Reviewerly/exHarmony huggingface dataset:
Visual-Linguistic Alignment and Composition for Image Retrieval with Text Feedback

In this paper, we focus on the task of image retrieval with text feedback, which maintains two key challenges. One is the misalignment problem between different modalities, and the other is to selectively alter the corresponding attributes on the reference image according to the textual words. To this end, we propose a novel visual-linguistic alignment and composition network (ACNet) consisting of two key components: the modality alignment module (MAM) and the relation composition module (RCM). Specifically, the MAM performs alignment between the features from different modalities by applying image-text contrastive loss. The RCM correlates the image regions with their corresponding words and then adaptively modifies the specific regions of the reference image conditioned on textual semantics. Quantitative and Qualitative experiments on three datasets not only demonstrate that our ACNet outperforms state-of-the-art models, but also verify the effectiveness of our method.
One-Stage Object Detector Using Feature Fusion and Dual Attention

Multi-scale object detection and small object detection are important tasks in computer vision and robotics. Traditional one-stage object detection algorithms are fast, but cannot adapt to changes in object scale perfectly and have low accuracy for small object detection. In this paper, we proposed a one-stage object detector using feature fusion and attentional mechanisms. This detector has better adaptability and higher accuracy with marginal extra cost. Our algorithm is based on the CenterNet framework, with modifications and enhancements. First, we used a feature pyramid network with bidirectional cross-scale connections to enable the full integration of lower layers and topmost features, which improved the network's adaptability to scale changes. Second, we proposed a dual attention module that integrates spatial attention and channel attention and added it to every layer of the pyramid, so that the network can focus more on useful information, which enhances the network's capability to detect small objects. Using a lightweight backbone, our algorithm achieves 40.2 mAP on COCO at 33 fps on a Titan Xp GPU, up to 3 mAP compared to CenterNet with the same backbone. Besides, for the small targets in COCO, our algorithm achieves 20.63 mAP, Which is 30% higher than CenterNet. We also tested our algorithm on a body part detection dataset of Pascal, Voc,which further demonstrates the effectiveness of our algorithm in terms of application.
The Need for Proactive Digital Forensics in Addressing Critical Infrastructure Cyber Attacks

Currently, cyberattacks are increasing and have negatively impacted the operation of critical infrastructures globally. Consequently, the organizations overseeing these infrastructures are seemingly allocating more resources toward investigating cyberattacks after they have occurred instead of planning for and preventing potential attacks. Global supply chains, financial and health services, industrial, nuclear power plants, water treatment facilities, pipelines, and transportation are increasingly being targeted by threat actors utilizing ransomware and other destructive attacks. This research explores the need for proactive digital forensic modeling approaches that focus on Supervisory Control and Data Acquisition (SCADA), Industrial Control Systems (ICSs), and automation in the preparation and defense of critical infrastructure networks.
Incentives Effect on the Size of Mining Pool for Power-of-Work Blockchain Systems

Blockchain mining is a trending topic because bitcoin prices and mined incentives are attractive. The trend of blockchain mining is joining a mining pool, which also allows recording of a block. Pool selection is a major problem for the proof-of-work consensus protocol because larger mining pools provide higher winning probability; however, numerous miners share the reward. We modeled a mining process as a draw ball problem to simulate various sizes of mining pools according to the existing mining mechanism used for bitcoin blockchains. An average winning rate and individual reward were set as metrics to help miners select the pool. By utilizing the ball drawing computational simulation model, we analyzed and compared the mining winning rate, reward, and margin reward with several reward mechanisms. The simulation results provided managerial insights for individual miners. Moreover, they support advanced reward mechanism selection for mining pool operators.
CDAP: A Cultural Algorithm for Data Placement in Big Data Workflows

