Reviewerly/exHarmony huggingface dataset:
Achievable Minimally-Contrastive Counterfactual Explanations

Decision support systems based on machine learning models should be able to help users identify opportunities and threats. Popular model-agnostic explanation models can identify factors that support various predictions, answering questions such as “What factors affect sales?” or “Why did sales decline?”, but do not highlight what a person should or could do to get a more desirable outcome. Counterfactual explanation approaches address intervention, and some even consider feasibility, but none consider their suitability for real-time applications, such as question answering. Here, we address this gap by introducing a novel model-agnostic method that provides specific, feasible changes that would impact the outcomes of a complex Black Box AI model for a given instance and assess its real-world utility by measuring its real-time performance and ability to find achievable changes. The method uses the instance of concern to generate high-precision explanations and then applies a secondary method to find achievable minimally-contrastive counterfactual explanations (AMCC) while limiting the search to modifications that satisfy domain-specific constraints. Using a widely recognized dataset, we evaluated the classification task to ascertain the frequency and time required to identify successful counterfactuals. For a 90% accurate classifier, our algorithm identified AMCC explanations in 47% of cases (38 of 81), with an average discovery time of 80 ms. These findings verify the algorithm’s efficiency in swiftly producing AMCC explanations, suitable for real-time systems. The AMCC method enhances the transparency of Black Box AI models, aiding individuals in evaluating remedial strategies or assessing potential outcomes.
Zero-Shot Neural Decoding with Semi-Supervised Multi-View Embedding

Zero-shot neural decoding aims to decode image categories, which were not previously trained, from functional magnetic resonance imaging (fMRI) activity evoked when a person views images. However, having insufficient training data due to the difficulty in collecting fMRI data causes poor generalization capability. Thus, models suffer from the projection domain shift problem when novel target categories are decoded. In this paper, we propose a zero-shot neural decoding approach with semi-supervised multi-view embedding. We introduce the semi-supervised approach that utilizes additional images related to the target categories without fMRI activity patterns. Furthermore, we project fMRI activity patterns into a multi-view embedding space, i.e., visual and semantic feature spaces of viewed images to effectively exploit the complementary information. We define several source and target groups whose image categories are very different and verify the zero-shot neural decoding performance. The experimental results demonstrate that the proposed approach rectifies the projection domain shift problem and outperforms existing methods.
Linguistic Explanations of Black Box Deep Learning Detectors on Simulated Aerial Drone Imagery

Deep learning has become increasingly common in aerial imagery analysis. As its use continues to grow, it is crucial that we understand and can explain its behavior. One eXplainable AI (XAI) approach is to generate linguistic summarizations of data and/or models. However, the number of summaries can increase significantly with the number of data attributes, posing a challenge. Herein, we proposed a hierarchical approach for generating and evaluating linguistic statements of black box deep learning models. Our approach scores and ranks statements according to user-specified criteria. A systematic process was outlined for the evaluation of an object detector on a low altitude aerial drone. A deep learning model trained on real imagery was evaluated on a photorealistic simulated dataset with known ground truth across different contexts. The effectiveness and versatility of our approach was demonstrated by showing tailored linguistic summaries for different user types. Ultimately, this process is an efficient human-centric way of identifying successes, shortcomings, and biases in data and deep learning models.
Attention boosted autoencoder for building energy anomaly detection

Significant energy savings can be realised from buildings if deviations from the usual operating conditions are detected early, and appropriate measures are taken. Building anomaly detection techniques automate identifying such instances by leveraging the high dimensional data collected from the installed smart meters. Autoencoders allow for dimensionality reduction and also model the underlying data distribution. However, these models treat features as independent quantities. In contrast, the current work investigates an attention mechanism with an autoencoder to include the correlations among the features. The value addition from the attention mechanism is demonstrated by comparing the model’s reconstruction ability with an ANN-based autoencoder on synthetic datasets. The study identifies that adding an attention layer enables the encoder–decoder architecture to be robust to outliers in training data, thereby reducing the preprocessing required. Further, the model is tested on a real-world dataset, and the attention maps generated from the model are used to interpret the correlations among the features and across the time dimension, thereby establishing a human-interpretable way to understand the reconstruction from the model.
Multi-Scale Feature Fusion and Structure-Preserving Network for Face Super-Resolution

