Reviewerly/exHarmony huggingface dataset:
Cryptographic Mechanisms that Ensure the Efficiency of SNARK-Systems

The section provides, strictly substantiated, estimates of the resistance of the block encryption algorithm HadesMiMC and the hash function Poseidon to linear and differential attacks. Based on the obtained results, specific parameters for the HadesMiMC encryption algorithm and the Poseidon hash function were determined, which allow them to be used for recurrent SNARK-proofs based on MNT-4 and MNT-6 triplets. It is also shown how better to choose S-boxes for these algorithms, so that this choice was optimal both in terms of stability and in terms of complexity of calculations in SNARK-proofs. It is determined how many rounds are sufficient to guarantee the stability of algorithms for different options. The number of constraints per bit is calculated for different options, and it is shown that it is significantly smaller than for the Pedersen function, which is currently used in the cryptocurrency Zcash. The significant advantages of the considered HadesMiMC block encryption algorithm and the corresponding Poseidon hash function when using these cryptographic primitives in the blockchain as a decentralized environment, an additional function of which is to ensure the anonymity of personal information, are substantiated. These advantages are both a reasonably high level of resistance to known attacks on such cryptographic primitives, and a significant increase in speed (up to 15 times) in the construction of appropriate SNARK-proofs, which are an integral part of the mechanisms of anonymity in the blockchain. The results showed that the HadesMiMC block encryption algorithm, based on operations over a simple finite field, is secure against linear and differential cryptanalysis, with the correct choice of parameters of this algorithm. As a result, the Poseidon hash function, built using the SPONGE design and the HadesMiMC permutation, will be stable. It was also shown how the parameters of the algorithm should be selected to ensure its specified level of security. In addition, the number of constraints per bit of information was calculated and it was shown that the HadesMiMC algorithm and the Poseidon hash function currently have no competitors when used in SNARK-proofs.
Deepfake Detection for Facial Images with Facemasks

Hyper-realistic face image generation and manipulation have givenrise to numerous unethical social issues, e.g., invasion of privacy,threat of security, and malicious political maneuvering, which re-sulted in the development of recent deepfake detection methods with the rising demands of deepfake forensics. Proposed deepfake detection methods to date have shown remarkable detection performance and robustness. However, none of the suggested deepfake detection methods assessed the performance of deepfakes with the facemask during the pandemic crisis after the outbreak of theCovid-19. In this paper, we thoroughly evaluate the performance of state-of-the-art deepfake detection models on the deepfakes with the facemask. Also, we propose two approaches to enhance the masked deepfakes detection: face-patch and face-crop. The experimental evaluations on both methods are assessed through the base-line deepfake detection models on the various deepfake datasets. Our extensive experiments show that, among the two methods, face-crop performs better than the face-patch, and could be a train method for deepfake detection models to detect fake faces with facemask in real world.
Deep neural network approximation theory for high-dimensional functions

The purpose of this article is to develop machinery to study the capacity of deep neural networks (DNNs) to approximate high-dimensional functions. In particular, we show that DNNs have the expressive power to overcome the curse of dimensionality in the approximation of a large class of functions. More precisely, we prove that these functions can be approximated by DNNs on compact sets such that the number of parameters necessary to represent the approximating DNNs grows at most polynomially in the reciprocal $1/\varepsilon$ of the approximation accuracy $\varepsilon>0$ and in the input dimension $d\in \mathbb{N} =\{1,2,3,\dots\}$. To this end, we introduce certain approximation spaces, consisting of sequences of functions that can be efficiently approximated by DNNs. We then establish closure properties which we combine with known and new bounds on the number of parameters necessary to approximate locally Lipschitz continuous functions, maximum functions, and product functions by DNNs. The main result of this article demonstrates that DNNs have sufficient expressiveness to approximate certain sequences of functions which can be constructed by means of a finite number of compositions using locally Lipschitz continuous functions, maxima, and products without the curse of dimensionality.
Automated Atlas-based Segmentation of Single Coronal Mouse Brain Slices  using Linear 2D-2D Registration

A significant challenge for brain histological data analysis is to precisely identify anatomical regions in order to perform accurate local quantifications and evaluate therapeutic solutions. Usually, this task is performed manually, becoming therefore tedious and subjective. Another option is to use automatic or semi-automatic methods, among which segmentation using digital atlases co-registration. However, most available atlases are 3D, whereas digitized histological data are 2D. Methods to perform such 2D-3D segmentation from an atlas are required. This paper proposes a strategy to automatically and accurately segment single 2D coronal slices within a 3D volume of atlas, using linear registration. We validated its robustness and performance using an exploratory approach at whole-brain scale.
Recurrent Neural Networks

