Reviewerly/exHarmony huggingface dataset:
Synthetic Data-based Detection of Zebras in Drone Imagery

Nowadays, there is a wide availability of datasets that enable the training of common object detectors or human detectors. These come in the form of labelled real-world images and require either a significant amount of human effort, with a high probability of errors such as missing labels, or very constrained scenarios, e.g. VICON systems. On the other hand, uncommon scenarios, like aerial views, animals, like wild zebras, or difficult-to-obtain information, such as human shapes, are hardly available. To overcome this, synthetic data generation with realistic rendering technologies has recently gained traction and advanced research areas such as target tracking and human pose estimation. However, subjects such as wild animals are still usually not well represented in such datasets. In this work, we first show that a pre-trained YOLO detector can not identify zebras in real images recorded from aerial viewpoints. To solve this, we present an approach for training an animal detector using only synthetic data. We start by generating a novel synthetic zebra dataset using GRADE, a state-of-the-art framework for data generation. The dataset includes RGB, depth, skeletal joint locations, pose, shape and instance segmentations for each subject. We use this to train a YOLO detector from scratch. Through extensive evaluations of our model with real-world data from i) limited datasets available on the internet and ii) a new one collected and manually labelled by us, we show that we can detect zebras by using only synthetic data during training. The code, results, trained models, and both the generated and training data are provided as open-source at https://eliabntt.github.io/grade-rr.
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness

Emerging quantum computing technologies, such as Noisy Intermediate-Scale Quantum (NISQ) devices, offer potential advancements in solving mathematical optimization problems. However, limitations in qubit availability, noise, and errors pose challenges for practical implementation. In this study, we examine two decomposition methods for Mixed-Integer Linear Programming (MILP) designed to reduce the original problem size and utilize available NISQ devices more efficiently. We concentrate on breaking down the original problem into smaller subproblems, which are then solved iteratively using a combined quantum-classical hardware approach. We conduct a detailed analysis for the decomposition of MILP with Benders and Dantzig-Wolfe methods. In our analysis, we show that the number of qubits required to solve Benders is exponentially large in the worst-case, while remains constant for Dantzig-Wolfe. Additionally, we leverage Dantzig-Wolfe decomposition on the use-case of certifying the robustness of ReLU networks. Our experimental results demonstrate that this approach can save up to 90\% of qubits compared to existing methods on quantum annealing and gate-based quantum computers.
Neural Radiance Fields (NeRFs): A Review and Some Recent Developments

Neural Radiance Field (NeRF) is a framework that represents a 3D scene in the weights of a fully connected neural network, known as the Multi-Layer Perception(MLP). The method was introduced for the task of novel view synthesis and is able to achieve state-of-the-art photorealistic image renderings from a given continuous viewpoint. NeRFs have become a popular field of research as recent developments have been made that expand the performance and capabilities of the base framework. Recent developments include methods that require less images to train the model for view synthesis as well as methods that are able to generate views from unconstrained and dynamic scene representations.
Towards Computational Architecture of Liberty: A Comprehensive Survey on Deep Learning for Generating Virtual Architecture in the Metaverse

3D shape generation techniques leveraging deep learning have garnered significant interest from both the computer vision and architectural design communities, promising to enrich the content of the future metaverse. However, research on virtual architectural design remains limited, particularly regarding human-AI collaboration and deep learning-assisted design. We first illuminate the principles, generation techniques, and current literature of virtual architecture, focusing on challenges such as datasets, multimodality, design intuition, and generative frameworks. In our survey, we reviewed 187 related articles (80.7\% of articles published between 2018 and 2022) covering architectural research, virtual environments, and technical approaches. This survey investigates the latest approaches to 3D object generation with deep generative models (DGMs) and summarizes four characteristics of deep-learning generation approaches for virtual architecture. According to our analysis of the survey, we expound on four research agendas, including agency, communication, user consideration, and integrating tools, and highlight three important enablers of ubiquitous interaction with immersive systems in deep learning-assisted architectural generation. Our work contributes to fostering understanding between designers and deep learning techniques, broadening access to human-AI collaboration. We advocate for interdisciplinary efforts to address this timely research topic, facilitating content designing and generation in the metaverse.
Quaternion Matrix Completion Using Untrained Quaternion Convolutional Neural Network for Color Image Inpainting

