Reviewerly/exHarmony huggingface dataset:
Lightweight spatial-channel adaptive coordination of multilevel refinement enhancement network for image reconstruction

Benefiting from the vigorous development of deep learning, many CNN-based image super-resolution methods have emerged and achieved better results than traditional algorithms. However, it is difficult for most algorithms to adaptively adjust the spatial region and channel features at the same time, let alone the information exchange between them. In addition, the exchange of information between attention modules is even less visible to researchers. To solve these problems, we put forward a lightweight spatial-channel adaptive coordination of multilevel refinement enhancement networks(MREN). Specifically, we construct a space-channel adaptive coordination block, which enables the network to learn the spatial region and channel feature information of interest under different receptive fields. In addition, the information of the corresponding feature processing level between the spatial part and the channel part is exchanged with the help of jump connection to achieve the coordination between the two. We establish a communication bridge between attention modules through a simple linear combination operation, so as to more accurately and continuously guide the network to pay attention to the information of interest. Extensive experiments on several standard test sets have shown that our MREN achieves superior performance over other advanced algorithms with a very small number of parameters and very low computational complexity.
A neural network based framework for effective laparoscopic video quality assessment

Video quality assessment is a challenging problem having a critical significance in the context of medical imaging. For instance, in laparoscopic surgery, the acquired video data suffers from different kinds of distortion that not only hinder surgery performance but also affect the execution of subsequent tasks in surgical navigation and robotic surgeries. For this reason, we propose in this paper neural network-based approaches for distortion classification as well as quality prediction. More precisely, a Residual Network (ResNet) based approach is firstly developed for simultaneous ranking and classification task. Then, this architecture is extended to make it appropriate for the quality prediction task by using an additional Fully Connected Neural Network (FCNN). To train the overall architecture (ResNet and FCNN models), transfer learning and end-to-end learning approaches are investigated. Experimental results, carried out on a new laparoscopic video quality database, have shown the efficiency of the proposed methods compared to recent conventional and deep learning based approaches.
NABILD: Noise And Blur Invariant Local Descriptor for Face Recognition

Abstract Most of the existing local descriptors unable to perform well in front of noise and blur variations. Additionally there are very less descriptors persist in literature which are noise and blur invariant. To remedy this challenge the proposed work launch novel descriptor under noise and blur changes so-called Noise and Blur Invariant Local Descriptor (NABILD). With respect to two artificial noises i.e. Gaussian White Noise (GWN) and Salt &amp; Pepper Noise (SPN) with artificial image blurring, the NABILD is introduced. Precisely NABILD takes essentials of two well performed descriptors with respect to noise and blur variations. The first one is Median Robust Extended LBP based on Neighborhood Intensity (MRELBP-NI) and second one is Multiscale Local Phase Quantization (MLPQ). MRELBP-NI is very effective in controlling GWN and SPN due to the capturing of microstructure and macrostructure information. LPQ is the efficient blur invariant descriptor as it quantizes the phase. By considering the merits of both of these descriptors their features are integrated into one framework called as NABILD. To lower down the feature dimension FLDA is deployed and classification is conducted by SVMs. Experiments on ORL face dataset confirm strength of NABILD against other tested descriptors in noise and blur variations. Various literature methods are also outclassed by NABILD.
OCFSP: self-supervised one-class classification approach using feature-slide prediction subtask for feature data

One-class classification (OCC) is a machine learning problem where training data has only one class. Recently, self-supervised OCC algorithms have been increasing attention. These algorithms train the model for pretext tasks and use the model error for OCC. However, these tasks are specialized for images, and applying them to feature data is not practical or appropriate for such a purpose. The motivation of this study is to apply self-supervised OCC to feature data. For this purpose, this paper proposes an OCC approach using feature-slide prediction (FSP) subtask for feature data (OCFSP). The main originality is the FSP subtask, which is the first classification subtask for feature data. In particular, the proposed method creates a self-labeled dataset by generating additional feature vectors with the feature slide of original vectors and self-annotating these vectors as the number of the slides. Such a dataset is applied to train a multi-class classifier to predict the number of feature slides. Since this classification model learns data from only one class, the FSP accuracy for a seen class is higher relative to unseen classes. Accordingly, OCC could be made using the accuracy of FSP. The proposed methods are experimented with using the imbalanced-learn, covtype, and kddcup datasets. OCFSP shows fair accuracy where few training data is given. In addition, classification subtask for feature data shows a relatively fast testing speed, unlike image data. Therefore, the bottleneck of the self-supervised approach is considered the memory size, which is the main difference between image and feature data. Source code is uploaded at https://github.com/ToshiHayashi/OCFSP
Software Quality: How Much Does It Matter?

