Reviewerly/exHarmony huggingface dataset:
Efficient Semantic Segmentation via Self-Attention and Self-Distillation

Lightweight models are pivotal in efficient semantic segmentation, but they often suffer from insufficient context information due to limited convolution and small receptive field. To address this problem, we propose a tailored approach to efficient semantic segmentation by leveraging two complementary distillation schemes for supplementing context information to small networks: 1) a self-attention distillation scheme, which transfers long-range context knowledge adaptively from large teacher networks to small student networks; and 2) a layer-wise context distillation scheme, which transfers structured context from deep layers to shallow layers within student networks for promoting semantic consistency of the shallow layers. Extensive experiments on the ADE20K, Cityscapes, and Camvid datasets well demonstrate the effectiveness of our proposal.
TNCR: Table net detection and classification dataset

We present TNCR, a new table dataset with varying image quality collected from open access websites. TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes. TNCR contains 9428 labeled tables with approximately 6621 images. In this paper, we have implemented state-of-the-art deep learning-based methods for table detection to create several strong baselines. Deformable DERT with Resnet-50 Backbone Network achieves the highest performance compared to other methods with a precision of 86.7%, recall of 89.6%, and f1 score of 88.1% on the TNCR dataset. We have made TNCR open source in the hope of encouraging more deep learning approaches to table detection, classification and structure recognition. The dataset and trained model checkpoints are available at https://github.com/abdoelsayed2016/TNCR_Dataset.
Digital microbiology

<h2>Abstract</h2><h3>Background</h3> Digitalization and artificial intelligence have an important impact on the way microbiology laboratories will work in the near future. Opportunities and challenges lie ahead to digitalize the microbiological workflows. Making efficient use of big data, machine learning, and artificial intelligence in clinical microbiology requires a profound understanding of data handling aspects. <h3>Objective</h3> This review article summarizes the most important concepts of digital microbiology. The article gives microbiologists, clinicians and data scientists a viewpoint and practical examples along the diagnostic process. <h3>Sources</h3> We used peer-reviewed literature identified by a PubMed search for digitalization, machine learning, artificial intelligence and microbiology. <h3>Content</h3> We describe the opportunities and challenges of digitalization in microbiological diagnostic processes with various examples. We also provide in this context key aspects of data structure and interoperability, as well as legal aspects. Finally, we outline the way for applications in a modern microbiology laboratory. <h3>Implications</h3> We predict that digitalization and the usage of machine learning will have a profound impact on the daily routine of laboratory staff. Along the analytical process, the most important steps should be identified, where digital technologies can be applied and provide a benefit. The education of all staff involved should be adapted to prepare for the advances in digital microbiology.
PyExplainer: Explaining the Predictions of Just-In-Time Defect Models

Just-In-Time (JIT) defect prediction (i.e., an AI/ML model to predict defect-introducing commits) is proposed to help developers prioritize their limited Software Quality Assurance (SQA) resources on the most risky commits. However, the explainability of JIT defect models remains largely unexplored (i.e., practitioners still do not know why a commit is predicted as defect-introducing). Recently, LIME has been used to generate explanations for any AI/ML models. However, the random perturbation approach used by LIME to generate synthetic neighbors is still suboptimal, i.e., generating synthetic neighbors that may not be similar to an instance to be explained, producing low accuracy of the local models, leading to inaccurate explanations for just-in-time defect models. In this paper, we propose PyExplainer—i.e., a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of JIT defect models. Through a case study of two open-source software projects, we find that our PyExplainer produces (1) synthetic neighbors that are 41%-45% more similar to an instance to be explained; (2) 18%-38% more accurate local models; and (3) explanations that are 69%-98% more unique and 17%-54% more consistent with the actual characteristics of defect-introducing commits in the future than LIME (a state-ofthe-art model-agnostic technique). This could help practitioners focus on the most important aspects of the commits to mitigate the risk of being defect-introducing. Thus, the contributions of this paper build an important step towards Explainable AI for Software Engineering, making software analytics more explainable and actionable. Finally, we publish our PyExplainer as a Python package to support practitioners and researchers (https://github.com/awsm-research/PyExplainer).
An Improved Inter-Intra Contrastive Learning Framework on Self-Supervised Video Representation

