Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence strategy of the Uzbekistan: Policy framework, Preferences, and challenges

Artificial intelligence strategies refer to the plans and actions taken by governments to develop and apply AI technologies to achieve specific goals. This article explores Uzbekistan's policies and preferences regarding the development and implementation of artificial intelligence (AI) technologies. The study examines the country's national strategies and regulatory frameworks for AI, as well as the challenges it faces in realizing its AI ambitions. The analysis reveals that Uzbekistan sees AI as a key enabler of economic growth, social development, and modernization, and aims to become a regional leader in AI by 2030. To achieve this goal, the government has launched several initiatives, such as establishing AI research centers, promoting entrepreneurship and innovation, and investing in digital infrastructure. However, the article also identifies several obstacles, such as a lack of skilled workforce, limited funding, and ethical and legal concerns. The study concludes by providing recommendations for how Uzbekistan can address these challenges and strengthen its AI ecosystem.
Review of Blockchain Applications in Food Supply Chains

Blockchain has found wide acceptance not just in the DeFi and Crypto space, but also in digital supply chains, non-monetary transactions, and governance. Amongst many, the food supply chain is riddled with lots of inefficiencies and untraceable corruption. Hence, many have investigated the integration of blockchain technology into the food system. This paper discusses the major advancement in blockchain technology from the aspect of food security and proposes roadmaps for future applications in businesses. We dive into the different pillars of food security and how blockchains can play a valuable role in the technology infrastructure of food security in a holistic sense. Next, the paper also discusses the organizational, economic, and management aspects of technology adoption. Finally, we end by discussing the nexus between Blockchain and Decentralized Autonomous Organizations (DAO), as well as Digital Twins, respectively.
Neural architecture search with interpretable meta-features and fast predictors

Neural Architecture Search (NAS) is well-known for automatizing neural architecture design and finding better architectures. Although NAS methods have shown substantial progress over the years, most still suffer from data inefficiency, high model complexity, and lack of interpretability. This paper proposes a solution for these problems by introducing a Prediction-based and interpretable Meta-Learning method called MbML-NAS, capable of generalizing to different search spaces and datasets using less data than several state-of-the-art NAS methods. The proposal uses interpretable meta-features extracted from neural architectures and regression models as meta-predictors to infer Convolutional Networks performances. Experiments compared MbML-NAS with a graph-based Neural Predictor, state-of-the-art NAS methods, a lower bound, and an upper bound Oracle baseline. Furthermore, an interpretability analysis of meta-features and meta-predictors is presented. As a result, using at least 172 examples representing 0.04% and 1.1% of the popular NAS-Bench-101 and NAS-Bench-201 search spaces, MbML-NAS find architectures with better or comparable performances than most baselines, including the Oracle. Moreover, the results showed the potential for using simple meta-features to generalize across NAS search spaces and datasets, encoding neural architectures so that even linear models can accurately predict their performances. Additionally, novel meta-datasets suitable for NAS are proposed to facilitate research on NAS.
CTRAN: CNN-Transformer-based network for natural language understanding

Intent-detection (ID) and slot-filling (SF) are fundamental tasks for natural language understanding. This study introduces a new encoder–decoder CNN-Transformer-based architecture (CTRAN) designed for ID and SF. The encoder integrates of BERT, followed by several convolutional layers with different kernel sizes. We propose using a kernel size of 1 to preserve the one-to-one correspondence between input tokens and output tags. Subsequently, we rearrange the output of the convolutional layer using the window feature sequence and apply stacked Transformer encoders. The ID decoder leverages self-attention and a linear layer, while the SF decoder employs an aligned Transformer decoder with a zero diagonal mask, facilitating alignment between output tags and input tokens. We evaluate our model on ATIS and SNIPS datasets, achieving 98.46% and 98.30% F1 score for the SF task, respectively. These results surpass the previous state-of-the-art by 0.64% and 0.99%. Moreover, we compare two strategies: using a language model as the encoder or as word embedding. We find out that the latter strategy yields better results.
A Digital Twin-Based Drone-Assisted Secure Data Aggregation Scheme with Federated Learning in Artificial Intelligence of Things

