and propose design guidelines supporting cultural resilience, focusing on flexible, visual storytelling.
A Decision Tree-Based Online Traffic Classification Method for QoS Routing in Data Center Networks

In data center networks, how to ensure the quality of service (QoS) of traffic effectively has become an important optimization goal. Traffic classification has been widely studied to guarantee network flow QoS since it provides a basis for the network performance level requirement of different flow types. However, traditional network traffic classification methods, which can only identify traffic type offline, have no ability for online classification and the in-time QoS guarantee. To solve this problem, we propose an online traffic classification for QoS routing (OCQR) framework based on programmable data plane in this paper. Firstly, in order to improve the accuracy of flow classification and reduce the impact on line-rate packet forwarding, the way of machine learning model offline training and online deployment is used to classify the network flow in real time. Based on the hardware foundation of programmable switch and the complexity of machine learning algorithm, a CART decision tree model is used to classify network flows. Secondly, in order to actually deploy and run the decision tree model in the programmable switch with limited computing and storage resources, we prune the decision tree model and optimize the leaf nodes to reduce the size of the decision tree and improve the classification recall rate. Finally, by building a P4 simulation environment, the recall rate of OCQR’s classified scheduling of some application flows reaches more than 98%, which is consistent with the evaluation results of the offline dataset. At the same time, compared with the classification method based on SDN, OCQR does not require additional bandwidth occupation, so it has less impact on network performance.
The role of data sharing in survey dropout: a study among scientists as respondents

Purpose One of the currently debated changes in scientific practice is the implementation of data sharing requirements for peer-reviewed publication to increase transparency and intersubjective verifiability of results. However, it seems that data sharing is a not fully adopted behavior among researchers. The theory of planned behavior was repeatedly applied to explain drivers of data sharing from the perspective of data donors (researchers). However, data sharing can be viewed from another perspective as well: survey participants. The research questions (RQs) for this study were as follows: 1 Does data sharing increase participant's nonresponse? 2 Does data sharing influence participant's response behavior? The purpose of this paper is to address these issues. Design/methodology/approach To answer the RQs, a mixed methods approach was applied, consisting of a qualitative prestudy and a quantitative survey including an experimental component. The latter was a two-group setup with an intervention group (A) and a control group (B). A list-based recruiting of members of the Medical Faculty of the University of Freiburg was applied for 15 days. For exploratory data analysis of dropouts and nonresponse, we used Fisher's exact tests and binary logistic regressions. Findings In sum, we recorded 197 cases for Group A and 198 cases for Group B. We found no systematic group differences regarding response bias or dropout. Furthermore, we gained insights into the experiences our sample made with data sharing: half of our sample already requested data of other researchers or shared data on request of other researchers. Data repositories, however, were used less frequently: 28% of our respondents used data from repositories and 19% stored data in a repository. Originality/value To the authors’ knowledge, their study is the first study that includes researchers as survey subjects investigating the effect of data sharing on their response patterns.