In the previous chapters, we’ve seen that simple data that can be expressed as a vector can be easily provided to the input layers of a feedforward neural network. More complex data like images can either be transformed and flattened to be sent as input in vector form or can be used to learn filters in a convolutional neural network (CNN). CNNs helped capture essential patterns in the data that occur due to certain values present in the proximity of a pixel. However, there’s another pattern that usually occurs in data formats like text, speech, etc., as shown in Figure 15-1.
A Piece-wise Polynomial Filtering Approach for Graph Neural Networks

Graph Neural Networks (GNNs) exploit signals from node features and the input graph topology to improve node classification task performance. However, these models tend to perform poorly on heterophilic graphs, where connected nodes have different labels. Recently proposed GNNs work across graphs having varying levels of homophily. Among these, models relying on polynomial graph filters have shown promise. We observe that solutions to these polynomial graph filter models are also solutions to an overdetermined system of equations. It suggests that in some instances, the model needs to learn a reasonably high order polynomial. On investigation, we find the proposed models ineffective at learning such polynomials due to their designs. To mitigate this issue, we perform an eigendecomposition of the graph and propose to learn multiple adaptive polynomial filters acting on different subsets of the spectrum. We theoretically and empirically show that our proposed model learns a better filter, thereby improving classification accuracy. We study various aspects of our proposed model including, dependency on the number of eigencomponents utilized, latent polynomial filters learned, and performance of the individual polynomials on the node classification task. We further show that our model is scalable by evaluating over large graphs. Our model achieves performance gains of up to 5% over the state-of-the-art models and outperforms existing polynomial filter-based approaches in general.
Domain Adaptation of Networks for Camera Pose Estimation: Learning  Camera Pose Estimation Without Pose Labels

One of the key criticisms of deep learning is that large amounts of expensive and difficult-to-acquire training data are required in order to train models with high performance and good generalization capabilities. Focusing on the task of monocular camera pose estimation via scene coordinate regression (SCR), we describe a novel method, Domain Adaptation of Networks for Camera pose Estimation (DANCE), which enables the training of models without access to any labels on the target task. DANCE requires unlabeled images (without known poses, ordering, or scene coordinate labels) and a 3D representation of the space (e.g., a scanned point cloud), both of which can be captured with minimal effort using off-the-shelf commodity hardware. DANCE renders labeled synthetic images from the 3D model, and bridges the inevitable domain gap between synthetic and real images by applying unsupervised image-level domain adaptation techniques (unpaired image-to-image translation). When tested on real images, the SCR model trained with DANCE achieved comparable performance to its fully supervised counterpart (in both cases using PnP-RANSAC for final pose estimation) at a fraction of the cost. Our code and dataset are available at https://github.com/JackLangerman/dance
Spatial Search on Johnson Graphs by Discrete-Time Quantum Walk

The spatial search problem aims to find a marked vertex of a finite graph using a dynamic with two constraints: (1) The walker has no compass and (2) the walker can check whether a vertex is marked only after reaching it. This problem is a generalization of unsorted database search and has many applications to algorithms. Classical algorithms that solve the spatial search problem are based on random walks and the computational complexity is determined by the hitting time. On the other hand, quantum algorithms are based on quantum walks and the computational complexity is determined not only by the number of steps to reach a marked vertex, but also by the success probability, since we need to perform a measurement at the end of the algorithm to determine the walker's position. In this work, we address the spatial search problem on Johnson graphs using the coined quantum walk model. Since Johnson graphs are vertex- and distance-transitive, we have found an invariant subspace of the Hilbert space, which aids in the calculation of the computational complexity. We have shown that, for every fixed diameter, the asymptotic success probability is $1/2$ after taking $\pi\sqrt N/(2\sqrt 2)$ steps, where $N$ is the number of vertices of the Johnson graph.
Choose Your Programming Copilot: A Comparison of the Program Synthesis  Performance of GitHub Copilot and Genetic Programming

GitHub Copilot, an extension for the Visual Studio Code development environment powered by the large-scale language model Codex, makes automatic program synthesis available for software developers. This model has been extensively studied in the field of deep learning, however, a comparison to genetic programming, which is also known for its performance in automatic program synthesis, has not yet been carried out. In this paper, we evaluate GitHub Copilot on standard program synthesis benchmark problems and compare the achieved results with those from the genetic programming literature. In addition, we discuss the performance of both approaches. We find that the performance of the two approaches on the benchmark problems is quite similar, however, in comparison to GitHub Copilot, the program synthesis approaches based on genetic programming are not yet mature enough to support programmers in practical software development. Genetic programming usually needs a huge amount of expensive hand-labeled training cases and takes too much time to generate solutions. Furthermore, source code generated by genetic programming approaches is often bloated and difficult to understand. For future work on program synthesis with genetic programming, we suggest researchers to focus on improving the execution time, readability, and usability.
Weighted Histogram Equalization Using Entropy of Probability Density  Function