In this paper, we propose a self-supervised contrastive learning method to learn video feature representations.In traditional self-supervised contrastive learning methods, constraints from anchor, positive, and negative data pairs are used to train the model.In such a case, different samplings of the same video are treated as positives, and video clips from different videos are treated as negatives.Because the spatio-temporal information is important for video representation, we set the temporal constraints more strictly by introducing intra-negative samples.In addition to samples from different videos, negative samples are extended by breaking temporal relations in video clips from the same anchor video.With the proposed Inter-Intra Contrastive (IIC) framework, we can train spatio-temporal convolutional networks to learn feature representations from videos.Strong data augmentations, residual clips, as well as head projector are utilized to construct an improved version.Three kinds of intra-negative generation functions are proposed and extensive experiments using different network backbones are conducted on benchmark datasets.Without using pre-computed optical flow data, our improved version can outperform previous IIC by a large margin, such as 19.4% (from 36.8% to 56.2%) and 5.2% (from 15.5% to 20.7%) points improvements in top-1 accuracy on UCF101 and HMDB51 datasets for video retrieval, respectively.For video recognition, over 3% points improvements can also be obtained on these two benchmark datasets.Discussions and visualizations validate that our IICv2 can capture better temporal clues and indicate the potential mechanism.
Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor

Abstract This paper introduces a comparative analysis of the proficiencies of various textures and geometric features in the diagnosis of breast masses on mammograms. An improved machine learning-based framework was developed for this study. The proposed system was tested using 106 full field digital mammography images from the INbreast dataset, containing a total of 115 breast mass lesions. The proficiencies of individual and various combinations of computed textures and geometric features were investigated by evaluating their contributions towards attaining higher classification accuracies. Four state-of-the-art filter-based feature selection algorithms (Relief-F, Pearson correlation coefficient, neighborhood component analysis, and term variance) were employed to select the top 20 most discriminative features. The Relief-F algorithm outperformed other feature selection algorithms in terms of classification results by reporting 85.2% accuracy, 82.0% sensitivity, and 88.0% specificity. A set of nine most discriminative features were then selected, out of the earlier mentioned 20 features obtained using Relief-F, as a result of further simulations. The classification performances of six state-of-the-art machine learning classifiers, namely k-nearest neighbor (k-NN), support vector machine, decision tree, Naive Bayes, random forest, and ensemble tree, were investigated, and the obtained results revealed that the best classification results (accuracy = 90.4%, sensitivity = 92.0%, specificity = 88.0%) were obtained for the k-NN classifier with the number of neighbors having k = 5 and squared inverse distance weight. The key findings include the identification of the nine most discriminative features, that is, FD26 (Fourier Descriptor), Euler number, solidity, mean, FD14, FD13, periodicity, skewness, and contrast out of a pool of 125 texture and geometric features. The proposed results revealed that the selected nine features can be used for the classification of breast masses in mammograms.
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items

Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff. This article presents a novel strategy that extends a conventional encoder-decoder architecture to perform instance-aware segmentation and extract merged instances of contraband items without using any additional subnetwork or an object detector. The encoder-decoder network first performs conventional semantic segmentation and retrieves cluttered baggage items. The model then incrementally evolves during training to recognize individual instances using significantly reduced training batches. To avoid catastrophic forgetting, a novel objective function minimizes the network loss in each iteration by retaining the previously acquired knowledge while learning new class representations and resolving their complex structural interdependencies through Bayesian inference. A thorough evaluation of our framework on two publicly available X-ray datasets shows that it outperforms state-of-the-art methods, especially within the challenging cluttered scenarios, while achieving an optimal tradeoff between detection accuracy and efficiency.
A dynamic computational model of gaze and choice in multi-attribute decisions.

When making decisions, how people allocate their attention influences their choices. One empirical finding is that people are more likely to choose the option that they have looked at more. This relation has been formalized with the attentional drift-diffusion model (aDDM; Krajbich et al., 2010). However, options often have multiple attributes, and attention is also thought to govern the relative weighting of those attributes (Roe et al., 2001). Little is known about how these two distinct features of the choice process interact; we still lack a model (and tests of that model) that incorporate both option- and attribute-wise attention. Here, we propose a multi-attribute attentional drift-diffusion model (maaDDM) to account for attentional discount factors on both options and attributes. We then use five eye-tracking datasets (two-alternative, two-attribute preferential tasks) from different choice domains to test the model. We find very stable option-level and attribute-level attentional discount factors across datasets, though nonfixated options are consistently discounted more than nonfixated attributes. Additionally, we find that people generally discount the nonfixated attribute of the nonfixated option in a multiplicative way, and so that feature is consistently discounted the most. Finally, we also find that gaze allocation reflects attribute weights, with more gaze to higher-weighted attributes. In summary, our work uncovers an intricate interplay between attribute weights, gaze processes, and preferential choice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Effectiveness of cybersecurity audit

