Reviewerly/exHarmony huggingface dataset:
GALA: Toward Geometry-and-Lighting-Aware Object Search for Compositing

Compositing-aware object search aims to find the most compatible objects for compositing given a background image and a query bounding box. Previous works focus on learning compatibility between the foreground object and background, but fail to learn other important factors from large-scale data, i.e. geometry and lighting. To move a step further, this paper proposes GALA (Geometry-and-Lighting-Aware), a generic foreground object search method with discriminative modeling on geometry and lighting compatibility for open-world image compositing. Remarkably, it achieves state-of-the-art results on the CAIS dataset and generalizes well on large-scale open-world datasets, i.e. Pixabay and Open Images. In addition, our method can effectively handle non-box scenarios, where users only provide background images without any input bounding box. Experiments are conducted on real-world images to showcase applications of the proposed method for compositing-aware search and automatic location/scale prediction for the foreground object.
ECO-TR: Efficient Correspondences Finding via Coarse-to-Fine Refinement

Modeling sparse and dense image matching within a unified functional correspondence model has recently attracted increasing research interest. However, existing efforts mainly focus on improving matching accuracy while ignoring its efficiency, which is crucial for real-world applications. In this paper, we propose an efficient structure named Efficient Correspondence Transformer ( $${\textbf {ECO-TR}}$$ ) by finding correspondences in a coarse-to-fine manner, which significantly improves the efficiency of functional correspondence model. To achieve this, multiple transformer blocks are stage-wisely connected to gradually refine the predicted coordinates upon a shared multi-scale feature extraction network. Given a pair of images and for arbitrary query coordinates, all the correspondences are predicted within a single feed-forward pass. We further propose an adaptive query-clustering strategy and an uncertainty-based outlier detection module to cooperate with the proposed framework for faster and better predictions. Experiments on various sparse and dense matching tasks demonstrate the superiority of our method in both efficiency and effectiveness against existing state-of-the-arts. Project page: https://dltan7.github.io/ecotr/ .
A Comparison of ASP-Based and SAT-Based Algorithms for the Contension Inconsistency Measure

We propose an algorithm based on satisfiability problem (SAT) solving for determining the contension inconsistency degree in propositional knowledge bases. In addition, we present a revised version of an algorithm based on answer set programming (ASP), which serves the same purpose. In an experimental analysis, we compare the two algorithms to each other, as well as to a naive baseline method. Our results demonstrate that both the SAT and the ASP approach expectedly outperform the baseline algorithm. Further, the revised ASP method is not only superior to the SAT approach, but also to its predecessors from the literature. Hence, it poses a new state of the art.
A Divide &amp; Concur Approach to Collaborative Goal Modeling with Merge in Early-RE

Goal modeling enables the elicitation of stakeholders’ intentionality in the earlier stages of a project. Often, approaches are limited by the effort required to create an initial goal model. In this paper, we investigate the problem of model merging for Tropos goal models. Specifically, we propose a formal approach to the problem of automatically merging the attributes of intentions and actors, once these elements have been matched. Additionally, recent approaches have investigated answering questions about future evolutions of stakeholders’ projects with goal models. In this work we consider both static models, as well as those with timing information, using the principles of gullibility, contradiction, and consensus. We study our implementation and validate the merge operation on a variety of models from the literature.
Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain

Shortcut learning occurs when a deep neural network overly relies on spurious correlations in the training dataset in order to solve downstream tasks. Prior works have shown how this impairs the compositional generalization capability of deep learning models. To address this problem, we propose a novel approach to mitigate shortcut learning in uncontrolled target domains. Our approach extends the training set with an additional dataset (the source domain), which is specifically designed to facilitate learning independent representations of basic visual factors. We benchmark our idea on synthetic target domains where we explicitly control shortcut opportunities as well as real-world target domains. Furthermore, we analyze the effect of different specifications of the source domain and the network architecture on compositional generalization. Our main finding is that leveraging data from a source domain is an effective way to mitigate shortcut learning. By promoting independence across different factors of variation in the learned representations, networks can learn to consider only predictive factors and ignore potential shortcut factors during inference.
Comparison of Automatic Modulation Classification Techniques