Interconnected computers and software systems have become an indispensable part of people’s lives in the period of digital transformation. Consequently, software quality research is becoming more and more critical. There have been multiple attempts to synthesise knowledge gained in software quality research; however, they were focused mainly on single aspects of software quality and did not structure the knowledge holistically. To fill this gap, we harvested software quality publications indexed in the Scopus bibliographic database. We analysed them using synthetic content analysis which is a triangulation of bibliometrics and content analysis. The search resulted in 15,468 publications. The performance bibliometric analysis showed that the production of research publications relating to software quality is currently following an exponential growth trend and that the software quality research community is growing. The most productive country was the United States, followed by China. The synthetic content analysis revealed that the published knowledge could be structured into six themes, the most important being the themes regarding software quality improvement by enhancing software engineering, advanced software testing and improved defect and fault prediction with machine learning and data mining.
A Closer Look into Collaborative Publishing at Software-Engineering Conferences

Computer science and particularly software engineering is a rapidly evolving research discipline increasingly conducted by large, collaborative teams. Unfortunately, there is little research on the underlying publication activity and collaboration patterns in software engineering. To address this gap, we study two properties of research collaborations in software engineering: the number of collaborators (i.e., authors of a paper) and their academic age (i.e., their experience of working in research). More precisely, we investigate collaborations for papers published at all main tracks of three top-level software-engineering conferences (i.e., ASE, ESEC/FSE, ICSE) and one top-level reference conference (i.e., JCDL), including a total of 5,188 papers and the corresponding 8,730 unique authors. Our results indicate that collaboration is more prevalent now than ever before, with a decline in the proportion of researchers who contribute single-author papers. Moreover, our analysis revealed that the ideal team size seems to range from two to four researchers, and that junior researchers seem to need the support of more experienced co-authors to get published at such top-level conferences. Ultimately, our goal is to understand how collaborations in software engineering have evolved and impact different researchers (e.g., newcomers, juniors), helping to highlight potential impediments and consequent improvements regarding the quality of research, collaborations, and mentoring.
Searching Wartime Photograph Archive for Serious Leisure Purposes

A survey of image information search goals, search tactics and what was looked at in the images was circulated among serious-leisure war historians. Also, we used the user engagement scale (UES), and a user success score were measured. Search goals included general and more specific goals. It is shown that despite the lack of full coverage of descriptions and metadata, users preferred mostly keyword searching. Surprisingly, general targets were popularly looked at the images, such as milieu or scenery. Participants gave high UES and varying success scores, but they were, in general, satisfied with their interactions. Serious leisure users are very engaged with their activities, which makes them tolerant to the possible system and indexing shortages. We discuss the findings and suggest research implications based on them.
Automatic Knowledge Extraction from a Digital Library and Collaborative Validation

The automatic extraction of knowledge from a Digital Library is a crucial task in the world of Digital Humanities by enabling the discovery of information on a scale that is not achievable by human experts alone. However, the automation of information extraction processes brings the typical problems of a fully automated process, namely data quality and explainability of the results. This paper proposes a system that allows domain experts to collaboratively validate information previously automatically extracted from a Digital Library (DL), supporting an incremental data quality improvement approach, specifically through entity linking. Furthermore, rather than seeing just the results of the extraction process, the domain experts can trace the origin of where the AI recognized a specific entity (i.e. a "snippet" of text or an image). In order to allow the domain experts to contextualize the information they need to validate (i.e. topics, descriptions, etc.) leveraging the Knowledge Graph potential, in the proposed use case, the validation is integrated into the interface designated for the DL semantic exploration.
Dual-Channel Interactive Graph Convolutional Networks for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis aims to identify the sentiment polarity of one or more aspect terms in a sentence. At present, many researchers have applied dependency trees and graph neural networks (GNNs) to aspect-level sentiment analysis and achieved promising results. However, when a sentence contains multiple aspects, most methods model each aspect independently, ignoring the issue of sentiment connection between aspects. To address this problem, this paper proposes a dual-channel interactive graph convolutional network (DC-GCN) model for aspect-level sentiment analysis. The model considers both syntactic structure information and multi-aspect sentiment dependencies in sentences and employs graph convolutional networks (GCN) to learn its node information representation. Particularly, to better capture the representations of aspect and opinion words, we exploit the attention mechanism to interactively learn the syntactic information features and multi-aspect sentiment dependency features produced by the GCN. In addition, we construct the word embedding layer by the BERT pre-training model to better learn the contextual semantic information of sentences. The experimental results on the restaurant, laptop, and twitter datasets show that, compared with the state-of-the-art model, the accuracy is up to 1.86%, 2.50, 1.36%, and 0.38 and the Macro-F1 values are up to 1.93%, 0.61%, and 0.4%, respectively.
The Use of Virtual Reality Technology in the Treatment of Psychopathological Disorders

