Reviewerly/exHarmony huggingface dataset:
Dynamic Circular Convolution for Image Classification

In recent years, Vision Transformer (ViT) has achieved an outstanding landmark in disentangling diverse information of visual inputs, superseding traditional Convolutional Neural Networks (CNNs). Although CNNs have strong inductive biases such as translation equivariance and relative positions, they require deep layers to model long-range dependencies in input data. This strategy results in high model complexity. Compared to CNNs, ViT can extract global features even in earlier layers through token-to-token interactions without considering geometric location of pixels. Therefore, ViT models are data-efficient and data-hungry, in another work, learning data-dependent and producing high performances on large-scale datasets. Nonetheless, ViT has quadratic complexity with the length of the input token because of the natural dot product between query and key matrices. Different from ViTs-and-CNNs-based models, this paper proposes a Dynamic Circular Convolution Network (DCCNet) that learns token-to-token interactions in Fourier domain, relaxing model complexity to O(Nlog(N) instead of $$O(N^2)$$ in ViTs, and global Fourier filters are treated dependently and dynamically rather than independent and static weights in conventional operators. The token features, dynamic filters in spatial domain are transformed to frequency domain via Fast Fourier Transform (FFT). Dynamic circular convolution, in lieu of matrix multiplication in Fourier domain, between Fourier features and transformed filters are performed in a separable way along channel dimension. The output of circular convolution is revered back to spatial domain by Inverse Fast Fourier Transform (IFFT). Extensive experiments are conducted and evalued on large-scaled dataset ImageNet1k and small dataset CIFAR100. On ImageNet1k, the proposed model achieves 75.4% top-1 accuracy and 92.6% top-5 accuracy with the budget 7.5M paramaters under similar setting with ViT-based models, surpassing ViT and its variants. When fine-tuning the model on smaller dataset, DCCNet still works well and gets the state-of-the-art performances. Both evaluating the model on large and small datasets verifies the effectiveness and generalization capabilities of the proposed method.
Text-to-Image Synthesis using BERT Embeddings and Multi-Stage GAN

A technique called text-to-image creation is used to create visuals that correspond to provided written descriptions. In this study, we have proposed a Multi-Stage GAN model using BERT Embeddings which consists of three layers of generators and discriminators. Based on a provided text description, the Stage-I GAN creates low-resolution drawings (64 × 64) of a scene’s basic structure and colors. High-resolution pictures (128 × 128) with photorealistic details are produced by the Stage-II GAN using the text description and Stage-I findings as inputs. Further the Stage-III GAN takes Stage-II images as input and adds enticing details and corrects flaws in Stage-II outcomes through refining and also increases the resolution to 256 × 256. For the purpose of creating photorealistic pictures based on text descriptions, the model is evaluated using the Oxford-102 dataset and the CUB dataset and Inception Score as the metric. The IS of Multi-Stage GAN is 3.27 on Oxford-102 dataset and 4.05 on CUB dataset.
Amplify Supplements

Amplify is not just limited to these core features which we have discussed in previous chapters – there are also a number of supplementary services and capabilities that can be used to enhance and extend its functionality. From creating chatbots with AWS Lex service to adding AI/ML capabilities to applications, Amplify provides a wide range of capabilities to enhance the development process. In this chapter, we will explore some of the key features of AWS Amplify, including its support for multiple frameworks, its integration with AI/ML services, and its capabilities for chatbot development. We will also introduce AWS Amplify Studio, a powerful tool for UI development. By the end of this chapter, you will have a solid understanding of the many ways in which AWS Amplify can help you streamline your development process and build powerful, responsive, and scalable applications.
Devanagari Handwritten Character Recognition Using Dynamic Routing Algorithm

Devanagari is the most commonly used script in India. It is the official writing system in many institutes or organizations for documentation purposes. Due to digitization, these documents need to be stored in digital format. However, automatic transcription of images of the handwritten text is a tedious task. This paper addresses the problem of handwritten Devanagari character recognition. CNN (convolutional neural networks) was found to be able to classify handwritten text. However, CNN fails to recognize the image correctly for rotated samples. This is due to the changed spatiality of the rotated image. This chapter discusses how the pooling layer of CNN failed in cases of disfiguration and/or proportional transformation of any image. This chapter introduces the use of Capsule Networks for Devanagari text classification. A Capsule network uses a dynamic routing algorithm that explores the spatial relationship among the features such as size, feature orientation, perspective, etc. The experiment results show that the CapsNet (Capsule Network) Framework improves results over CNNs for handwritten Devanagari character recognition.
Teaching Research of Secondary School Mathematics