The aim of this paper is to analyze the effectiveness of internal audit of cybersecurity. We developed a Cybersecurity Audit Index composed of three dimensions – planning, performing and reporting – to address this question. We hypothesize that cybersecurity audit effectiveness is positively related to cyber risk management maturity and negatively to the probability of a successful cyber attack. We tested our hypotheses in a survey with auditors and Chief Audit Executives from various countries and industries. We found that Cybersecurity Audit Index scores significantly vary, with a mean of 58 on a scale from 0 to 100. While the planning and performing phases are strongly and positively correlated, they are less strongly related to reporting about cyber risk management effectiveness to the Board of Directors. As predicted, the Cybersecurity Audit Index is positively associated with maturity, but contrary to expectations, it is not related to the probability of a successful cyber attack. This is the first paper that comprehensively measures the effectiveness of cybersecurity audit and its effects on cyber risk management.
Variational Single Nighttime Image Haze Removal With a Gray Haze-Line Prior

Influenced by glowing effects, nighttime haze removal is a challenging ill-posed task. Existing nighttime dehazing methods usually result in glowing artifacts, color shifts, overexposure, and noise amplification. Thus, through statistical and theoretical analyses, we propose a simple and effective gray haze-line prior (GHLP) to identify accurate hazy feature areas. This prior demonstrates that haze is concentrated on the haze line in the RGB color space and can be accurately projected into the gray component in the Y channel of the YUV color space. Based on this prior, we establish a new unified nighttime haze removal framework and then decompose a nighttime hazy image into color and gray components in the YUV color space. Glowing color correction and haze removal are two important consecutive steps in the nighttime dehazing process. The glowing color correction method is designed to separately remove glow in the color component and enhance illumination in the gray component. After obtaining a refined nighttime hazy image, we propose a new structure-aware variational framework to simultaneously estimate the inverted scene radiance and the transmission in the gray component. This approach can not only recover the high-quality nighttime scene radiance but also preserve the significant structural information and intrinsic color of the scene. Quantitative and qualitative comparisons validate the excellent effectiveness of the proposed nighttime dehazing method against previous state-of-the-art methods. In addition, the proposed approach can be extended to achieve image enhancement for inclement weather scenes, such as sandstorm scenes and extreme daytime hazy scenes.
Learning graph normalization for graph neural networks

Graph Neural Networks (GNNs) have emerged as a useful paradigm to process graph-structured data. Usually, GNNs are stacked to multiple layers and node representations in each layer are computed through propagating and aggregating the neighboring node features. To effectively train a GNN with multiple layers, normalization techniques are necessary. Though existing normalization techniques have achieved good results in helping GNNs training, but they seldom consider the structure information of the graph. In this paper, we propose two graph-aware normalization techniques, namely adjacency-wise normalization and graph-wise normalization, which fully take into account the structure information of the graph. Furthermore, we propose a novel approach, termed Attentive Graph Normalization (AGN), which learns a weighted combination of multiple graph-aware normalization methods, aiming to automatically select the optimal combination of multiple normalization methods for a specific task. We conduct extensive experiments on eleven benchmark datasets, including three single-graph and eight multiple-graph datasets, and the experimental results provide a comprehensive evaluation on the effectiveness of our proposals.
NFTs: Tulip Mania or Digital Renaissance?

Galleries, Libraries, Archives and Museums (GLAM) institutions have begun to sell non-fungible tokens (NFTs) of works from their collections following the $69.3 M USD record sale of Beeple’s Everydays: The First 5000 Days at Christie’s auction house on March 11, 2021. But many open questions exist about whether NFTs are beneficial or harmful for such institutions from financial, regulatory, and environmental perspectives. In this paper, we aim to unpack what NFTs are within the context of tokenomics and Ethereum standards development by providing an overview of notable NFTs and selling platforms before discussing the pros and cons of their use in GLAM institutions and exploring open research challenges through the lens of Computational Archival Science. Methodologies for the creation (minting) and purchase of NFTs are provided, emphasizing NFTs’ record keeping abilities, while also highlighting their inherent vulnerabilities, particularly with regards to the now-infamous broken link problem and its implications for provenance tracking and authenticity.
Specific Emitter Identification Based on Complex Fourier Neural Network

Specific emitter identification (SEI) is a well-established approach to providing precise target information for civilian and military applications. For most deep learning (DL) based SEI schemes, neural operators directly learn mappings from the raw baseband waveform or its transformed representation. Different from existing schemes, we propose a novel complex Fourier neural operator (CFNO) in this letter, which introduces a time and frequency domain attention mechanism. With the CFNO block, features are fully learned from different domain perspectives. We evaluate the proposed method based on the joint distortion model of the transmitter and compare it with several state-of-the-art SEI algorithms. Simulation results demonstrate its excellent performance, making the CFNO block a good candidate for extracting fingerprints.
TS-ABOS-CMS: time-bounded secure attribute-based online/offline signature with constant message size for IoT systems