The performance of executing big data workflows in the Cloud highly depends on the placement of the workflow original datasets. An optimal data placement reduces the data movement among virtual machines, and as a result, it significantly reduces the workflow makespan. Data placement is an NP-hard problem; therefore in this paper, we propose CDAP (Cultural algorithm Data Placement), a novel metaheuristic data placement strategy based on Cultural Algorithms (CA) to improve the performance of a workflow by minimizing the data movement among the virtual machines during workflow executions. The effectiveness of CDAP is demonstrated through extensive experiments where we evaluated our proposed method against a set of well-known data placement strategies.
Implementation of A Real-Time Lossless JPEG-LS Compression Algorithm Based on FPGA

In order to balance image compression ratio, image compression quality and image processing time, an FPGA-based image compression system is proposed. The LOCO-I / JPEG-LS algorithm aims to provide lossless compression ratio, and reduce the overall complexity of the compression algorithm. In this work, the maximum compression ratio can reach 16:1 and average compression ratio can reach 2.61:1 for different input image streams. The maximum operational frequency of the proposed work can reach 103 MHz. Previous FPGA/ASIC-based JPEG-LS compressors bring forward optimization road maps. It is to use pipeline optimization techniques such as forward and look-ahead to split the context update loop, shorten the critical path to increase the clock frequency [1]–[4]. This road map fully explores the pipelining of JPEG-LS but ignores its parallelism potential, which limits the performance at 1 pixel per cycle (PPC). After implementing the algorithm into a Xilinx xc7k70tfbv676-3 FPGA demo board, we propose a new architecture that can process an average of 4 pixels per cycle.
Design a Hardware of Image Stitching Using LBP Algorithm

Image stitching algorithms are often used in real life and are mostly handled by software. The proposed method is a hardware design level image processing method using a local binary pattern. To solve the background matching problem during stitching, the proposed method is used instead of the existing stitching algorithm. Designed with Verilog HDL, it minimizes multipliers to reduce resource consumption. It applies parallel processing and pipeline architecture to increase processing speed. The goal is to use the proposed algorithm to design hardware with similar accuracy to the existing algorithm.
Improved UCT Algorithm for WTN-EinStein Würfelt Nicht! Game Combining Domain Knowledge

EinStein würfelt nicht! (EWN) game attracts the participation of the majority of scholars relying on randomness and strategy. From the perspective of the global optimization, the UCT algorithm and the domain knowledge are introduced to apply in EinStein würfelt nicht! game agent. First, the evaluation factor C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cur</inf> and the sub-optimal node balance factor C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bal</inf> are introduced to maintain the balance of depth search and breadth search. Second, the domain knowledge for simulation and establishing the tree based on probabilistic rules and natural characteristics of EWN is presented. Finally, a complete EinStein würfelt nicht! game agent (AKe) is constructed. AKe contrasted with other five algorithm agents (Random, SA, SE, UCT1, UCT2), which proved the effectiveness of the improvement. Ake has won the champion of the 2021 Chinese University Student Computer Games Competition with an 87% winning rate, which further validates the significance of the algorithm.
Study on Hybrid Encryption Technology of Power Gateway Based on AES and RSA Algorithm

In order to solve the problem of security risks of gateways in complex communication networks of power Internet of Things, this paper proposes a hybrid encryption system that combines AES and RSA to ensure the security of power gateway communication. Aiming at the security risks of S-boxes in AES algorithms, this paper proposes a new pair of irreducible polynomials and affine transformations to construct a new S-box, which solves the problems of short iteration cycle of traditional S- boxes and failure to meet strict avalanche criteria. Aiming at the problem of low computational efficiency of RSA algorithm, this paper changes the double prime number to three prime number, and uses the direct modulo power algorithm and Montgomery modulo multiplication to simplify the large number modulo power operation. Experimental results show that the optimized hybrid encryption can ensure both the operation rate and the security of the system.
On Unsupervised Reconstruction with Dressed Multilayered Variational Quantum Circuits