Secondary school mathematics teaching research is a purposeful and planned creative activity to study the laws of mathematics education in the field of secondary school mathematics education. For a long time, countries all over the world have attached great importance to the research and reform of secondary school mathematics teaching. For current and future secondary school mathematics teachers, it is necessary to master the methods of secondary school mathematics teaching research systematically, understand some of the past and current reforms and the development trend of future reforms, so as to throw themselves into teaching research and reforms actively. In this regard, we will make some brief introduction.
Privacy-Enhancing Techniques forGradients in Federated Machine Learning

A Federated Learning (FL) technique is widely applied in many fields because models are trained locally with participant data instead of the training data being gathered centrally. Applications such as medical imaging, next-word prediction, and speech prediction widely use FL because these use cases involve sensitive and private data. Although FL avoids sharing actual data, it still faces various privacy and security concerns. Adversaries can actively or passively attack the participant’s privacy from the shared model. There are mainly two issues to handle in FL: (1) protecting user’s privacy and (2) integrity of the averaged model (gradients). Currently, no single solution for privacy preservation applies to all situations. But applications may adopt different Privacy-Enhancing Technologies (PETs) to fulfill the additional privacy and security priorities. Many researchers have proposed a solution to tackle FL’s security and privacy challenges. Ongoing research addresses the possibility of improving the privacy and security of participants’ private data using various techniques such as Homomorphic Encryption (HE), Trusted Execution Environments (TEE), Differential Privacy Preservation (DPP), and Secure Multiparty Computation (SMC). This chapter demonstrates how the gradients can be inverted to get the original image, which reveals limitations in these methodologies. In light of such limitations, the chapter presents ways to protect the privacy and security of participants’ data in FL.
Educational Measurement of Secondary School Mathematics

Educational measurement of secondary school mathematics is a science taking the examination and evaluation of secondary school mathematics learning as the main research object, also known as the secondary school mathematics examination science.
Rough Target Region Extraction with Background Learning

Object localization is a fundamental and important task in computer vision, that is used as a pre-processing step for object detection and semantic segmentation. However, fully supervised object localization requires bounding boxes and pixel-level labels, and these annotations are expensive. For this reason, Weakly Supervised Object Localization (WSOL) with image-level (weak) supervision has been the focus of much research in recent years. However, WSOL requires a large dataset to detect the region of an object in images with high performance. When the large dataset is unavailable, it is difficult to localize the image with high performance. This paper proposes a method for extracting target regions using small amounts of target and background images with image-level labels. The proposed method enables the detection of object locations with high performance using relatively less training images by classifying multiple patches cut from the image. This object localization method differs from the typical WSOL method that takes a single image as input and detects the location of an object because it assumes a small patch of area as input. The label of the patch cropped from the image must be labeled with the ground truth. However, the proposed method uses labels attached to images because ground truth labeling is costly. Instead, in the proposed method, the network learns by learning many “background” labeled background patches, and learns to induce the network to classify the mislabeled background patches that resemble ground truth as background. We call this key idea Decision-Boundary Induction(DBI). Moreover, learning many background patches for such a DBI is what we call background learning. In our experiments, we verified that decision boundaries are induced, and accordingly, we could roughly extract the target region. Also, we showed that the Loc. Acc. is higher than that of WSOL.
Continuous-Time Fitted Value Iteration for Robust Policies

One approach to obtain the optimal control inputs that maximize the reward, is to solve the equation, as this differential equation expresses a sufficient and necessary condition for optimality [45]. Solving the yields the optimal value function, which can be used to retrieve the optimal action at each state. Therefore, this ansatz has been used by various research communities, including economics [13, 14] and robotics [4, 17, 25, 44], to compute the optimal plan for a given reward function.
Calibrate to Operate: Aligning Patient-Level Predictions with System-Wide Forecasts