The advancement of digital communication and technology triggered new challenges related to the channel and radio spectrum utilization. From the other hand, real-time communications are keen of time where requests need to be processed in very short time. Automatic modulation is one of promising approaches that relies on pretrained classifiers in order to recognize the type of modulation techniques used by the transmitter. Considering that noise is dominating between the transmitter and receiver, the task of automatic modulation classification is become harder. Noise is destroying the obvious features of the signals and degrade the classification accuracy. The modulation identification technique is made to recognize the type of modulation using the deep learning technology. This paper is listing the common stat of the arts used in automatic modulation classification along with their performance measures. It was realized that deep learning classifier manifested in Conventional Neural Network (CNN) is outperformed in AMC scoring of 85.41 % of recognition accuracy.
Deep Learning Based Sentiment Analysis from Bangla Text Using Glove Word Embedding along with Convolutional Neural Network

Language is the primary means by which humans interact with each other and express their emotions. Despite the fact that teaching robots to understand textual meaning is challenging, it is not entirely impossible because of Natural Language Processing (NLP), a branch of machine learning (ML) that allows for the analysis of the meaning of spoken or written words. The field of ML has evolved quite a bit in English. Even though English-based NLP systems have been extremely successful in many fields, other languages are also being used for this crucial function. In contrast, hardly much work has been done on Bengali Text. This study was based on several Bangla texts that were categorized into three: positive, negative, and neutral. Glove word embedding, Adam optimizer, and CNN's deep learning classifier along with Glove-BiLSTM. Glove-CNN were utilized to assure an accurate result from the data obtained, and GLove+CNN acquired an accuracy of 99.43%.
A Spatio- Temporal Attentive Network for Video-Based Crowd Counting

Automatic people counting from images has recently drawn attention for urban monitoring in modern Smart Cities due to the ubiquity of surveillance camera networks. Current computer vision techniques rely on deep learning-based algorithms that estimate pedestrian densities in still, individual images. Only a bunch of works take advantage of temporal consistency in video sequences. In this work, we propose a spatio-temporal attentive neural network to estimate the number of pedestrians from surveillance videos. By taking advantage of the temporal correlation between consecutive frames, we lowered state-of-the-art count error by 5% and localization error by 7.5% on the widely-used FDST benchmark.
Clean Architecture Implementation Impacts on Maintainability Aspect for Backend System Code Base

The rapid growth of backend technology correlates positively with the number of problems that arise. One of the issues is the negligence of following an ideal backend system architecture. It causes various problems such as huge numbers of code duplication in many locations of the backend system codebase leading to a low maintainability level. This research aims to investigate the impacts of applying clean architecture on the backend codebase by creating several layers in the codebase according to the business logic hierarchy. To analyze the impacts, this research utilizes several metrics such as Cyclomatic Complexity, Weighted Method Count, Kan's Defects, Halstead's score, and Maintainability Index. The results showed that all maintainability metric scores improved after refactoring a selected backend codebase by applying the clean architecture principle. The improvements range from 21% to 61% for various maintainability metrics. This study validates that the implementation of a clean architecture in the backend codebase could increase the maintainability, reduce its complexity, and reduce developer effort to modify the codebase.
Uncertainty Learning in Kernel Estimation for Multi-stage Blind Image Super-Resolution

Conventional wisdom in blind super-resolution (SR) first estimates the unknown degradation from the low-resolution image and then exploits the degradation information for image reconstruction. Such sequential approaches suffer from two fundamental weaknesses - i.e., the lack of robustness (the performance drops when the estimated degradation is inaccurate) and the lack of transparency (network architectures are heuristic without incorporating domain knowledge). To address these issues, we propose a joint Maximum a Posteriori (MAP) approach for estimating the unknown kernel and high-resolution image simultaneously. Our method first introduces uncertainty learning in the latent space when estimating the blur kernel, aiming at improving the robustness to the estimation error. Then we propose a novel SR network by unfolding the joint MAP estimator with a learned Laplacian Scale Mixture (LSM) prior and the estimated kernel. We have also developed a novel approach of estimating both the scale prior coefficient and the local means of the LSM model through a deep convolutional neural network (DCNN). All parameters of the MAP estimation algorithm and the DCNN parameters are jointly optimized through end-to-end training. Extensive experiments on both synthetic and real-world images show that our method achieves state-of-the-art performance for the task of blind image SR.
Automatic Assessment of English CEFR Levels Using BERT Embeddings

The automatic assessment of language learners’ competences represents an increasingly promising task thanks to recent developments in NLP and deep learning technologies. In this paper, we propose the use of neural models for classifying English written exams into one of the CEFR competence levels. We employ pre-trained BERT models which provide efficient and rapid language processing on account of attention-based mechanisms and the capacity of capturing long-range sequence features. In particular, we investigate on augmenting the original learner’s text with corrections provided by an automatic tool or by human evaluators. We consider different architectures where the texts and corrections are combined at an early stage, via concatenation before the BERT network, or as late fusion of the BERT embeddings. The proposed approach is evaluated on two open-source datasets: the EFCAMDAT and the CLC-FCE. The experimental results show that the proposed approach can predict the learner’s competence level with remarkably high accuracy, in particular when large labelled corpora are available. In addition, we observed that augmenting the input text with corrections provides further improvement in the automatic language assessment task.
Digital citizenship in primary education: A systematic literature review describing how it is implemented

