Reviewerly/exHarmony huggingface dataset:
Flexible Interactive Guided Image Matting

Image matting is an important computer vision problem. Many existing matting methods leverage a hand-made trimap to provide auxiliary information, which is expensive and time-consuming for real-world applications. In recent years, some trimap-free methods have been proposed, which completely get rid of any user input. However, their performance lag far behind trimap-based methods due to the ambiguity problem. This paper proposes a matting method that uses flexible interactive guidance as user hints, which means the proposed method can use trimap, scribblemap, clickmap, and bounding box as guidance information or can even work without any guidance input. Our goal is to maintain accuracy while simplifying input. To achieve this, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Progressive Trimap Deformation (PTD)</b> scheme that gradually transforms the input trimaps into scribblemaps during training. To enable bounding box input, based on the same network, this paper proposes <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Progressive Bounding Box Deformation (PBD)</b> scheme that gradually turns trimaps into bounding box masks during training. To further ensure the stability of training and enhance performance, this paper also introduces the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Random Deformation Degree</b> scheme. Experiments show that the proposed method can achieve competitive results compared with existing trimap-based and trimap-free methods.
Towards understanding students’ sensemaking of test case design

Software testing is the most used technique for quality assurance in industry. However, in computer science education software testing is still treated as a second-class citizen and students are unable to test their software well enough. One reason for this is that teaching the subject of software testing is difficult as it is a complex intellectual activity for which students need to allocate multiple cognitive resources at the same time. A myriad of primary and secondary studies have tried to solve this problem in education, however still with very limited results. Before we can design interventions to improve our pedagogical approaches, we need to gain more in-depth understanding and recognition of sensemaking as it is happening when students design test cases. An initial exploratory study identified four different sensemaking approaches used by students while creating test models. In this paper we present a follow-up study with 50 students from a large university in Spain. The used methodology was based on the previous study with the improvements that originated from its evaluation. We asked the participants to create a test model based on a description of a test problem using a specialized web-based tool for modeling test cases. We measured how well these models fit the test problem, the sensemaking process that students went through when creating the models, and the students’ perception of the modeling task. The participants received no compensation for their efforts, and we scheduled the experiment during a regular class. Apart from the created models and their metadata, we also collected recordings of the students’ computer screens made during the experiment and used a questionnaire to study their perspectives on the assignment. All the collected textual, graphical, and video data was analyzed using an iterative inductive analysis process to allow new information about the different sensemaking approaches to emerge. We gained better insights into the sensemaking processes of students while modeling test cases for a problem. The results enabled us to refine our previous findings, and we identified new sensemaking approaches. Based on these results, we can further investigate ways to influence the sensemaking process in education, the possible misconceptions that have a negative influence on it, and the desired mental model we want our students to have to design test cases.
Designing a User-Centric School Organizational Club Application System: Development and Evaluation

This paper presents the design and evaluation of a user-centric school organizational club application system. The study aims to improve the management of school clubs by providing a user-friendly platform that facilitates efficient club administration and enhances the overall club experience for students. The design phase focuses on developing an intuitive and easy-to-use application, while the evaluation phase assesses the system's performance in terms of usability, functionality, security, and maintainability. The evaluation results demonstrate that the system achieves high scores in these categories, indicating its effectiveness in addressing the needs of school clubs. With its user-centric design, the system offers enhanced usability, accurate data management, secure access, and easy maintenance. Overall, this research highlights the importance of user-centric design principles in creating effective and efficient solutions for school club management, ultimately improving the club experience for students and administrators alike.
Interhemispheric connections in the maintenance of language performance and prognosis prediction: fully connected layer-based deep learning model analysis

Language-related networks have been recognized in functional maintenance, which has also been considered the mechanism of plasticity and reorganization in patients with cerebral malignant tumors. However, the role of interhemispheric connections (ICs) in language restoration remains unclear at the network level. Navigated transcranial magnetic stimulation (nTMS) and diffusion tensor imaging fiber tracking data were used to identify language-eloquent regions and their corresponding subcortical structures, respectively.Preoperative image-based IC networks and nTMS mapping data from 30 patients without preoperative and postoperative aphasia as the nonaphasia group, 30 patients with preoperative and postoperative aphasia as the glioma-induced aphasia (GIA) group, and 30 patients without preoperative aphasia but who developed aphasia after the operation as the surgery-related aphasia group were investigated using fully connected layer-based deep learning (FC-DL) analysis to weight ICs.GIA patients had more weighted ICs than the patients in the other groups. Weighted ICs between the left precuneus and right paracentral lobule, and between the left and right cuneus, were significantly different among these three groups. The FC-DL approach for modeling functional and structural connectivity was also tested for its potential to predict postoperative language levels, and both the achieved sensitivity and specificity were greater than 70%. Weighted IC was reorganized more in GIA patients to compensate for language loss.The authors' method offers a new perspective to investigate brain structural organization and predict functional prognosis.
AI Cricket Score Board

