Reviewerly/exHarmony huggingface dataset:
Homomorphic Encryption-Based System Design for Secure Data Processing

Homomorphic Encryption (HE) is a useful data encryption technique that can support computations on encrypted data. For example, it allows users to add and multiply numbers within encrypted database, using custom-designed arithmetic circuits. Its main applications include cloud computing, data aggregation, machine learning, and multi-party communications. This research project implements a secure Encrypted Query System, supporting multiple clients using Homomorphic Encryption. In order to improve computational speed, we use High-Level Synthesis (HLS) to develop a hardware accelerated Number Theoretic Transform (NTT) based polynomial multiplier. Comparing with some existing methods in the literature, our proposed design can achieve markedly lower latency.
KTPose: Keypoint-Based Tokens in Vision Transformer for Human Pose Estimation

Transformers have made remarkable progress on human pose estimation in recent years, however, vision tokens are all in a fixed position, a property unsuitable for unknown human deformation. In this paper, we propose KTPose, a novel keypoint-based tokens in Vision Transformer for human pose estimation, which includes an instance-aware keypoint head and a keypoint refinement with transformer. To address the limb deformation issue, the instance-aware keypoint head is devised to capture the discriminative features dynamically based on the coarse localized keypoints. Further, we propose the multi-granularity vision tokens, in which each keypoint is explicitly embedded as a token to simultaneously learn spatial dependencies and constraint relationships from vision transformer for human pose estimation. Extensive experiments are carried out on two benchmark datasets, which demonstrate that KTPose outperforms state-of-the-art methods and achieves 76.6AP (?1.06%) and 75.7AP (?0.93%) on COCO validation and test-dev sets, respectively. This is accomplished with a smaller computational footprint when compared to the current mainstream transformer-based methods. Code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/WINGS-999/KTPose.
Dynamic Equilibrium-Based Continual Learning Model with Disentangled Meta-features

The field of artificial intelligence research has witnessed remarkable advancements in recent decades. How-ever, conventional approaches in AI research primarily depend on fixed datasets and stationary settings, which have limited applicability to real-world scenarios. In order to address this limitation, there is an increasing need to develop and study algorithms and methods for continual learning, which enables artificial systems to learn from a continuous stream of data. One of the key challenges in continual learning is to strike a balance between transfer and interference, and to identify an equilibrium solution that can effectively learn from non-stationary data. This paper presents a canonical model that is specifically designed for continual learning, utilizing the derivative of the loss function to evaluate parameter changes between tasks and achieve dynamic equilibrium. Additionally, to improve the efficiency of limited training samples in continual tasks, a feature learning method based on meta-feature disentangling is proposed. By leveraging the second derivative term of the canonical model, the parameter vector can be decoupled and meta-features can be discovered. Experimental results demon-strate the superiority of the proposed method over state-of-the-art methods in continual lifelong supervised learning benchmarks. The validity of the proposed canonical model is further supported by these experimental results. As the demands of available settings become increasingly stringent, the advantages of disentangling meta-features become more prominent, resulting in a significant performance gap with other continual learning methods.
Fine-Tuning Vision Transformer for Arabic Sign Language Video Recognition on Augmented Small-Scale Dataset

With the rise of AI, the recognition of Sign Language (SL) through sign-to-text has gained significance in the field of computer vision and deep machine learning. However, there are only a few medium to large open datasets available for this task, as it requires a vast dataset of thousands of signs for words/phrases in different environments, which is a time-consuming and tedious process. Furthermore, there has been very little effort towards Arabic Sign Language Recognition (ArSLR). This research paper presents the results of fine-tuning the Vision Transformer (ViT) model on a small-scale in-house dataset of ArSL. The main goal is to attain satisfactory results by utilizing minimal computing power and a small dataset involving less than 10 individuals, with only one recording made for each sign in every environment. The dataset comprises 49 classes/signs, all of which were made with two hands and belong to the Level I category in terms of popularity. To enhance the dataset, three types of augmentations - translation, shear, and rotation were employed. The ViT model, pre-trained on the Kinetics dataset, was trained on the variation of augmented datasets with 2 to 40 times samples for each original video, where the training set includes original and augmented videos of 8 volunteers and the test set includes only original videos of one particular volunteer. Experimental results reveal that the combination of rotation and shear outperformed the others, achieving an accuracy of 93% on the 20 times augmented samples per class per signer dataset. We believe this study sheds light on small-scale dataset-based SLR tasks and video/action recognition in general.
Distributed Lattice Smooth Variable Structure Filter for Target Tracking Under Model Uncertainty