Low-contrast image enhancement is essential for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to increase the visibility of an image while maintaining its naturalness. In this paper, the weighted histogram equalization using the entropy of the probability density function is proposed. The computation of the local mapping functions utilizes the relationship between non-height bin and height bin distributions. Finally, the complete tone mapping function is produced by concatenating local mapping functions. Computer simulation results on the CSIQ dataset demonstrate that the proposed method produces images with higher visibility and visual quality, which outperforms traditional and recently proposed contrast enhancement algorithms methods in qualitative and quantitative metrics.
Tilt Angle Estimation for Building Integrated Photo-voltaics - An Investigative Study

This paper demonstrates an investigation procedure to determine the optimum tilt angle for eight different regions of Bangladesh, a country of South Asia situated in the Northern Hemisphere. The work focuses on determining one tilt angle for each region for a whole year and finally finds out the suitable areas to accumulate more incident energy through installing photovoltaic panels that are tilted at an optimum angle. The simulation outcome observation also shows that the energy accumulation increases because of the optimization of the tilt angle of photovoltaic panels. From the result analysis, the paper observed that location-wise the optimum tilt angle varies from 25.57° to 22.10°. Moreover, the study also finds out that the Southern Region of Bangladesh is more suitable to install building-integrated photovoltaic tilting the solar panels at an optimized angle because the tilted photovoltaic panels in the Southern region can harness almost 9% −10% more energy than Northern Region.
One-Shot Generative Domain Adaptation

This work aims at transferring a Generative Adversarial Network (GAN) pre-trained on one image domain to a new domain referring to as few as just one target image. The main challenge is that, under limited supervision, it is extremely difficult to synthesize photo-realistic and highly diverse images, while acquiring representative characters of the target. Different from existing approaches that adopt the vanilla fine-tuning strategy, we import two lightweight modules to the generator and the discriminator respectively. Concretely, we introduce an attribute adaptor into the generator yet freeze its original parameters, through which it can reuse the prior knowledge to the most extent and hence maintain the synthesis quality and diversity. We then equip the well-learned discriminator backbone with an attribute classifier to ensure that the generator captures the appropriate characters from the reference. Furthermore, considering the poor diversity of the training data (i.e., as few as only one image), we propose to also constrain the diversity of the generative domain in the training process, alleviating the optimization difficulty. Our approach brings appealing results under various settings, substantially surpassing state-of-the-art alternatives, especially in terms of synthesis diversity. Noticeably, our method works well even with large domain gaps, and robustly converges within a few minutes for each experiment.
Opportunities for Using Health Information Technology for Elderly Care in the Emergency Departments: A Qualitative Study.

Recently, several technologies have been developed for being used in the field of geriatric emergency medicine. As a large number of elderly patients visit emergency departments, the use of health information technology in this department can help to improve patient care and control the outcome of diseases. The present study aimed to identify opportunities for using various health information technologies for elderly care in the emergency department.This qualitative study was conducted in 2020. The participants included geriatricians, geriatric nurses, emergency medicine specialists, and nurses who worked in the emergency department. In total, 33 semi-structured interviews were conducted, and data were analyzed by using framework analysis method and MAXQDA software.The findings of the qualitative study included four main themes, nine subthemes, and 20 categories. The main themes were the common process of elderly care in the emergency departments, data required for elderly care in the emergency departments, the elderly treatment team, and current information technologies used in the emergency departments for elderly care. Overall, the results showed that there was no specific workflow for elderly care in the emergency departments; the great workload of this department prevents the clinicians to conduct cognitive and functional assessments; geriatricians were not involved in the care process; and none of the current information systems were designed specifically for elderly patients. It seems that using specific heath information technology for elderly care in the emergency department can help to overcome current challenges.Identifying opportunities for using health information technologies for geriatric patients in the emergency department can lead to better use of financial, physical, and human resources, and improve staff performance. These systems can be designed and used for different purposes such as reducing work load, readmissions, and hospitalization. Improving access to data and better collaboration between different specialties are other benefits of using these systems. However, more research is required to evaluate the effectiveness of technology in this area.
Disentangled Unsupervised Image Translation via Restricted Information  Flow