We explore the challenges of re-using machine learning models originally developed for patient-level predictions to forecast system-wide quantities in hospitals. As a running example, we examine the use of patient-level predictions of length of stay to predict short-term occupancy in an emergency department. We first provide analytical evidence for the potential consequences of the misalignment between the patient-level and system-level prediction tasks: the emergence of bias and the failure to detect non-stationarity. Using patient-level data from an emergency department, we demonstrate the existence of such challenges in our data. We show that the simple calibration procedures we propose produce unbiased and more accurate system-wide predictions. We also show that dynamic re-training of the calibration over time further improves the accuracy in our non-stationary setting. Overall, we provide a practical roadmap for hospitals considering making sophisticated patient-level predictions to improve system operations.
Risk Analysis Using Multi-Source Data for Distribution Networks Facing Extreme Natural Disasters

Distribution networks denote important public infrastructure necessary for people’s livelihoods. However, extreme natural disasters, such as earthquakes, typhoons, and mudslides, severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life. Therefore, considering the requirements for distribution network disaster prevention and mitigation, there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions. This paper accesses multi-source data, presents the data quality improvement methods of distribution networks, and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory. Furthermore, the paper realizes real-time, accurate access to distribution network disaster information. The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study. The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study. The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.
Attribute Auxiliary Clustering for Person Re-Identification

The main objective of the person re-identification task is to retrieve the specific identity under multiple non-overlapping camera scenarios. Though unsupervised person re-ID has already achieved great performance and even surpasses some classic supervised re-ID methods, the existing methods pay much attention to training the neural networks with the memory-based idea which ignore the quality of the generated pseudo label. The quality of the clustering process does not only depend on the intra-cluster similarity but also on the number of clusters. In this paper, our approach employs an attribute auxiliary clustering method for person re-ID task. The proposed method could divide the generated cluster by the leveraged attribute label. Employed the attribute auxiliary clustering, the task changed from unsupervised case to weakly supervised case. The method is compared with state-of-the-art and analyzes the effectiveness caused by the variation of the cluster number. The proposed approach achieves great performance on the public Market-1501 datasets.
Evolutionary extreme learning machine based collaborative filtering

Since their emergence in the middle of the 1990s, recommender systems (RSs) have drawn more interest from academics and businesses [1,2].These systems investigate and assess user data from social media platforms, including followers, followed, comments, likes, dislikes, tweets, posts, and more [3,4].Additionally, the Internet of Things (IoT) has developed into a new source of information that includes global positioning system (GPS) coordinates, radio frequency identification (RFID) data, health indicators [5] surveillance data, etc.To provide consumers, with the suggestions or recommendations they want, RS uses a variety of information sources, opening a popular field for additional study and improvement [3,4].
Development of a Causality Least Association Rules Algorithm Tool Using Rational Unified Process Methodology

Among the most crucial research areas in data mining is association rule mining (ARM). Rules are classified into two types: frequent rules and least frequent rules. Extracting the least association rules is more difficult and always leads to the “rare item problem” quandary. The rules with the fewest items are known as the “least association rules.” However, most data mining tools favour frequent association rules over the least frequent association rules. Furthermore, the process of extracting the least association rules is more difficult. Therefore, this paper proposes and develops Causality Least Association Rules Algorithm Tool (CLART) using the Rational Unified Process (RUP) methodology and the C# programming language. The results showed that CLART is workable, and the proposed algorithm also outperformed the existing benchmark algorithm. In addition, CLART is a dedicated tool that is freely available and can be used to extract the causality least association rules from the benchmarked datasets.
Classification of Texts with Emojis to Classify Sentiments, Moods, and Emotions

Here we propose a method to classify emoji-based content using both text and emoji images. In order to do this, we have investigated a number of machine learning and deep learning techniques for text and image classification. Two machine learning approaches- Multinomial Naive Bayes and Bernouli Naive Bayes, and one BiLSTM-based deep learning approach used for text classification in various sentiments, moods, and emotions labels, and Zero-Shot image classification approach for classifying emoji images for the same purpose have been proposed. In terms of accuracy, the emoji classification method performs with an accuracy of 50%. Both machine learning approaches produce an accuracy of 27%. The BiLSTM model performs comparatively better with an accuracy of 30%.
A Relation Between Properties of S-box and Linear Inequalities of DDT