The use of quaternions as a novel tool for color image representation has yielded impressive results in color image processing. By considering the color image as a unified entity rather than separate color space components, quaternions can effectively exploit the strong correlation among the RGB channels, leading to enhanced performance. Especially, color image inpainting tasks are highly beneficial from the application of quaternion matrix completion techniques, in recent years. However, existing quaternion matrix completion methods suffer from two major drawbacks. First, it can be difficult to choose a regularizer that captures the common characteristics of natural images, and sometimes the regularizer that is chosen based on empirical evidence may not be the optimal or efficient option. Second, the optimization process of quaternion matrix completion models is quite challenging because of the non-commutativity of quaternion multiplication. To address the two drawbacks of the existing quaternion matrix completion approaches mentioned above, this paper tends to use an untrained quaternion convolutional neural network (QCNN) to directly generate the completed quaternion matrix. This approach replaces the explicit regularization term in the quaternion matrix completion model with an implicit prior that is learned by the QCNN. Extensive quantitative and qualitative evaluations demonstrate the superiority of the proposed method for color image inpainting compared with some existing quaternion-based and tensor-based methods.
Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments

Motion planning of autonomous agents in partially known environments with incomplete information is a challenging problem, particularly for complex tasks. This paper proposes a model-free reinforcement learning approach to address this problem. We formulate motion planning as a probabilistic-labeled partially observable Markov decision process (PL-POMDP) problem and use linear temporal logic (LTL) to express the complex task. The LTL formula is then converted to a limit-deterministic generalized B\"uchi automaton (LDGBA). The problem is redefined as finding an optimal policy on the product of PL-POMDP with LDGBA based on model-checking techniques to satisfy the complex task. We implement deep Q learning with long short-term memory (LSTM) to process the observation history and task recognition. Our contributions include the proposed method, the utilization of LTL and LDGBA, and the LSTM-enhanced deep Q learning. We demonstrate the applicability of the proposed method by conducting simulations in various environments, including grid worlds, a virtual office, and a multi-agent warehouse. The simulation results demonstrate that our proposed method effectively addresses environment, action, and observation uncertainties. This indicates its potential for real-world applications, including the control of unmanned aerial vehicles (UAVs).
On APN functions and their derivatives

We determine a connection between the weight of a Boolean function and the total weight of its first-order derivatives. The relationship established is used to study some cryptographic properties of Boolean functions. We establish a characterization of APN permutations in terms of the weight of the first-order derivatives of their components. We also characterize APN functions by the total weight of the second-order derivatives of their components. The total weight of the first-order and second-order derivatives for functions such as permutations, bent, partially-bent, quadratic, plateaued and balanced functions is determined.
Quantum Solvable Nonlinear Differential Equations

Quantum computers have the potential to efficiently solve nonlinear ordinary differential equations (ODEs), which play a crucial role in various industries and scientific fields. However, it remains unclear which nonlinear ODEs, and under what assumptions, can achieve exponential speedup using a quantum computer. In this work, we introduce a class of nonlinear ODEs, called quantum solvable ODEs, that can be efficiently solved on quantum computers, where the efficiency is defined as solving the ODE with computational complexity of polylog(N) for a number N of variables in ODEs. Specifically, we employ Koopman-von-Neumann linearization to map nonliner ODEs to Hamiltonian dynamics and find conditions where the norm of the mapped Hamiltonian is preserved and the mapped Hamiltonian is sparse. This allows us to use the optimal Hamiltonian simulation technique for solving the quantum solvable ODEs with polylog(N) overhead. We further show that quantum solvable ODEs include a wide range of nonlinear ODEs, such as the extended short-range Kuramoto model. Since this is the first concrete example of solving nonlinear differential equations with exponential quantum speedup, these findings contribute significantly to the application of quantum computers in solving nonlinear problems.
Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning

Cameras digitize real-world scenes as pixel intensity values with a limited value range given by the available bits per pixel (bpp). High Dynamic Range (HDR) cameras capture those luminance values in higher resolution through an increase in the number of bpp. Most displays, however, are limited to 8 bpp. Naive HDR compression methods lead to a loss of the rich information contained in those HDR images. In this paper, tone mapping algorithms for thermal infrared images with 16 bpp are investigated that can preserve this information. An optimized multi-scale Retinex algorithm sets the baseline. This algorithm is then approximated with a deep learning approach based on the popular U-Net architecture. The remaining noise in the images after tone mapping is reduced implicitly by utilizing a self-supervised deep learning approach that can be jointly trained with the tone mapping approach in a multi-task learning scheme. Further discussions are provided on denoising and deflickering for thermal infrared video enhancement in the context of tone mapping. Extensive experiments on the public FLIR ADAS Dataset prove the effectiveness of our proposed method in comparison with the state-of-the-art.
Routing Protocols for Quantum Networks: Overview and Challenges