Unsupervised image-to-image translation methods aim to map images from one domain into plausible examples from another domain while preserving structures shared across two domains. In the many-to-many setting, an additional guidance example from the target domain is used to determine domain-specific attributes of the generated image. In the absence of attribute annotations, methods have to infer which factors are specific to each domain from data during training. Many state-of-art methods hard-code the desired shared-vs-specific split into their architecture, severely restricting the scope of the problem. In this paper, we propose a new method that does not rely on such inductive architectural biases, and infers which attributes are domain-specific from data by constraining information flow through the network using translation honesty losses and a penalty on the capacity of domain-specific embedding. We show that the proposed method achieves consistently high manipulation accuracy across two synthetic and one natural dataset spanning a wide variety of domain-specific and shared attributes.
Cryptanalyze and design strong S-Box using 2D chaotic map and apply to  irreversible key expansion

Cryptanalysis result of key expansion algorithms in AES and SM4 revealed that, (1) there exist weaknesses in their S-Boxes, and (2) the round key expansion algorithm is reversible, i.e., the initial key can be recovered from any round key, which may be an exploitable weakness by attacker. To solve these problems, first we constructed a non-degenerate 2D exponential hyper chaotic map (2D-ECM), derived the recursion formula to calculate the number of S-Boxes that satisfied three conditions, and designed a strong S-Box construction algorithm without weakness. Then based on 2D-ECM and S-Box, we designed an irreversible key expansion algorithm, to transform the initial key into independent round keys, to make the initial key can not be recovered from any round key. Security and statistical analysis demonstrated the flexible and effectiveness of the proposed irreversible key expansion algorithm.
Extrapolation Frameworks in Cognitive Psychology Suitable for Study of  Image Classification Models

We study the functional task of deep learning image classification models and show that image classification requires extrapolation capabilities. This suggests that new theories have to be developed for the understanding of deep learning as the current theory assumes models are solely interpolating, leaving many questions about them unanswered. We investigate the pixel space and also the feature spaces extracted from images by trained models (in their hidden layers, including the 64-dimensional feature space in the last hidden layer of pre-trained residual neural networks), and also the feature space extracted by wavelets/shearlets. In all these domains, testing samples considerably fall outside the convex hull of training sets, and image classification requires extrapolation. In contrast to the deep learning literature, in cognitive science, psychology, and neuroscience, extrapolation and learning are often studied in tandem. Moreover, many aspects of human visual cognition and behavior are reported to involve extrapolation. We propose a novel extrapolation framework for the mathematical study of deep learning models. In our framework, we use the term extrapolation in this specific way of extrapolating outside the convex hull of training set (in the pixel space or feature space) but within the specific scope defined by the training data, the same way extrapolation is defined in many studies in cognitive science. We explain that our extrapolation framework can provide novel answers to open research problems about deep learning including their over-parameterization, their training regime, out-of-distribution detection, etc. We also see that the extent of extrapolation is negligible in learning tasks where deep learning is reported to have no advantage over simple models.
Improving the robustness and accuracy of biomedical language models  through adversarial training

Deep transformer neural network models have improved the predictive accuracy of intelligent text processing systems in the biomedical domain. They have obtained state-of-the-art performance scores on a wide variety of biomedical and clinical Natural Language Processing (NLP) benchmarks. However, the robustness and reliability of these models has been less explored so far. Neural NLP models can be easily fooled by adversarial samples, i.e. minor changes to input that preserve the meaning and understandability of the text but force the NLP system to make erroneous decisions. This raises serious concerns about the security and trust-worthiness of biomedical NLP systems, especially when they are intended to be deployed in real-world use cases. We investigated the robustness of several transformer neural language models, i.e. BioBERT, SciBERT, BioMed-RoBERTa, and Bio-ClinicalBERT, on a wide range of biomedical and clinical text processing tasks. We implemented various adversarial attack methods to test the NLP systems in different attack scenarios. Experimental results showed that the biomedical NLP models are sensitive to adversarial samples; their performance dropped in average by 21 and 18.9 absolute percent on character-level and word-level adversarial noise, respectively. Conducting extensive adversarial training experiments, we fine-tuned the NLP models on a mixture of clean samples and adversarial inputs. Results showed that adversarial training is an effective defense mechanism against adversarial noise; the models robustness improved in average by 11.3 absolute percent. In addition, the models performance on clean data increased in average by 2.4 absolute present, demonstrating that adversarial training can boost generalization abilities of biomedical NLP systems.