Deep convolutional neural networks have demonstrated significant performance improvements in face super-resolution tasks. However, many deep learning-based approaches tend to overlook the inherent structural information and feature correlation across different scales in face images, making the accurate recovery of face structure in low-resolution cases challenging. To address this, this paper proposes a method that fuses multi-scale features while preserving the facial structure. It introduces a novel multi-scale residual block (MSRB) to reconstruct key facial parts and structures from spatial and channel dimensions, and utilizes pyramid attention (PA) to exploit non-local self-similarity, improving the details of the reconstructed face. Feature Enhancement Modules (FEM) are employed in the upscale stage to refine and enhance current features using multi-scale features from previous stages. The experimental results on CelebA, Helen and LFW datasets provide evidence that our method achieves superior quantitative metrics compared to the baseline, the Peak Signal-to-Noise Ratio (PSNR) outperforms the baseline by 0.282 dB, 0.343 dB, and 0.336 dB. Furthermore, our method demonstrates improved visual performance on two additional no-reference datasets, Widerface and Webface.
Distortion Free Image Reconstruction using a Deep Neural Network for an MRI-Linac

The advent of MRI-guided radiotherapy has elevated demand for high geometric fidelity imaging. However, gradient nonlinearity can cause image distortion, which limits the accuracy of radiotherapy. In this work, we develop a deep neural network, namely DFReconNet, to reconstruct distortion free images directly from raw k-space in real time. Experiments on simulated brain datasets and phantom images acquired from an MRI-Linac demonstrated the utility of the proposed method.
Highly Efficient Anchor-Free Oriented Small Object Detection for Remote Sensing Images via Periodic Pseudo-Domain

With the continuous progress of remote sensing image object detection tasks in recent years, researchers in this field have gradually shifted the focus of their research from horizontal object detection to the study of object detection in arbitrary directions. It is worth noting that some properties are different from horizontal object detection during oriented object detection that researchers have yet to notice much. This article presents the design of a straightforward and efficient arbitrary-oriented detection system, leveraging the inherent properties of the orientation task, including the rotation angle and box aspect ratio. In the detection of low aspect ratio objects, the angle is of little importance to the orientation bounding box, and it is even difficult to define the angle information in extreme categories. Conversely, in the detection of objects with high aspect ratios, the angle information plays a crucial role and can have a decisive impact on the quality of the detection results. By exploiting the aspect ratio of different targets, this letter proposes a ratio-balanced angle loss that allows the model to make a better trade-off between low-aspect ratio objects and high-aspect ratio objects. The rotation angle of each oriented object, which we naturally embed into a two-dimensional Euclidean space for regression, thus avoids an overly redundant design and preserving the topological properties of the circular space. The performance of the UCAS-AOD, HRSC2016, and DLR-3K datasets show that the proposed model in this paper achieves a leading level in terms of both accuracy and speed.
A multi-output deep learning algorithm to improve brain lesion segmentation by enhancing the resistance of variabilities in tissue contrast

We propose a multi-output segmentation approach, which incorporates other non-lesion brain tissue maps into the additional output layers to force the model to learn more about the lesion and tissue characteristics. We construct a cross-vendor study by training the white matter hyperintensities segmentation model on cases collected from one vendor and testing the model performance on eight different data sets. The model performance can be significantly improved, especially in testing sets which shows low image contrast similarity with training data, suggesting the feasibility of incorporating the non-lesion characteristics into segmentation model to enhance the resistance of cross-vendor image contrast variabilities.
Ethics of E-Learning Recommender Systems: Epistemic Positioning and Ideological Orientation

Abstract Recommender systems are increasingly used in e-learning to provide users with personalized services and advice. Depending on the specific context for which the system is implemented (e.g., homework on a specific subject for university students, new training courses for life-long learners), the objectives and proposed items, the chosen recommendation techniques, the features that are considered, the way the recommendations are presented to the users are closely related to the designers’ perception of learners and knowledge. The various approaches reflect different epistemic and ethical viewpoints; for example, representing people using fixed models is easier to process, diagnose, predict and explain, but presents a partial view of reality and obscures the fact that they are complex and evolving individuals. Similarly, some filtering methods can restrict the view of available courses to items considered similar to those that the learner has already followed, thus promoting specialization rather than diversification and openness. This aspect is closely related to fundamental issues involved in the theory of knowledge, questioning the notions of utility and purposes of science, as well as a key issue for academic change and, more fundamentally, that of modern societies. Indeed, these issues should be seen in a broader context of reflection about the economic changes and ideological transformations of a society grounded on neoliberal capitalism. The main goal of this study is to explain how the design of recommender systems in e-learning has both ethical and practical implications since it reflects an ideological conception of science and techniques, thus requiring a previous examination of these issues in order to define the theoretical model of knowledge in which it takes place. For that purpose, we study the certain visions of teaching and learning that can be brought about by algorithms and models used by existing recommender systems in e-learning.
Geochemical dispersion evaluation by criterion-based backward elimination at the Glojeh polymetallic Au (Ag–Cu–Pb–Zn) vein deposit, NW Iran