Gesture Recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost important in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation, monitoring patients or elder people, surveillance systems, sports gesture analysis, human behavior analysis etc., to virtual reality. In recent years, there has been increased interest in video summarization and automatic sports highlights generation in the game of Cricket. In Cricket, the Umpire has the authority to make important decisions about events on the field. The Umpire signals important events using unique hand on signals and gestures. The primary intention of our work is to design and develop a new robust method for Umpire Action and Non-Action Gesture Identification and Recognition based on the Umpire Segmentation and the proposed Histogram Oriented Gradient (HOG) feature Extraction oriented of Deep Features. Primarily the 80% of Umpire action and non-action images in a cricket match, about 1, 93,000 frames, the Histogram of Oriented Gradient Deep Features are calculated and trained the system having six gestures of Umpire pose using Densenet121.
Truth about Artifacts

Truth in a correspondence sense is objective in two ways. It is objective because the relation of correspondence is objective and because the facts to which truths correspond are objective. Truth about artifacts is problematic because artifacts are intentionally designed to perform certain functions, and so are not entirely mind independent. Against this, it is argued in this paper that truth about artifacts is perfectly objective despite the role played by intention and purpose in the production of artifacts.
Genetic algorithm optimization for parametrization, digital twinning, and now-casting of unknown small- and medium-scale PV systems based only on on-site measured data

Accurately predicting and balancing energy generation and consumption are crucial for grid operators and asset managers in a market where renewable energy is increasing. To speed up the process, these predictions should ideally be performed based only on on-site measured data and data available within the monitoring platforms, data which are scarce for small- and medium-scale PV systems. In this study, we propose an algorithm that can now-cast the power output of a photovoltaic (PV) system with high accuracy. Additionally, it offers physical information related to the configuration of such a PV system. We adapted a genetic algorithm-based optimization approach to parametrize a digital twin of unknown PV systems, using only on-site measured PV power and irradiance in the plane of array. We compared several training datasets under various sky conditions. A mean deviation of −1.14 W/kW p and a mean absolute percentage deviation of 1.81% were obtained when we analyzed the accuracy of the PV power now-casting for the year 2020 of the 16 unknown PV systems used for this analysis. This level of accuracy is significant for ensuring the efficient now-casting and operation of PV assets.
BinBench: a benchmark for x64 portable operating system interface binary function representations

In this article we propose the first multi-task benchmark for evaluating the performances of machine learning models that work on low level assembly functions. While the use of multi-task benchmark is a standard in the natural language processing (NLP) field, such practice is unknown in the field of assembly language processing. However, in the latest years there has been a strong push in the use of deep neural networks architectures borrowed from NLP to solve problems on assembly code. A first advantage of having a standard benchmark is the one of making different works comparable without effort of reproducing third part solutions. The second advantage is the one of being able to test the generality of a machine learning model on several tasks. For these reasons, we propose BinBench, a benchmark for binary function models. The benchmark includes various binary analysis tasks, as well as a dataset of binary functions on which tasks should be solved. The dataset is publicly available and it has been evaluated using baseline models.
A MOOC Course Data Analysis Based on an Improved Metapath2vec Algorithm

Many real-world scenarios can be naturally modeled as heterogeneous graphs, which contain both symmetry and asymmetry information. How to learn useful knowledge from the graph has become one of the hot spots of research in artificial intelligence. Based on Metapath2vec algorithm, an improved Metapath2vec algorithm is presented, which combines Metapath random walk, used to capture semantics and structure information between different nodes of a heterogeneous network, and GloVe model to consider the advantage of global text representation. In order to verify the feasibility and effectiveness of the model, node clustering and link prediction experiments were conducted on the self-generated ideal dataset and the MOOC course data. The analysis of experimental data on these tasks shows that the Metapath–GloVe algorithm learns consistently better embedding of heterogeneous nodes, and the algorithm improves the node embedding performance to better characterize the heterogeneous network structure and learn the characteristics of nodes, which proves the effectiveness and scalability of the proposed method in heterogeneous network mining tasks. It is also shown through extensive experiments that the Metapath–GloVe algorithm is more efficient than the non-negative matrix decomposition algorithm (NMF), and it can obtain better clustering results and more accurate prediction results in the video recommendation task.
Dual Space Latent Representation Learning for Image Representation