Over the past 50 years, conventional network routing design has undergone substantial growth, evolving from small networks with static nodes to large systems connecting billions of devices. This progress has been achieved through the separation of concerns principle, which entails integrating network functionalities into a graph or random network design and employing specific network protocols to facilitate diverse communication capabilities. This paper aims to highlight the potential of designing routing techniques for quantum networks, which exhibit unique properties due to quantum mechanics. Quantum routing design requires a substantial deviation from conventional network design protocols since it must account for the unique features of quantum entanglement and information. However, implementing these techniques poses significant challenges, such as decoherence and noise in quantum systems, restricted communication ranges, and highly specialized hardware prerequisites. The paper commences by examining essential research on quantum routing design methods and proceeds to cover fundamental aspects of quantum routing, associated quantum operations, and the steps necessary for building efficient and robust quantum networks. This paper summarizes the present state of quantum routing techniques, including their principles, protocols, and challenges, highlighting potential applications and future directions.
SGX Switchless Calls Made Configless

Intel's software guard extensions (SGX) provide hardware enclaves to guarantee confidentiality and integrity for sensitive code and data. However, systems leveraging such security mechanisms must often pay high performance overheads. A major source of this overhead is SGX enclave transitions which induce expensive cross-enclave context switches. The Intel SGX SDK mitigates this with a switchless call mechanism for transitionless cross-enclave calls using worker threads. Intel's SGX switchless call implementation improves performance but provides limited flexibility: developers need to statically fix the system configuration at build time, which is error-prone and misconfigurations lead to performance degradations and waste of CPU resources. ZC-SWITCHLESS is a configless and efficient technique to drive the execution of SGX switchless calls. Its dynamic approach optimises the total switchless worker threads at runtime to minimise CPU waste. The experimental evaluation shows that ZC-SWITCHLESS obviates the performance penalty of misconfigured switchless systems while minimising CPU waste.
How to address monotonicity for model risk management?

In this paper, we study the problem of establishing the accountability and fairness of transparent machine learning models through monotonicity. Although there have been numerous studies on individual monotonicity, pairwise monotonicity is often overlooked in the existing literature. This paper studies transparent neural networks in the presence of three types of monotonicity: individual monotonicity, weak pairwise monotonicity, and strong pairwise monotonicity. As a means of achieving monotonicity while maintaining transparency, we propose the monotonic groves of neural additive models. As a result of empirical examples, we demonstrate that monotonicity is often violated in practice and that monotonic groves of neural additive models are transparent, accountable, and fair.
Interpreting Deep Forest through Feature Contribution and MDI Feature Importance

Deep forest is a non-differentiable deep model which has achieved impressive empirical success across a wide variety of applications, especially on categorical/symbolic or mixed modeling tasks. Many of the application fields prefer explainable models, such as random forests with feature contributions that can provide local explanation for each prediction, and Mean Decrease Impurity (MDI) that can provide global feature importance. However, deep forest, as a cascade of random forests, possesses interpretability only at the first layer. From the second layer on, many of the tree splits occur on the new features generated by the previous layer, which makes existing explanatory tools for random forests inapplicable. To disclose the impact of the original features in the deep layers, we design a calculation method with an estimation step followed by a calibration step for each layer, and propose our feature contribution and MDI feature importance calculation tools for deep forest. Experimental results on both simulated data and real world data verify the effectiveness of our methods.
Large Linguistic Models: Analyzing theoretical linguistic abilities of LLMs

The performance of large language models (LLMs) has recently improved to the point where the models can perform well on many language tasks. We show here that for the first time, the models can also generate coherent and valid formal analyses of linguistic data and illustrate the vast potential of large language models for analyses of their metalinguistic abilities. LLMs are primarily trained on language data in the form of text; analyzing and evaluating their metalinguistic abilities improves our understanding of their general capabilities and sheds new light on theoretical models in linguistics. In this paper, we probe into GPT-4's metalinguistic capabilities by focusing on three subfields of formal linguistics: syntax, phonology, and semantics. We outline a research program for metalinguistic analyses of large language models, propose experimental designs, provide general guidelines, discuss limitations, and offer future directions for this line of research. This line of inquiry also exemplifies behavioral interpretability of deep learning, where models' representations are accessed by explicit prompting rather than internal representations.
IMPLEMENTATION OF THE DIGITAL OPEN SOURCES IN THE CRIMINAL INVESTIGATION PROCESSES