We applied the backward elimination method as a criterion-based iterative stepwise method to estimate the concentration of Au from the variables Ag, Cu, Pb and Zn. We optimized the quadratic polynomial model on various boreholes and trenches. The results indicate that the vertical zonation of Au is associated with Ag and Cu, along with their respective elemental functions Ag × Cu, Pb 2 , Pb × Zn and Zn 2 . On the other hand, the lateral dispersion of Au is determined by Pb and Zn, along with the essential functions Ag × Zn and Pb × Zn. Zonation is in-depth and is indicated primarily by Zn rather than Cu and minor Pb, with Cu–Pb–Zn gradually extending upward at the upper levels. The vertical zonation trend describes the Ag &lt; Au &lt; Cu &lt; Pb &lt; Zn sequence from proximal to distal veins, with the near-surface zone exhibiting enrichment in Zn. At the Glojeh site, the host rock value, Ag × Cu, Zn 2 and Pb × Zn were identified as strong indicators of the vertical occurrence of Au in the upper host rock, whereas Pb 2 proved more suitable for the deeper boreholes. Cu→Pb→Zn demonstrates the enrichment trend from depth to the surface. The lateral kriging surface estimation confirms the dispersion pattern of elements revealed in-depth at Glojeh. Our aims with this approach were to reduce the cost of fire-assay analysis due to high-precision estimation by associated variables and to determine elemental interactions and the lateral or vertical dependencies of elements.
The Influence of Educational Snakes and Ladders Game on the Cognitive Development of Children Aged 5-6 Years in TK Aisyiyah Bustanul Athfal 41

This study aims to find out how the educational snakes and ladders game influences the cognitive development of children aged 5-6 years at Aisyiyah Bustanul Athfal 41 Private Kindergarten. This research was conducted at Aisyiyah Bustanul Athfal 41 Private Kindergarten, with a population of 54 children and 15 children as a sample. who is in the Tulip class. The sampling technique used purposive sampling. In this study the method used was the Quasi Experimental Designs research method using the Equivalent Time Samples Design research design. The data collection instrument uses an observation sheet. The first treatment (X1) which was given treatment, the child obtained an average score of 7.4 included in the Less Good category. The second treatment (X0) which was not given child treatment obtained an average value of 6.3 included in the Not Good category. Furthermore, in the third treatment (X1) which was given treatment, the child got an average value of 13 included in the Very Good category. And finally the fourth treatment (X0) which was not given treatment, the child got an average value of 12.2 which was in the Good category. Based on the results of the Friedman test calculations, it shows the Asymp Value. Sig (0.000) &lt; 0.05, it can be concluded that there is a significant effect of the educational snakes and ladders game on the cognitive development of children aged 5-6 years in TK Aisyiyah Bustanul Athfal 41.
Learning to segment with limited annotations: Self-supervised pretraining with Regression and Contrastive loss in MRI

The availability of large unlabeled datasets compared to labeled ones motivate the use of self-supervised pretraining to initialize deep learning models for subsequent segmentation tasks. We consider two pre-training approaches for driving a CNN to learn different representations using: a) a reconstruction loss that exploits spatial dependencies and b) a contrastive loss that exploits semantic similarity. The techniques are evaluated in two MR segmentation applications: a) liver and b) prostate segmentation in T2-weighted images. We observed that CNNs pretrained using self-supervision can be finetuned for comparable performance with fewer labeled datasets.
Characteristic curves of the photodiode and phototransistor with Arduino

Abstract A simple circuit with Arduino for determining characteristic curves of the photodiode and phototransistor is described. The experimental results show that the measured I – V curves of the photodiode and phototransistor are consistent with the datasheet.
A High-performance Masking Design Approach for Saber against High-order Side-channel Attack