Jaron Lanier proposed the name "Virtual Reality" to refer to interactive simulations produced through the use of computer technology, although the idea was formulated in the sixties by a pioneer of computer graphics, Ivan Sutherland [...].
Multi-Agent Multi-View Collaborative Perception Based on Semi-Supervised Online Evolutive Learning

In the edge intelligence environment, multiple sensing devices perceive and recognize the current scene in real time to provide specific user services. However, the generalizability of the fixed recognition model will gradually weaken due to the time-varying perception scene. To ensure the stability of the perception and recognition service, each edge model/agent needs to continuously learn from the new perception data unassisted to adapt to the perception environment changes and jointly build the online evolutive learning (OEL) system. The generalization degradation problem can be addressed by deploying the semi-supervised learning (SSL) method on multi-view agents and continuously tuning each discriminative model by collaborative perception. This paper proposes a multi-view agent's collaborative perception (MACP) semi-supervised online evolutive learning method. First, each view model will be initialized based on self-supervised learning methods, and each initialized model can learn differentiated feature-extraction patterns with certain discriminative independence. Then, through the discriminative information fusion of multi-view model predictions on the unlabeled perceptual data, reliable pseudo-labels are obtained for the consistency regularization process of SSL. Moreover, we introduce additional critical parameter constraints to continuously improve the discriminative independence of each view model during training. We compare our method with multiple representative multi-model and single-model SSL methods on various benchmarks. Experimental results show the superiority of the MACP in terms of convergence efficiency and performance. Meanwhile, we construct an ideal multi-view experiment to demonstrate the application potential of MACP in practical perception scenarios.
Towards Robustifying Image Classifiers against the Perils of Adversarial Attacks on Artificial Intelligence Systems

Adversarial machine learning (AML) is a class of data manipulation techniques that cause alterations in the behavior of artificial intelligence (AI) systems while going unnoticed by humans. These alterations can cause serious vulnerabilities to mission-critical AI-enabled applications. This work introduces an AI architecture augmented with adversarial examples and defense algorithms to safeguard, secure, and make more reliable AI systems. This can be conducted by robustifying deep neural network (DNN) classifiers and explicitly focusing on the specific case of convolutional neural networks (CNNs) used in non-trivial manufacturing environments prone to noise, vibrations, and errors when capturing and transferring data. The proposed architecture enables the imitation of the interplay between the attacker and a defender based on the deployment and cross-evaluation of adversarial and defense strategies. The AI architecture enables (i) the creation and usage of adversarial examples in the training process, which robustify the accuracy of CNNs, (ii) the evaluation of defense algorithms to recover the classifiers' accuracy, and (iii) the provision of a multiclass discriminator to distinguish and report on non-attacked and attacked data. The experimental results show promising results in a hybrid solution combining the defense algorithms and the multiclass discriminator in an effort to revitalize the attacked base models and robustify the DNN classifiers. The proposed architecture is ratified in the context of a real manufacturing environment utilizing datasets stemming from the actual production lines.
Low-Light Image Enhancement Using Hybrid Deep-Learning and Mixed-Norm Loss Functions

This study introduces a low-light image enhancement method using a hybrid deep-learning network and mixed-norm loss functions, in which the network consists of a decomposition-net, illuminance enhance-net, and chroma-net. To consider the correlation between R, G, and B channels, YCbCr channels converted from the RGB channels are used for training and restoration processes. With the luminance, the decomposition-net aims to decouple the reflectance and illuminance and to train the reflectance, leading to a more accurate feature map with noise reduction. The illumination enhance-net connected to the decomposition-net is used to enhance the illumination such that the illuminance is improved with reduced halo artifacts. In addition, the chroma-net is independently used to reduce color distortion. Moreover, a mixed-norm loss function used in the training process of each network is described to increase the stability and remove blurring in the reconstructed image by reflecting the properties of reflectance, illuminance, and chroma. The experimental results demonstrate that the proposed method leads to promising subjective and objective improvements over state-of-the-art deep-learning methods.
Significance of Preprocessing Techniques on Text Classification Over Hindi and English Short Texts