The conventional filters algorithm cannot provide a stable and precise state estimation for target tracking in Air Traffic Control (ATC) with motion model uncertainty. In this paper, the new filter and distributed fusion strategies are proposed. First, a Lattice Smooth Variable Structure Filter (LSVSF) is proposed for nonlinear system with model uncertainty, which avoids the calculation of Jacobi matrices. Then, the distributed lattice smooth variable structure filter Distributed Lattice Smooth Variable Structure Filter (MW-LSVSF) is proposed, and the cross-covariance of local estimators is calculated by lattice sampling. Finally, the state estimation accuracy and robustness are verified by ATC scenarios.
Properly Scoring Users' Mainstreamness to Evaluate Recommendation Bias

In recommender systems, a mainstream bias (MS bias) exists, which degrades the fairness of recommender systems and may decrease user satisfaction. In other words, the recommendation quality for users who prefer mainstream items is higher than that for users who prefer niche items. The MS bias degrades fairness in the recommender system and might decrease user satisfaction. To address the MS bias, we first need to quantify the degree of bias. However, the mainstreamness of users (the MS score) is difficult to measure properly, and we reveal that previous MS scores are greatly confounded by the number of user interactions that are irrelevant to mainstreamness. To address this issue, we propose a novel MS score that is unaffected by the number of interactions. Specifically, we introduce a new similarity metric that differs from the conventional Jaccard similarity measure to eliminate the effect of interaction numbers. We confirm the validity of our proposed MS score through a simulation study that discriminates mainstream users from niche users under varying numbers of user interactions. The proposed unconfounded MS score enables the proper evaluation of MS biases and the selection of a recommendation model with less MS bias. We evaluate the MS biases of two popular recommendation models using seven real-world datasets. We demonstrate that MS biases exist in all datasets and that a better model differs depending on the datasets. Furthermore, we confirm that the previous MS scores fail to select recommendation models with small biases. The results show the effectiveness of our new MS score in improving recommendations for fairness among users.
Mutual Learning for Pattern Recognition

Mutual learning algorithm can be an efficient mechanism for improving the machine learning and neural network efficiency in a multi-agent system. Specifically, in many cases, where the system cannot be trained using a big training dataset, the data exchange in teacher-student network system can lead to efficient learning. Usually, in mutual learning algorithms, a big network plays the role of a static teacher and passes the data to smaller networks, known as student networks, to improve the efficiency of the latter. In this paper, we will show that two small networks can dynamically play the changing roles of teacher and student to share their knowledge and hence, the efficiency of both the networks improve simultaneously. We demonstrate the concept and the proposed mutual learning algorithm using convolutional neural networks (CNNs) to recognize the benchmark Modified National Institute of Standards and Technology (MNIST) hand-writing dataset.
Identification of Online Social Media Bots Based on Classification Models

Text messages from customers, which are commonly exchanged through Internet services (in particular, transport companies, taxi services, energy generating companies etc.) such as messengers and chats, serve as conventional communication tools between customers and service providers. They also serve as a valuable source of operational data, containing crucial information about customer preferences, as well as opinions regardig specific aspects of the service and its quality.At the same time, among a number of problems that arise in this case, there is the influence of bots. Identification of bots and the content generated by them is an urgent task.The authors build and investigate a number of classification models on the example of Twitter content. It is shown that the model based on random forests provides the best results for bot identification.
System Electromechanics Adaptive Modular Platform Concept for Research on Electric Machines and Electromechanical Devices

The reasons for the non-compliance of the organization and bench support of physical experiments with modern requirements related to the educational field of knowledge production are analyzed. It is shown that the creation of bench equipment based on multi-purpose technological platforms with wide adaptive capabilities is a promising way to solve the mentioned problems. The concept of a System electromechanics (SEM) modular platform is proposed, the scientific-methodological and informational basis of which is determined by the system-genetic paradigm of the organization of the educational environment. The genetic nature of modularity is revealed, based on the results of its analysis, the choice of the dominant generic geometry is justified, which provides a wide range of structure formation of the research object from a limited number of functional modules. The construction of a Smart-stand with an adaptive space of modular structuring of the active zone of the research object is proposed, which allows the direct participation of the researcher to form the structure and carry out experimental studies of various types of electromechanical objects. Attention is focused on the possibilities of organizing an intellectual educational environment, which allows for the formation of individual scientific and educational trajectories through the convergence of the prognostic potential of genetic programs of structure formation, the cognitive mechanisms of the researcher's thinking and the adaptive space of the Smart-stand. The SEM modular platform represents the concept of a modular smart laboratory of the new generation, scientific and methodological support, the structure and functions of which are endowed with wide adaptive possibilities for the setting and execution of highly intellectual tasks of the corresponding educational or research program.
Automatic Deep Active Contour Model Based on Local Image Characteristics