Semi-supervised non-negative matrix factorization (NMF) has achieved successful results due to the significant ability of image recognition by a small quantity of labeled information. However, there still exist problems to be solved such as the interconnection information not being fully explored and the inevitable mixed noise in the data, which deteriorates the performance of these methods. To circumvent this problem, we propose a novel semi-supervised method named DLRGNMF. Firstly, dual latent space is characterized by the affinity matrix to explicitly reflect the interrelationship between data instances and feature variables, which can exploit the global interconnection information in dual space and reduce the adverse impacts caused by noise and redundant information. Secondly, we embed the manifold regularization mechanism in the dual graph to steadily retain the local manifold structure of dual space. Moreover, the sparsity and the biorthogonal condition are integrated to constrain matrix factorization, which can greatly improve the algorithm’s accuracy and robustness. Lastly, an effective alternating iterative updating method is proposed, and the model is optimized. Empirical evaluation on nine benchmark datasets demonstrates that DLRGNMF is more effective than competitive methods.
Nested Entity Recognition Fusing Span Relative Position and Region Information

At present, span-based entity recognition methods are mainly used to accurately identify the span (entity) boundary for entity recognition, in which the relative position information of the span boundary and the information of words in the span region are routinely ignored. This information can be used to improve entity recognition performance. Therefore, a nested entity recognition model, which integrates the relative position information of the span and the region information within the span, is proposed. The span representation is first obtained with a triaffine attention. Then, the relative position of the span boundary and the word information in the span region, as well as the previous span representation, are fused to obtain a new label-level span representation with another triaffine attention. Finally, the span (entity) recognition task is carried out by a cooperative biaffine mechanism. Experiments were conducted on some public datasets, including ACE2004, ACE2005 and GENIA. The results show that the F1-scores achieved using the proposed method were 87.66%, 86.86% and 80.90% on ACE2004, ACE2005 and GENIA, respectively. These experiments show that the method achieved state-of-the-art (SOTA) results. Moreover, the proposed model has fewer parameters and needs fewer resources with a lower time complexity than the existing triaffine mechanism model.
On Sequential Bayesian Inference for Continual Learning

Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revisit sequential Bayesian inference and assess whether using the previous task's posterior as a prior for a new task can prevent catastrophic forgetting in Bayesian neural networks. Our first contribution is to perform sequential Bayesian inference using Hamiltonian Monte Carlo. We propagate the posterior as a prior for new tasks by approximating the posterior via fitting a density estimator on Hamiltonian Monte Carlo samples. We find that this approach fails to prevent catastrophic forgetting, demonstrating the difficulty in performing sequential Bayesian inference in neural networks. From there, we study simple analytical examples of sequential Bayesian inference and CL and highlight the issue of model misspecification, which can lead to sub-optimal continual learning performance despite exact inference. Furthermore, we discuss how task data imbalances can cause forgetting. From these limitations, we argue that we need probabilistic models of the continual learning generative process rather than relying on sequential Bayesian inference over Bayesian neural network weights. Our final contribution is to propose a simple baseline called Prototypical Bayesian Continual Learning, which is competitive with the best performing Bayesian continual learning methods on class incremental continual learning computer vision benchmarks.
Limits of Human Expert Ensembles in Mitosis Multi-expert Ground Truth Generation

Computer vision classification tasks rely on the availability of ground truth labels. Especially in medical imaging, these are typically given by experts and can be of differing quality. To reduce the expert bias influence on labels, commonly blinded multi-expert consensus labels are used as ground truth in machine learning. In this work, we approach the question of how good a multiexpert consensus can be for the example of mitotic figure (MF) identification, which is a relevant task in tumor malignancy assessment. For this, we provide an exhaustive evaluation of all possible majority ensembles of 23 pathologists who independently assessed MFs based on a preselected region of interest. We compared the ensemble against a immunohistochemistry-based ground truth. We found that there were upper bounds to the recognition of MFs by the experts, which were, in our dataset, an accuracy, sensitivity and specificity of 88%, 82%, and 100%, respectively. An analysis of our results revealed cells in prophase and blurry cells to be amongst the most challenging to recognize.
Reversible data hiding based on histogram and prediction error for sharing secret data