Artificial intelligence of things (AIoT) has brought new promises of efficiency in our daily lives by integrating AI with the IoT. However, owing to limited resources (e.g., computational power), it is difficult to implement modern technology (e.g., AI) and improve its performance (i.e., the IoT). Moreover, cyberthreats and privacy challenges can hinder the success of the IoT. This situation is aggravated by network scarcity (i.e., limited network connectivity). This article presents a digital twin-based data aggregation scheme in which data are collected using federated learning by operating a drone and stored securely in the blockchain. Before data sharing, differential privacy is realized to enhance privacy. A multirole training scheme is proposed, along with a duplex model verification architecture using a Hampel filter and performance check. To validate the specifications, an authentication scheme was implemented by combining a cuckoo filter and timeframe check. A case study to construct an experimental environment using real hardware is discussed. Different experiments were conducted in this environment and the feasibility of the proposed scheme was validated from the outcomes.
An Image Encryption Scheme Combining 2D Cascaded Logistic Map and Permutation-Substitution Operations

Confusion, diffusion, and encryption keys affect the quality of image encryption. This research proposes combining bit- and pixel-level permutation and substitution methods based on three advanced chaotic logistic map methods. The three chaotic methods are the 2D Logistic-adjusted-Sine map (2D-LASM), the 2D Logistic-sine-coupling map (2D-LSCM), and the 2D Logistic ICMIC cascade map (2D-LICM). The encryption method’s design consists of six stages of encryption, involving permutation operations based on chaotic order, substitution based on modulus and bitXOR, and hash functions. Hash functions are employed to enhance key space and key sensitivity quality. Several testing tools are utilized to assess encryption performance, including histogram and chi-square analysis, information entropy, correlation of adjacent pixels, differential analysis, key sensitivity and key space analysis, data loss and noise attacks, NIST randomness tests, and TestU01. Compared to using a single 2D logistic map, the amalgamation of bit-level and pixel-level encryption and the utilization of three 2D cascade logistic maps has improved encryption security performance. This method successfully passes the NIST, TestU01, and chi-square tests. Furthermore, it outperforms the previous method regarding correlation, information entropy, NPCR, and UACI tests.
Model-guided Reinforcement Learning Enclosing for UAVs with Collision-free and Reinforced Tracking Capability

Enclosing a maneuverable target for Unmanned Aerial Vehicles (UAVs) in a constrained environment is intractable as existing methods fail to coordinate collision safety and tracking. This paper aims to propose a model-guided reinforcement learning (RL) enclosing algorithm for UAVs to approximate a cooperative target within a fixed time, where collision-free behaviors are guaranteed and reinforced tracking capability is attained. Firstly, a polar frame-based fixed-time enclosing controller is contrived to enforce UAVs to reach the target circles within a prescribed time. Secondly, by interpreting disturbance rejection and collision avoidance constraints as two skilled reward functions, deep deterministic policy gradient (DDPG) is employed to generate the supplement component, which can enhance tracking performance and learn collision avoidance skills. Furthermore, a novel curriculum study training procedure that transfers the past learned policy to the current episode is introduced, which results in faster convergence rate and fewer numbers of sampling data collections than classical random parameter settings. The remarkable innovation lies in that an optimal intelligent enclosing policy is formulated to realize a good balance between collision avoidance and enclosing performances. Moreover, by utilizing a model-based control to generate confidential experienced data, unnecessary explorations can be reduced effectively, while a better generalization ability can be assured. Finally, abundant simulations are offered to substantiate the utility and superiorities of developed algorithm.
Summary of the Competition on Legal Information, Extraction/Entailment (COLIEE) 2023

We summarize the 10th Competition on Legal Information Extraction and Entailment. In this edition, the competition included four tasks on case law and statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and an unseen case (Task 2). The statute law component includes an information retrieval task (Task 3), and an entailment/question answering task based on retrieved civil code statutes (Task 4). Participation was open to any group based on any approach. Ten different teams participated in the case law competition tasks, most of them in more than one task. We received results from 8 teams for Task 1 (22 runs) and seven teams for Task 2 (18 runs). On the statute law task, there were 9 different teams participating, most in more than one task. 6 teams submitted a total of 16 runs for Task 3, and 9 teams submitted a total of 26 runs for Task 4. We describe the variety of approaches, our official evaluation, and analysis of our data and submission results.
MCA: Multidimensional collaborative attention in deep convolutional neural networks for image recognition