Authentication is especially significant in IoT since the identity of the connected object needs to be verified before carrying out interaction processes with the various IoT data. The involvement of data has raised the concern about the risk of secure data sharing, which becomes a huge challenge when deploying IoT applications into real applications. In this paper, we present a time-bounded secure attribute-based online/offline signature with constant message size (TS-ABOS-CMS). Our scheme achieves high efficiency by introducing online/offline signing method and maintaining the communication overheads at a constant level. In addition, we adopt the key refreshing mechanism to provide time-bounded security protections for IoT terminals. We design the concrete algorithms of TS-ABOS-CMS and present its security proof. By performance assessment in terms of theoretical analysis and experiments on hardware, our TS-ABOS-CMS is demonstrated to be equipped with beneficial efficacy and appropriate to be deployed for data authentication in IoT scenarios.
COVID-19 Accelerating Academic Teachers’ Digital Competence in Distance Teaching

This study examines, using a cross-sectional approach, the digital competence of academic teachers at a time when teaching shifted to digital distance learning at the start of the COVID-19 pandemic. Teachers from different academic fields at a large multidisciplinary Finnish university (N = 265) responded to a questionnaire about the purposes for which they use digital tools in teaching, how they evaluated their competence at distance teaching during the lockdown of March-May 2020 and their beliefs about distance teaching. The respondents used digital tools in teaching mostly for delivering information. According to their evaluations, their competence in distance teaching increased during the early stages of COVID-19 pandemic in 2020, but their beliefs about distance teaching did not relate to the feelings of competence. Respondents with no experience in distance teaching before the lockdown evaluated their competence as having increased more than did respondents with previous experience. The implications of the findings for understanding competence development are then discussed.
Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams

Multi-label data streams are sequences of multi-label instances arriving over time to a multi-label classifier. The properties of the stream may continuously change due to concept drift. Therefore, algorithms must constantly adapt to the new data distributions. In this paper we propose a novel ensemble method for multi-label drifting streams named Adaptive Ensemble of Self-Adjusting Nearest Neighbor Subspaces (AESAKNNS). It leverages a self-adjusting kNN as a base classifier with the advantages of ensembles to adapt to concept drift in the multi-label environment. To promote diverse knowledge within the ensemble, each base classifier is given a unique subset of features and samples to train on. These samples are distributed to classifiers in a probabilistic manner that follows a Poisson distribution as in online bagging. Accompanying these mechanisms, a collection of ADWIN detectors monitor each classifier for the occurrence of a concept drift on the subspace. Upon detection, the algorithm automatically trains additional classifiers in the background to attempt to capture new concepts on new subspaces of features. The dynamic classifier selection chooses the most accurate classifiers from the active and background ensembles to replace the current ensemble. Our experimental study compares the proposed approach with 30 other classifiers, including problem transformation, algorithm adaptation, kNNs, and ensembles on 30 diverse multi-label datasets and 12 performance metrics. Results, validated using non-parametric statistical analysis, support the better performance of the AESAKNNS and highlight the contribution of its components in improving the performance of the ensemble.
Implementation of Blockchain Consortium to Prioritize Diabetes Patients’ Healthcare in Pandemic Situations

Diabetes is a metabolic disorder caused by high blood sugar levels, which can harm the kidneys, the heart, the eyes, and blood vessels. During the Covid-19 pandemic, diabetes patients were most affected. In the existing healthcare system, medical data is available in paper form or through a central server. Accessing the data from the central system and sharing it with all stakeholders would be a critical task during the pandemic. This research work deals with the design and implementation of a diabetes blockchain consortium. It can help all healthcare stakeholders to efficiently prioritize the needs of diabetes patients during a pandemic, such as oxygen beds, vaccinations, diabetes compensation, telemedicine, 5G-integrated remote location support, and other related records. The Ethereum sandbox simulation design is utilized to secure diabetes patients’ healthcare records. The Interplanetary file system (IPFS) encrypts health data and sends it to the blockchain to ensure the privacy of personal healthcare information. The NEM symbol blockchain is used to develop this consortium as a proof-of-concept (PoC) model. Each stakeholder in a consortium is assigned NEM generated QR code to track records as a distributed ledger. A smart contract designed to run the diabetes blockchain application. Attribute-based encryption (ABE) authenticates users and restricts malicious nodes. Certainly, this research suggests aggregation of transactions and blocks in the blockchain, which would increase transaction speed, minimize transaction fees, and consume less power in a future blockchain design.