Post-quantum cryptography (PQC) has become the most promising cryptographic scheme against the threat of quantum computing to conventional public-key cryptographic schemes. Saber, as the finalist in the third round of the PQC standardization procedure, presents an appealing option for embedded systems due to its high encryption efficiency and accessibility. However, side-channel attack (SCA) can easily reveal confidential information by analyzing the physical manifestations, and several works demonstrate that Saber is vulnerable to SCAs. In this work, a ciphertext comparison method for masking design based on the bitslicing technique and zerotest is proposed, which balances the tradeoff between the performance and security of comparing two arrays. The mathematical description of the proposed ciphertext comparison method is provided, and its correctness and security metrics are analyzed under the concept of PINI. Moreover, a high-order masking approach based on the state of the art, including the hash functions, centered binomial sampling, masking conversions, and proposed ciphertext comparison, is presented, using the bitslicing technique to improve throughput. As a proof of concept, the proposed implementation of Saber is on the ARM Cortex-M4. The performance results show that the runtime overhead factor of 1st-, 2nd-, and 3rd-order masking is 3.01×, 5.58×, and 8.68×, and the dynamic memory used for 1st-, 2nd-, and 3rd-order masking is 17.4kB, 24.0kB, and 30.2kB, respectively. The SCA-resilience evaluation results illustrate that the 1st-order Test Vectors Leakage Assessment (TVLA) result fails to reveal the secret key with 100,000 traces.
Laplacian-based Cluster-Contractive t-SNE for High-Dimensional Data Visualization

Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces to extract hidden and useful information or facilitate visual understanding and interpretation of the data. However, few of them take into consideration the potential cluster information contained implicitly in the high-dimensional data. In this article, we propose Lap tSNE, a new graph-layout nonlinear dimensionality reduction method based on t-SNE, one of the best techniques for visualizing high-dimensional data as 2D scatter plots. Specifically, Lap tSNE leverages the eigenvalue information of the graph Laplacian to shrink the potential clusters in the low-dimensional embedding when learning to preserve the local and global structure from high-dimensional space to low-dimensional space. It is nontrivial to solve the proposed model because the eigenvalues of normalized symmetric Laplacian are functions of the decision variable. We provide a majorization-minimization algorithm with convergence guarantee to solve the optimization problem of Lap tSNE and show how to calculate the gradient analytically, which may be of broad interest when considering optimization with Laplacian-composited objective. We evaluate our method by a formal comparison with state-of-the-art methods on seven benchmark datasets, both visually and via established quantitative measurements. The results demonstrate the superiority of our method over baselines such as t-SNE and UMAP. We also provide out-of-sample extension, large-scale extension, and mini-batch extension for our Lap tSNE to facilitate dimensionality reduction in various scenarios.
The usability gap in water resources open data and actionable science initiatives

Abstract The open data movement represents a major advancement for informed water management. Data that are findable, accessible, interoperable, and reusable—or FAIR —are now prerequisite to responsible data stewardship. In contrast to FAIR, accessibility and usability case studies and guidelines designed around human access and understanding are lacking in the literature, especially for water resources. Such decision support guidelines are critical because (i) inherent visual design trade‐offs are not best made using intuition or feedback (perceived preference), and (ii) choosing designs requires a nuanced understanding of why and how the design works (revealed effectiveness). Thus, the goal of this commentary is to highlight knowledge gaps and discuss a general usability testing method which can be applied to any water resources decision support product. The user‐testing approach includes (i) interviews about visualization goals, audiences, and the uses and decisions made with the data products, (ii) diagnosis of usability challenges, and (iii) redesign of decision support products given best practices and control versus treatment with intended end‐user audiences. We illustrate the method using high‐profile U.S. Geological Survey water science products. In sum, optimizing and testing for usability and understandability are as central to stakeholder use as FAIR standards are, and warrant being part of the development of data products and geovisualizations.
Exact pattern matching. Current achievements and research

The problem of exact pattern matching is an essential programming problem. Different algorithms that solve this problem are core elements of search engines, version control systems, text editors, DNA analyzers, and many others. For simplification reasons articles usually denote pattern as P or p and pattern length as M or m. Similarly, the text is usually denoted as T or t and its length - N or n. Alphabet is denoted Σ and its length - |Σ|. Based on these notations the problem of pattern matching can be written as follows: Find all positions/ amount of i, such that P[0...m] = T[i...i + m], or: Find all positions i in text for which substring starting at position i of the text of length m is equal to the pattern. The main parameters of this problem are pattern length and alphabet size. The length of the text usually doesn’t matter because, for any long enough text of a specific structure, the run time of the algorithm per character will be close to constant. Besides that, the specifics of the input data and text may also impact the performances of the algorithms. All of that makes the problem both very nuanced and interesting to investigate. This problem features a lot of different existing solutions developed over the course of the last 5 decades. The main part of the work provides short descriptions and analyses of a set of algorithms that are still relevant in the field. Besides that, some remarks are made on the topic of their theoretical regions of efficiency and how they depend on the specifics of the input. The results of the practical experimentation on the variety of randomly generated test data are provided. The conclusion provides some analysis of the received results and algorithms’ class efficiency based on the input as well as a visual representation of the received results in a form of a table representing the most efficient algorithm for each pair of pattern length and alphabet size.