A broad range of prior research has demonstrated that attention mechanisms offer great potential in advancing the performance of deep convolutional neural networks (CNNs). However, most existing approaches either ignore modeling attention in both channel and spatial dimensions or introduce higher model complexity and heavier computational burden. To alleviate this dilemma, in this paper, we propose a lightweight and efficient multidimensional collaborative attention, MCA, a novel method for simultaneously inferring attention in channel, height, and width dimensions with almost free additional overhead by using a three-branch architecture. For the essential components of MCA, we not only develop an adaptive combination mechanism for merging dual cross-dimension feature responses in squeeze transformation, enhancing the informativeness and discriminability of feature descriptors but also design a gating mechanism in excitation transformation that adaptively determines the coverage of interaction to capture local feature interactions, overcoming the paradox of performance and computational overhead trade-off. Our MCA is simple yet general and can be easily plugged into various classic CNNs as a plug-and-play module and trained along with the vanilla networks in an end-to-end manner. Extensive experimental results for image recognition on CIFAR and ImageNet-1K datasets demonstrate the superiority of our method over other state-of-the-art (SOTA) counterparts. In addition, we also provide insight into the practical benefits of MCA by visually inspecting the GradCAM++ visualization results. The code is available at https://github.com/ndsclark/MCANet.
Modeling Population Growth in <i>R</i> with the <b>biogrowth</b> Package

The growth of populations is of interest in a broad variety of fields, such as epidemiology, economics or biology. Although a large variety of growth models are available in the scientific literature, their application usually requires advanced knowledge of mathematical programming and statistical inference, especially when modelling growth under dynamic environmental conditions. This article presents the biogrowth package for R, which implements functions for modelling the growth of populations. It can predict growth under static or dynamic environments, considering the effect of an arbitrary number of environmental factors. Moreover, it can be used to fit growth models to data gathered under static or dynamic environmental conditions. The package allows the user to fix any model parameter prior to the fit, an approach that can mitigate identifiability issues associated to growth models. The package includes common S3 methods for visualization and statistical analysis (summary of the fit, predictions, . . . ), easing result interpretation. It also includes functions for model comparison/selection. We illustrate the functions in biogrowth using examples from food science and economy.
Improving CNN-based Person Re-identification using score Normalization

Person re-identification (PRe-ID) is a crucial task in security, surveillance, and retail analysis, which involves identifying an individual across multiple cameras and views. However, it is a challenging task due to changes in illumination, background, and viewpoint. Efficient feature extraction and metric learning algorithms are essential for a successful PRe-ID system. This paper proposes a novel approach for PRe-ID, which combines a Convolutional Neural Network (CNN) based feature extraction method with Cross-view Quadratic Discriminant Analysis (XQDA) for metric learning. Additionally, a matching algorithm that employs Mahalanobis distance and a score normalization process to address inconsistencies between camera scores is implemented. The proposed approach is tested on four challenging datasets, including VIPeR, GRID, CUHK01, and PRID450S. The proposed approach has demonstrated its effectiveness through promising results obtained from the four challenging datasets.
Deep-Learning-Based Energy Aware Images

In this paper, we present a method to compute energy-aware images, that aims to reduce the energy consumption of displays. This method relies on a lightweight unsupervised deep model which finds out the best trade-off between visual quality and energy reduction. From an input image and an energy reduction rate, a dimming map is inferred. We show that the proposed model performs as good as state-of-the-art methods, while being much more simple. In addition, the dimming map computation is constrained in order to ease its distribution throughout the video chain.
A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification

Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads to high memory usage and energy consumption. As a result, deploying DNNs on devices with constrained hardware resources poses significant challenges. To overcome this, various compression techniques have been widely employed to optimize DNN accelerators. A promising approach is quantization, in which the full-precision values are stored in low bit-width precision. Quantization not only reduces memory requirements but also replaces high-cost operations with low-cost ones. DNN quantization offers flexibility and efficiency in hardware design, making it a widely adopted technique in various methods. Since quantization has been extensively utilized in previous works, there is a need for an integrated report that provides an understanding, analysis, and comparison of different quantization approaches. Consequently, we present a comprehensive survey of quantization concepts and methods, with a focus on image classification. We describe clustering-based quantization methods and explore the use of a scale factor parameter for approximating full-precision values. Moreover, we thoroughly review the training of a quantized DNN, including the use of a straight-through estimator and quantization regularization. We explain the replacement of floating-point operations with low-cost bitwise operations in a quantized DNN and the sensitivity of different layers in quantization. Furthermore, we highlight the evaluation metrics for quantization methods and important benchmarks in the image classification task. We also present the accuracy of the state-of-the-art methods on CIFAR-10 and ImageNet. This article attempts to make the readers familiar with the basic and advanced concepts of quantization, introduce important works in DNN quantization, and highlight challenges for future research in this field.
Benchmarking Quantum Simulators Using Ergodic Quantum Dynamics

We propose and analyze a sample-efficient protocol to estimate the fidelity between an experimentally prepared state and an ideal target state, applicable to a wide class of analog quantum simulators without advanced spatiotemporal control. Our protocol relies on universal fluctuations emerging from generic Hamiltonian dynamics, which we discover in the present work. It does not require fine-tuned control over state preparation, quantum evolution, or readout capability, while achieving near optimal sample complexity: a percent-level precision is obtained with $\ensuremath{\sim}{10}^{3}$ measurements, independent of system size. Furthermore, the accuracy of our fidelity estimation improves exponentially with increasing system size. We numerically demonstrate our protocol in a variety of quantum simulator platforms, including quantum gas microscopes, trapped ions, and Rydberg atom arrays. We discuss applications of our method for tasks such as multiparameter estimation of quantum states and processes.
Mapping of decentralised photovoltaic and solar thermal systems by remote sensing aerial imagery and deep machine learning for statistic generation

As a mean to monitor the rapid expansion of the highly decentralized PV market, identifying solar energy systems in aerial imagery by deep machine learning, is a research field that is getting increasing interest. One general challenge in the field is to create testing data of high quality that are representative of the end-use application. In this study we use the open source convolutional neural network developed within the DeepSolar project and apply it in the country of Sweden, for the purpose of generating market statistics, by scanning three complete municipalities for small decentralized photovoltaic and solar thermal systems. The evaluation of the performance is done against a highly accurate ground truth, which was created by cross-checking the classification results with the inventory of the local distribution system operators and the database of photovoltaic systems that have received a capital subsidy in Sweden, and combining that with physical onsite inspections. A process of generate additional training data and re-training the algorithm after each municipality scan was developed, which successively improved the accuracy, resulting in that 95% of all detectable photovoltaic, excluding building integrated and vertical systems, and 80% of all detectable solar thermal systems were correctly identified in the last municipality scan. The accurate ground truth allowed a quantification of why some systems are not detected. The generated dataset of solar energy systems could be connected to existing building and property inventories, which allowed creation of market segment statistics with remarkably high detail information.
Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
Causal-ViT: Robust Vision Transformer by causal intervention

Artificial intelligence based on deep learning is better at improving the representation ability of models from data. However, due to the limitation of fixed receptive field, these agents are not able to provide a correct response outside the fixed receptive field. To address this problem, this paper provides a new perspective with improving the Image Recognition tasks. This study firstly constructs two extended receptive fields using structural causal model. Then, an approximate intervention method that changes the traditional likelihood prediction to predict the result of causal intervention is proposed. Finally, this study formulates the objective function to adapt the proxy training, which makes the whole model work well. Above all of these, a new Vision Transformer variant named Causal-ViT is proposed. Furthermore, rich experimental results of different tasks are reported. These results show that the proposed perspective makes a significant improvement in Image Recognition tasks. By simply plugging Causal-ViT to different sub-tasks, all of them bring the new benchmarks of themselves field, which proves our method is flexible.