Mixed integer linear programming (MILP)-based tools are widely used to analyze the security of block ciphers against differential attacks. The differential properties of an S-box are represented by the difference distribution table (DDT). The methods based on convex hull and logic minimization are used to represent the DDT through linear inequalities. The impossible transitions in DDT are utilized for the minimization of the number of linear inequalities generated using the convex hull approach. The Boolean logic minimization tools Logic Friday and MILES minimize the truth table of DDT to construct and reduce the set of linear inequalities. In this paper, we construct and compare the number of linear inequalities for S-boxes of size 4 bits used in 42 lightweight block ciphers. We analyze the cryptographic properties of S-boxes and observe an inverse relationship between the boomerang uniformity of S-boxes and the number of linear inequalities constructed. We establish that the number of linear inequalities for an S-box decreases as the value of boomerang uniformity increases.
NISQ-Ready Community Detection Based on Separation-Node Identification

The analysis of network structure is essential to many scientific areas ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO-based approach that only needs number-of-nodes qubits and is represented by a QUBO matrix as sparse as the input graph’s adjacency matrix. The substantial improvement in the sparsity of the QUBO matrix, which is typically very dense in related work, is achieved through the novel concept of separation nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which, upon its removal from the graph, yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept by achieving an up to 95% optimal solution quality on three established real-world benchmark datasets. This work hence displays a promising approach to NISQ-ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large-scale, real-world problem instances.
CenterNet Based on Diagonal Half-length and Center Angle Regression for Object Detection

CenterNet, a novel object detection algorithm without anchor based on key points, regards the object as a single center point for prediction and directly regresses the object's height and width.However, because the objects have different sizes, directly regressing their height and width will make the model difficult to converge and lose the intrinsic relationship between object's width and height, thereby reducing the stability of the model and the consistency of prediction accuracy.For this problem, we proposed an algorithm based on the regression of the diagonal half-length and the center angle, which significantly compresses the solution space of the regression components and enhances the intrinsic relationship between the decoded components.First, encode the object's width and height into the diagonal half-length and the center angle, where the center angle is the angle between the diagonal and the vertical centreline.Secondly, the predicted diagonal half-length and center angle are decoded into two length components.Finally, the position of the object bounding box can be accurately obtained by combining the corresponding center point coordinates.Experiments show that, when using CenterNet as the improved baseline and resnet50 as the Backbone, the improved model achieved 81.6% and 79.7% mAP on the VOC 2007 and 2012 test sets, respectively.When using Hourglass-104 as the Backbone, the improved model achieved 43.3% mAP on the COCO 2017 test sets.Compared with CenterNet, the improved model has a faster convergence rate and significantly improved the stability and prediction accuracy.
Data Access Control Scheme Based on Blockchain and Outsourced Verifiable Attribute-Based Encryption in Edge Computing

The arrival of the Internet of Things and 5G technology enables users to rely on edge computing platforms to process massive data.Data sharing based on edge computing refines the efficiency of data collection and analysis, saves the communication cost of data transmission back and forth, but also causes the privacy leakage of a lot of user data.Based on attribute-based encryption and blockchain technology, we design a fine-grained access control scheme for data in edge computing, which has the characteristics of verifiability, support for outsourcing decryption and user attribute revocation.User attributes are authorized by multi-attribute authorization, and the calculation of outsourcing decryption in attribute encryption is completed by edge server, which reduces the computing cost of end users.Meanwhile, We implemented the user's attribute revocation process through the dual encryption process of attribute authority and blockchain.Compared with other schemes, our scheme can manage users' attributes more flexibly.Blockchain technology also ensures the verifiability in the process of outsourcing decryption, which reduces the space occupied by ciphertext compared with other schemes.Meanwhile, the user attribute revocation scheme realizes the dynamic management of user attribute and protects the privacy of user attribute.
Review of Object Detection Challenges in Autonomous Driving

This paper presents a comprehensive review of object detection in autonomous driving applications. The classical object detection network is presented, along with several well-known online resources and benchmark methods. A thorough review of the challenges in object detection for autonomous driving is provided, along with potential solutions to these challenges. By exploring the current state of object detection in autonomous vehicles, this paper aims to contribute to the ongoing efforts to improve the safety and efficiency of autonomous driving technology.