The article explores the prospects of using digital open sources when applying modern investigative methods.The publication provides an overview of the benefits of using electronic open sources in criminal and human rights investigations, including their potential to reduce costs and the ability to quickly and efficiently retrieve large volumes of data.The study also notes the possible shortcomings of using digital open sources, in particular, the reliability of the obtained materials.To ensure the accuracy and reliability of data collected from open electronic sources, the study highlights the need to provide investigative bodies with appropriate guidance.The article also emphasizes the need to apply ethical principles in the process of using digital open sources in investigations.The author gives examples of national and international practices of using special means in the investigation process and modern trends in collecting and researching evidence obtained from open sources.In order to preserve individual rights and ensure that investigations are conducted in a fair and reasonable manner, there is a need for a clearly defined ethical legal framework to guide investigators in the use of digital open sources.The benefits and importance of existing legal requirements that ensure impartial investigation of violations while respecting the rights of all parties are also discussed in this publication.The study highlights the importance of a sound regulatory framework governing the use of electronic evidence.The author advocates the need to create a procedure and standards for obtaining such evidence, in particular data for forensic examination.The author cites the experience of law enforcement agencies in Ukraine as an example of the basic development of rules for the use of digital data, in particular during a criminal investigation.
CRIMINOLOGY CHARACTERISTICS OF CYBERCRIME

Продан Т.В., к.ю.н., доцент, асистент кафедри
Pseudo Random Numbers Generated By Using Mixture Of Modular Function And Tent Map

In this paper, we develop an algorithm in generating random numbers from the mixture of the modular function and the tent map. Results show that there are more random numbers generated from the mixture compared to the random numbers generated purely from the modular function and the tent map. Moreover, majority of the distributions of the generated random numbers for the mod100 are uniform. However, the distributions of the generated random numbers for the mod1000 are not uniform. Lastly, the sequences of random numbers generated for divisor of mod100 and mod1000 behave to be random.
Prediction of Breast Cancer Via Deep Learning

With the continuous progress of machine learning in image processing, artificial neural networks have more and more applications in medical image processing. Aiming at the method of selecting a BP neural network to realize the diagnosis of breast cancer pathological images, the method of deep learning is selected for prediction, which improves the prediction accuracy and obtains a more effective diagnosis effect.
Gradually Generative Adversarial Networks Method for Imbalanced Datasets

Imbalanced dataset can cause obstacles to classification and result in a decrease in classification performance.There are several methods that can be used to deal the data imbalances, such as methods based on SMOTE and Generative Adversarial Networks (GAN).These methods are used for overcoming data oversampling so that the amount of minority data can increase and it can reach a balance with the majority data.In this research, the selected dataset is classified as a small imbalanced dataset of less than 200 records.The proposed method is the Gradually Generative Adversarial Network (GradGAN) model which aims to handle data imbalances gradually.The stages of the GradGAN model are adding the original minority dataset gradually so that it will create new minority datasets until a balance of data is created.Based on the algorithm flow described, the minority data is multiplied by the value of the variable that has been determined repeatedly to produce new balanced minority data.The test results on the classification of datasets from the GradGAN model produce an accuracy value of 8.3% when compare to that without GradGAN.
Rain Streaks Removal in Images using Extended Generative Adversarial-based Deraining Framework

The visual quality of photographs and videos can be negatively impacted by various weather conditions, such as snow, haze, or rain, affecting the quality of the images and videos.Such impacts may greatly affect outdoor vision systems that rely on image/video data.It has recently drawn a lot of interest to remove rain streaks from a single image.Several deep learning-based methods have been introduced to address the issue of removing rain streaks from a single image.Still, the efficiency of rain streak removal with enhanced quality is challenging.Hence, a novel deep-learning method is introduced for rain streak removal.The proposed Extended Generative Adversarial based De-raining (Ex_GADerain) is the enhanced version of a traditional Generative adversarial network (GAN).The proposed Ex_GADerain introduced a Self-Attention based Convolutional Capsule Bidirectional Network (SA-CCapBiNet) based generator for enhancing the rain streaks removal process.Also, the loss function estimation using the adversarial loss and the mean absolute error loss minimizes the information loss during training.The minimal information loss enhances the generalization capability of Ex_GADerain, and hence the enhanced performance is acquired.The quality assessment of a derained image based on various assessment measures like SSIM, PSNR, RMSE, and DSSIM improved performance compared to the conventional rain streak removal methods.The maximal SSIM and PSNR acquired by the Ex_GADerain are 0.9923 and 26.7052, respectively.The minimal RMSE and DSSIM acquired by the Ex_GADerain are 0.9367 and 0.0051, respectively.