Digital citizenship (DC) has been introduced to the curriculum at Swiss primary schools, a fact that underscores the importance digital skills have for citizens of the future. Already prior to this development, an increasing number of scholarly publications on the concept appeared. In this project, a systematic literature review was conducted to identify how DC has been operationalized in teaching practices to date. The findings reveal that DC is often used as an umbrella term for technology-related learning tasks that nevertheless fail to actively engage students in practicing DC in a critical manner. In addition, older students are often asked to develop DC in project-based learning outside the school setting. While interdisciplinary approaches to DC have been proposed, disciplines such as philosophy or ecology—apparently necessary to work on citizenship—are not considered. Finally, digital tools/environments are never questioned and always imposed; as such, a critical approach to DC is not explicitly developed in the classroom. This literature review discusses the practical implications of the current state of research.
Indonesian Sentiment Analysis Model from Social Media by Stacking BERT and BI-LSTM

Social media have been a public place where people can have social interaction with each other. They can freely express their words on social media. This right is also known as freedom of speech. However, because not many people can effectively use their right to free speech, many feel they can say anything on the internet, mainly on social media. Hence, social media can be a platform to capture public sentiment toward a particular topic. Automatic sentiment analysis using machine learning can take part in capturing public sentiment. However, automatic sentiment analysis is not a straightforward task. There are several problems to be solved in the automatic sentiment analysis task. Firstly, words can be ambiguous, in particular in several languages. Secondly, the context can be different from one language to another language. Therefore, using another language model to train automatic sentiment analysis tasks in different languages is not practical. This research intends to integrate BERT(Bidirectional Encoder Representations from Transformers) and Bi-LSTM(Bidirectional Long Short-Term Memory) models for text classification since they are the two most commonly used models for text classification. The models proposed in this research focus on a specific language (i.e. Indonesian). The results demonstrate that the model trained with Bidirectional Encoder Representations from Transformers (BERT) architecture with indobertweet-base-uncased pre-trained model combined with Bi-directional Long Short Term Memory (BiLSTM) achieved the best results with 95.17%, 70.25% and 69.09% for training accuracy, validation accuracy and testing accuracy respectively, albeit the model is over-fitted.
Password Strength Analysis and its Classification by Applying Machine Learning Based Techniques

Passwords serve as a vital authentication mechanism for any system’s protection. Although numerous more secure methods of authentication exists such as biometrics and smart cards but still the largely popular technique is password authentication for ensuring protection of any system on account of its ease of implementation. But, passwords are exposed to various types of vulnerabilities due to the predictable patterns which human employs for setting passwords likely dictionary words, commonly used people and places names, keyboards patterns, date of birth, known phrases etc. and thereby, pave a route for online or offline muggers to intrude into the system by assuming the passwords. For guessing the passwords, various password cracking tools are available either online or offline and most of the tools can readily crack such accounts possessing passwords with weak strengths or passwords with common patterns. Therefore, the organizations should enforce unassailable strategies governing the use of strong passwords such that those vulnerabilities can be addressed. Hence, the most efficient way to protect the system against these online and offline attacks is by enforcing the users to implement a strong password. In this proposed work, prediction of the strength of a password has been modeled as a classification task with the aid of multiple supervised machine learning algorithms which were used for the learning purpose. The novelty of this work lies in the fact, that two new algorithms namely, XGBoost and Multilayer Perceptron have been implemented to determine the strength and the corresponding category of a password which has not been evaluated in similar other works. However, during testing the models with the test dataset, it has been observed that XGBoost outperformed the other machine learning classifiers with an accuracy of 99%. The final results reveal that machine learning approaches possesses ample capability to classify the passwords into different categories, namely, Weak, Medium and Strong passwords.
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-Tailed Learning