Abstract With the advancement of communication technology, a large number of data are constantly transmitted through the internet for various purposes, which are prone to be illegally accessed by third parties. Therefore, securing such data is crucial to protect the transmitted information from falling into the wrong hands. Among data protection schemes, Secret Image Sharing is one of the most popular methods. It protects critical messages or data by embedding them in an image and sharing it with some users. Furthermore, it combines the security concepts in that private data are embedded into a cover image and then secured using the secret-sharing method. Despite its advantages, this method may produce noise, making the resulting stego file much different from its cover. Moreover, the size of private data that can be embedded is limited. This research works on these problems by utilizing prediction-error expansion and histogram-based approaches to embed the data. To recover the cover image, the SS method based on the Chinese remainder theorem is used. The experimental results indicate that this proposed method performs better than similar methods in several cover images and scenarios.
Deep quantization network with visual-semantic alignment for zero-shot image retrieval

&lt;abstract&gt;&lt;p&gt;Approximate nearest neighbor (ANN) search has become an essential paradigm for large-scale image retrieval. Conventional ANN search requires the categories of query images to been seen in the training set. However, facing the rapid evolution of newly-emerging concepts on the web, it is too expensive to retrain the model via collecting labeled data with the new (unseen) concepts. Existing zero-shot hashing methods choose the semantic space or intermediate space as the embedding space, which ignore the inconsistency of visual space and semantic space and suffer from the hubness problem on the zero-shot image retrieval task. In this paper, we present an novel deep quantization network with visual-semantic alignment for efficient zero-shot image retrieval. Specifically, we adopt a multi-task architecture that is capable of $ 1) $ learning discriminative and polymeric image representations for facilitating the visual-semantic alignment; $ 2) $ learning discriminative semantic embeddings for knowledge transfer; and $ 3) $ learning compact binary codes for aligning the visual space and the semantic space. We compare the proposed method with several state-of-the-art methods on several benchmark datasets, and the experimental results validate the superiority of the proposed method.&lt;/p&gt;&lt;/abstract&gt;
Blockchained Trustable Federated Learning Utilizing Voting Accountability for Malicious Actor Mitigation

The federated learning (FL) approach in machine learning preserves user privacy during data collection. However, traditional FL schemes still rely on a centralized server, making them vulnerable to security risks, such as data breaches and tampering of models caused by malicious actors attempting to gain access by masquerading as trainers. To address these issues that hamper the trustability of federated learning, requirements were analyzed for several of these problems. The findings revealed that issues, such as the lack of accountability management, malicious actor mitigation, and model leakage, remained unaddressed in prior works. To fill this gap, a blockchain-based trustable FL scheme, MAM-FL, is proposed with the focus on providing accountability to trainers. MAM-FL established a group of voters responsible for evaluating and verifying the validity of the model updates submitted. The effectiveness of MAM-FL was tested based on the reduction of malicious actors present on both trainers’ and voters’ sides and the ability to handle colluding participants. Experiments show that MAM-FL succeeded at reducing the number of malicious actors, despite the test case involving initial collusion in the system.
Depth Estimation using DNN Architecture and Vision-Based Transformers

Depth Estimation is the process of estimating the depth of objects with a 2D image as an input. The importance of Depth Estimation is that it provides some critical information about the 3D structure of a scene given a sequence of 2D images. This is helpful in various applications like robotics, virtual reality, autonomous driving, medical imaging, and so on and so forth. Our main objective here is to make the environment more perceivable by autonomous vehicles. Unlike the traditional approaches which try to extract depth from the input image, we apply instance segmentation to the image and then use the segmented image as an input to the depth map generator. In this paper, we use a fully connected neural network with dense prediction transformers. As mentioned above, the image after instance segmentation is given as an input for the transformers. The Transformer is the backbone for producing high-resolution images. Our experimentation results have shown many improvements related to the details and sharpness of the image as compared to the traditional depth estimation techniques. The architecture is applied and tested on the KITTI data set.