In recent years, convolutional neural networks have made remarkable achievements in medical image segmentation. Unclear margins and fuzzy boundary regions are formed surrounding the thyroid gland area under the influence of artifacts and speckles, which increases the difficulty of thyroid ultrasound image segmentation. However, building an effective CNN model usually requires massive computing resources, long training time, and large amounts of carefully labeled data. In this work, we develop a fast, compact, accurate and robust image segmentation model for automatic thyroid segmentation in medical ultrasound images. We propose an automatic deep active contour (DAC) model based on local image characteristics, which utilizes the deep neural network model to obtain the initial contour and constructs external energy items based on the local image characteristics of contour points. Compared with the deep neural network model, the DAC model requires less training data, and the segmentation performance of the DAC model on a small dataset can reach that of the deep neural network model on a larger dataset. The proposed DAC model can be used for automatic thyroid segmentation in medical ultrasound images, which does not require massive computing resources, long training time, and large amounts of carefully labeled data.
Cap-CasMVSNet:Capsul-based Cascade Cost Volume for Multi-View Stereo Network

Currently deep learning has been widely used in multi-view stereo, but there is still room for optimization. In this paper, we propose a capsule network-based multi-view stereo network, namely Cap-CasMVSNet. We first introduce a transformer-based filter to highlight the foreground part of the feature. Then aiming at the shortcomings of the fixed receptive field of the traditional convolution kernel, we added a deformable convolution module to the network to enable the convolution to adapt to geometric deformation. We use a capsule neuron to handle global semantic connections between high-level features. Finally, we achieve competitive results on the DTU dataset, showing strong robustness.
ECT: Enhancement Cross Training for Weakly Supervised Semantic Segmentation

In recent years, Weakly Supervised Semantic Segmentation (WSSS) has garnered significant attention, as it enables pixel-level segmentation using only image-level labels. However, current WSSS methods typically rely on extracting Class Activation Map (CAM) from a classification network as the initial localization cues, which are often narrow and fragmented. In this paper, we demonstrate that more regions can be activated by roughly applying enhancement functions on CAM. Specifically, we propose an Enhancement Cross Training (ECT) approach for WSSS, which involves non-learning enhancement functions and a Cross Training process for integrating enhancement functions into the learnable CAM generation network. By cross-training two identical CAM generation models, ECT allows CAM to expand with its own localization information. Experiment on PASCAL VOC 2012 shows that our method is competitive with existing state-of-the-art methods.
Collaborative Estimating Multiple Gaussian Graphical Models on Resource Constrained Devices in IoT Networks

In recent years, the rapid development of the Internet of Things (IoT) has attracted significant interest in smart healthcare. However, such collaborative IoT applications still face three major challenges: multi-task data heterogeneity, high communication cost, decentralized computation and privacy preservation. As a result, efficiently integrating and processing such complex data has become a crucial problem to solve. In this paper, we propose COFFE, a collaborative federated learning framework that allows edge devices to participate in the model training. Our framework leverages the local computing power of edge devices and utilizes the communication resources of the IoT network to enable distributed learning. We apply a compression algorithm which optimizes the communication cost by minimizing the amount of exchanged data between the edge devices and the cloud server. We demonstrate the effectiveness of our approach through a set of experiments on a simulated IoT network. Our results show that our proposed COFFE approach achieves significant performance gains compared to traditional centralized learning approaches. Overall, our work provides a promising direction in smart healthcare for applying federated learning to IoT setting and enabling edge device collaboration.
Convolutional Neural Network Compression Based on Improved Fractal Decomposition Algorithm for Large Scale Optimization