Good quality text data gives a better interpretation of results and provides efficient learning models. The real-world text data comes with many irregularities and errors that need good preprocessing strategies to get quality text corpus. The preprocessing steps before performing text classification of documents involve various steps of transformation and manipulation of the documents. The proposed preprocessing framework combines all the best preprocessing techniques for both the languages Hindi and English. This study will look at the significance of preprocessing methods on text classification from different perspectives including classification accuracy, text language, and feature selection. Experiments on Tweets, Movie Reviews, and Product Reviews datasets reveal that, depending on the domain and language under consideration, selecting optimal pairings of preprocessing tasks rather than allowing or deleting them fully can significantly improve classification accuracy. Some preprocessing step is useful in Hindi language texts but not in English language texts, according to our findings.
Using Systemised Nomenclature of Medicine (SNOMED) codes to select digital pathology whole slide images for long-term archiving

The archiving of whole slide images represents a hurdle to digital pathology implementation largely because of the amount of data generated. The retention of glass slides is currently recommended for a minimum of 10 years, but it is for individual departments to determine how digital images are archived and for how long. In a retrospective study, we examined the combination of Systemised Nomenclature of Medicine (SNOMED) codes allocated to cases reported between July 2011 and December 2015 and recalled more than 12 months after diagnosis in comparison to non-recalled cases.Our results show that 0.2% of cases are recalled after 12 months, and SNOMED code combinations can be used to identify which cases are likely to be recalled and which are not. This approach could reduce the number of cases archived by 62% and still ensure all cases likely to be recalled remain in the archive.
Self-supervised Depth Estimation in Laparoscopic Image Using 3D Geometric Consistency

Depth estimation is a crucial step for image-guided intervention in robotic surgery and laparoscopic imaging system. Since per-pixel depth ground truth is difficult to acquire for laparoscopic image data, it is rarely possible to apply supervised depth estimation to surgical applications. As an alternative, self-supervised methods have been introduced to train depth estimators using only synchronized stereo image pairs. However, most recent work focused on the left-right consistency in 2D and ignored valuable inherent 3D information on the object in real world coordinates, meaning that the left-right 3D geometric structural consistency is not fully utilized. To overcome this limitation, we present M3Depth, a self-supervised depth estimator to leverage 3D geometric structural information hidden in stereo pairs while keeping monocular inference. The method also removes the influence of border regions unseen in at least one of the stereo images via masking, to enhance the correspondences between left and right images in overlapping areas. Extensive experiments show that our method outperforms previous self-supervised approaches on both a public dataset and a newly acquired dataset by a large margin, indicating a good generalization across different samples and laparoscopes.
Ontology Reuse: The Real Test of Ontological Design

Reusing ontologies in practice is still very challenging, especially when multiple ontologies are (jointly) involved. Moreover, despite recent advances, the realization of systematic ontology quality assurance remains a difficult problem. In this work, the quality of thirty biomedical ontologies, and the Computer Science Ontology are investigated, from the perspective of a practical use case. Special scrutiny is given to cross-ontology references, which are vital for combining ontologies. Diverse methods to detect potential issues are proposed, including natural language processing and network analysis. Moreover, several suggestions for improving ontologies and their quality assurance processes are presented. It is argued that while the advancing automatic tools for ontology quality assurance are crucial for ontology improvement, they will not solve the problem entirely. It is ontology reuse that is the ultimate method for continuously verifying and improving ontology quality, as well as for guiding its future development. Specifically, multiple issues can be found and fixed primarily through practical and diverse ontology reuse scenarios.
LightGBM using Enhanced and De-biased Item Representation for Better Session-based Fashion Recommender Systems

In this paper, we present our 5th place solution for the ACM RecSys 2022 challenge (http://www.recsyschallenge.com/2022/).The competition, organized by Dressipi, aims to predict the fashion item purchasing actions on a public dataset of 1.1 million online retail sessions. In the traditional sequence recommendation model, we mainly utilize the action sequence information to model the representations of items and users. However, the fashion categories and features of items change much more frequently in our task, and the bias caused by the popularity will greatly affect the representations learning for the users and items. In this work, our team, termed THLUO, devise a model, which injects the spatiotemporal features of each item in sessions to alleviate the bias problem and capture the latest fashion trend information hiding in the session. In more detail, we proposed a two stages model, which includes retrieval and re-ranking. In the retrieval stage, we adapt the positions and timestamp features into the item-CF model to eliminate the bias caused by the popularity. In the re-ranking stage, we not only adapt traditional feature engineering but also used the enhanced features created by neural net works and fusion them as inputs of LightGBM for final prediction. After careful experiments, our model’s result archive an outstanding score of 0.2062 in mean reciprocal rank metrics in the test dataset, finally ranked fifth in the competition.