Long-tailed learning aims to tackle the crucial challenge that head classes dominate the training procedure under severe class imbalance in real-world scenarios. However, little attention has been given to how to quantify the dominance severity of head classes in the representation space. Motivated by this, we generalize the cosine-based classifiers to a von Mises-Fisher (vMF) mixture model, denoted as vMF classifier, which enables to quantitatively measure representation quality upon the hyper-sphere space via calculating distribution overlap coefficient. To our knowledge, this is the first work to measure representation quality of classifiers and features from the perspective of distribution overlap coefficient. On top of it, we formulate the inter-class discrepancy and class-feature consistency loss terms to alleviate the interference among the classifier weights and align features with classifier weights. Furthermore, a novel post-training calibration algorithm is devised to zero-costly boost the performance via inter-class overlap coefficients. Our method outperforms previous work with a large margin and achieves state-of-the-art performance on long-tailed image classification, semantic segmentation, and instance segmentation tasks (e.g., we achieve 55.0% overall accuracy with ResNetXt-50 in ImageNet-LT). Our code is available at https://github.com/VipaiLab/vMF_OP.
Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images

Light and color uniformity is essential for the production of high-quality remote-sensing image mosaics. Existing color correction methods mainly use flexible models to express the color differences between multiple images and impose specific constraints (e.g., image gradient or contrast constraints) to preserve image texture information as much as possible. Due to these constraints, it is usually difficult to correct for the differences in texture between images during image processing. We propose a method that can optimize the luminance, contrast, and color difference of remote-sensing images. In the YCbCr color space, this method processes the chrominance and luminance channels of the image. This is conducive to reducing the influence of the different channels. In the luminance channel, the block-based Wallis transform method is used to optimize the luminance and contrast of the image. In the chromaticity channel, to optimize the color differences, a spline curve is used as a model; the color differences are formulated as a cost function and solved using convex quadratic programming. Moreover, considering the efficiency of our method, we use a graphics processing unit to make the algorithm parallel. The proposed method has been tested on several challenging datasets that cover different topographic regions. In terms of visuals and quality indicators, it shows better results than state-of-the-art approaches.
Learning Semantic Segmentation from Multiple Datasets with Label Shifts

While it is desirable to train segmentation models on an aggregation of multiple datasets, a major challenge is that the label space of each dataset may be in conflict with one another. To tackle this challenge, we propose UniSeg, an effective and model-agnostic approach to automatically train segmentation models across multiple datasets with heterogeneous label spaces, without requiring any manual relabeling efforts. Specifically, we introduce two new ideas that account for conflicting and co-occurring labels to achieve better generalization performance in unseen domains. First, we identify a gradient conflict in training incurred by mismatched label spaces and propose a class-independent binary cross-entropy loss to alleviate such label conflicts. Second, we propose a loss function that considers class-relationships across datasets for a better multi-dataset training scheme. Extensive quantitative and qualitative analyses on road-scene datasets show that UniSeg improves over multi-dataset baselines, especially on unseen datasets, e.g., achieving more than 8%p gain in IoU on KITTI. Furthermore, UniSeg achieves 39.4% IoU on the WildDash2 public benchmark, making it one of the strongest submissions in the zero-shot setting. Our project page is available at https://www.nec-labs.com/~mas/UniSeg .
Knowledge Graph Construction Research From Multi-source Vulnerability Intelligence

Abstract There is a huge Internet user group in China, and many enterprises and institutions are deeply affected by the threat of Cybersecurity vulnerabilities. At present, according to the needs of different business scenarios, relevant business personnel often need to search for different vulnerability information separately, relying on manpower, and the vulnerability intelligence distributed on the Internet has the characteristics of multi-source heterogeneity, which is difficult to ensure the effectiveness and reliability of vulnerability knowledge. In view of the above background, with vulnerabilities as the core, knowledge extraction of vulnerability intelligence is carried out according to existing standards, corresponding entities and relationships are established, and related and visualized knowledge graphs are studied and constructed to provide support for the discovery and traceability of vulnerability threats by information workers.
Identifying the Political Tendency of Social Bots in Twitter Using Sentiment Analysis: A Use Case of the 2021 Ecuadorian General Elections

Sentiment analysis of social network data increasingly represents the real political scenario of many countries, which has turned bots into a powerful tool of influence, mainly due to their high efficiency. This work analyzes the messages on Twitter during the 2021 Ecuadorian presidential elections to determine sentiments and bots detection. We obtained a sample of 35,242 tweets corresponding to each candidate’s first and second rounds. Our methodology consists of four phases: first, we perform data collection using the Twitter API; secondly, we pre-process the data; in the third phase, we perform sentiment analysis of the content of the tweets to understand their posture towards a candidate, and finally, we classify the users as bots or not. As a result, we discovered that bots and non-bots people on both sides had more positive feelings towards their respective candidates than unfavorable feelings against the other candidates.