Deep learning methods have shown state-of-the-art results in various application areas such as computer vision, NLP, etc. However, their practical use presents many challenges, including those caused by the large size of the models, especially in the context of model weight storage and transmission. One of the possible solutions to this problem is Neural Network (NN) compression, which is a process of obtaining a derived model serving the same task with a smaller number of parameters or with parameters of lower precision. The most common NN compression techniques include pruning, sparse representation, quantization, and knowledge transfer. In this article, the compression of Convolutional Neural Networks (CNNs) using fractional differentiation is investigated. A for-mulation of this task as a large-scale continuous optimization problem is then proposed, and its resolution is performed through a new optimization algorithm, called the Improved Fractal Decomposition Algorithm (IFDA), based on space geometric fractal decomposition. The results obtained show that MobileNetV3, for instance, is compressed by 18.5% with only a 2.5% decrease in accuracy. Additionally, the proposed IFDA algorithm outperforms all other competing metaheuristics in solving this problem.
Fusion of UNet and Criminisi algorithms for endoscopic specular highlight removal

To address the problems of incomplete highlight segmentation and unreasonable repair texture in the current endoscope specular highlight removal algorithm, a fusion of UNet and Criminisi is proposed for the endoscope specular highlight removal algorithm. Highlight removal is mainly divided into two processes: highlight segmentation and inpainting. In the highlight segmentation module, the highlights are segmented based on the improved UNet model in combination with the transfer learning strategy. Firstly, VGG16 is used as the backbone feature extraction network; secondly, in the enhanced feature extraction module, a simple pyramid pooling module is used to aggregate global contextual information to improve the network's ability to obtain global information; finally, we trained the model using a loss function that mixes dice loss and focus loss. In the highlight inpainting module, the Criminisi algorithm with a better reconstruction effect is chosen and the original priority function is optimized to improve the algorithm. The experimental results show that in the highlight segmentation module, the Dice coefficient and IoU of the improved algorithm are improved by 0.97% and 1.37%, respectively; in the highlight inpainting module, the PSRN and SSIM values of the improved algorithm are improved by 0.78 dB and 0.005, respectively, which verifies the effectiveness and feasibility of the improved algorithm and has certain clinical application potential.
MC-PDARTS: Multi-Cell Progressive Differentiable Architecture Search

Recently, network architecture search has been practiced in more and more tasks, but its high cost makes it difficult for small research teams or individual researchers to follow. Differentiable architecture search (DARTS) algorithm provides an easier available solution in searching effective network architectures. Although DARTS algorithm has greatly reduced the search cost as compared with other evolution based or reinforcement learning based methods, there is still a long way to go before it is widely used, since gradient based methods consume a lot of GPU memory and its search space lacks diversity. Encouraged by the idea of progressive differentiable architecture search (P-DARTS), we propose a multi-cell progressive differentiable architecture search (MC-PDARTS) algorithm, which allows cells at different levels to learn different structures, and progressively searches the best structure for the corresponding level in each search stage. The proposed algorithm greatly enlarges the search space of the search network while reducing the memory consumption and search time in the search process, and alleviates the problem of performance collapse caused by too many skip connections which often occur in previous gradient based methods. Compared with the previous algorithms, the proposed MC-PDARTS algorithm achieves state-of-the-art performance on CIFAR-10 and CIFAR-100 datasets with around 4-hour search on a single Nvidia GTX1080Ti GPU. In order to reduce the performance loss in the transfer process, MC-PDARTS searches directly on a large target dataset (ImageNet) without proxy tasks, achieving state-of-the-art performance.
Blockchain-Based Multi-Cloud Data Storage System Disaster Recovery

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.
FATPaSE: Proposing a Computationally Intelligent Framework for Automated Target Profiling and Social Engineering

Social engineering is recurrently listed as a prime threat according to major cyber security agencies. Over the course of the last decades, various sources expressed serious concerns about the potential impact of artificial intelligence on automating criminal cyber activities. With the recent developments and skyrocketing popularity of large language models, this threat forecast drastically worsened within an overwhelmingly short time period. In this paper, we propose the framework FATPaSE which pursues the goal of maximizing automation of digital profiling and social engineering aided by computational intelligence techniques. We discuss the design of the framework in detail, argue for possible technological choices to realize specific parts and elaborate on already existing proof of concept work. The interconnection of all components should enable a working, fully automated kill chain. During the subsequent design science oriented implementation, the achievable degree of automation will be intensely studied, resulting in novel, innovative research artifacts. Our progress shall be directly validated in the field based on a realistic, industrial test setup. We will continuously publish our results during the development process and expect to gain deepened insights on the potentials of the idea to contribute to the toolbox of cyber security experts.