The advantages of unsupervised quantum machine learning are still under study and appear to be very promising. Trainable variational quantum circuits are one example of successful approaches to combining classic machine learning and quantum. However, there is no clear path toward a quantum advantage for different types of variational circuits. This paper furthers the research efforts in understanding the potential and applications of hybrid quantum circuits. We study different circuits and see how similar they perform in an unsupervised learning task in an autoencoder configuration over a large multimodal dataset.
Image Super-Resolution with Implicit Texture Pattern Modulation

Image super-resolution is one of the classical low-level vision tasks with the purpose of restoring a high-resolution image with fine details. Being aware of texture patterns with an image would benefit super-resolution performance a lot. However, it would be difficult to predict texture patterns for each region because of lack of annotations with different kinds of categories. In this work, we propose to implicitly model texture information with each region and take that as prior to promote super-resolution performance. In order to fully explore texture patterns, a hybrid model of convolutional neural networks and transformers is proposed. It is able to take advantage of both local and long range dependencies within an image to super-resolve an image. Specifically, a set of transformers are employed to model self-similarity among pixels within an image and to make dense texture patterns prediction at each pixel. The computed texture pattern representation then works as condition to modulate convolution-based residual blocks. In this way texture patterns could be integrated into the CNNs to obtain powerful features for image super-resolution. Extensive experiments on several benchmark datasets demonstrate its favorable performance against state-of-the-art methods and show its potential as a generic design for hybrid of transformers and CNNs.
Satellite Video Stabilization Method Based on Global Motion Consistency

As a new earth observation technology, video satellite is of great significance for extracting and analyzing large-scale and deep-level dynamic information. Satellite video data will gradually become one of the important data sources for intelligent surveying and mapping application services. However, video satellites have problems such as random jitter, which leads to unstable video, and low matching accuracy between frames due to satellite video content containing moving objects. In this paper, a satellite video stabilization method based on global motion consistency constraints is proposed. The method first uses the lightweight ORB algorithm to extract and describe the feature points of each video frame and perform frame-by-frame matching. Secondly, global motion consistency is used to eliminate mismatch points and imprecise matching points. Then, the affine transformation matrix is obtained and linearized according to the matching point pairs. Finally, the linearized affine transformation matrix is used to resample frame by frame to obtain a stable video. To verify the effectiveness of the method in this paper, the video data of SkySat-1, Jilin-1 and Zhuhai-1 are selected for experiments. The experimental results show that the method proposed in this paper can overcome the inter-frame dynamic target interference and improve the inter-frame matching accuracy, to efficiently achieve stable satellite video output.
Challenges of Self-Supervised Learning for Unified, Multi-Modal, Multi-Task Transformer Models

The recent success of multi-modal multi-task transformer models combined with their ability to learn in a scalable self-supervised fashion has presented evidence that omnipotent models trained with heterogeneous data and tasks are within the realms of possibility. This paper presents several research questions and impediments related towards the training of generalized transformer architectures.
A Feedback Learning Segmentation Algorithm for Medical Images of Breast Cancer Incorporating Attention Mechanism

The study of breast cancer image lesion segmentation algorithms is gradually playing an increasingly important role in the medical field to assist physicians in diagnosis. To further segment lesions precisely, a network model with UNet as a substrate and a fusion of attention mechanisms is proposed. The model is trained using breast cancer image data. An image chunking training strategy is used in the model training process. The maximum pooling is replaced by adaptive adjustment pooling in the downsampling process. The SE module is introduced at the bottom layer of the model. A five-in-one feature fusion strategy is added before convolution operation. A model feedback learning from deep to shallow layers is added to the bottom three layers of the model. The experimental results show that this algorithm improves the accuracy compared to the original UNet, residual ResUnet, and CBAM for the segmentation of breast cancer lesions.
Data Behind the Walls – An Advanced Architecture for Data Privacy Management

In today's highly connected society, we are constantly asked to provide personal information to retailers, voter surveys, medical professionals, and other data collection efforts. The collected data is stored in large data warehouses. Organisations and statistical agencies share and use this data to facilitate research in public health, economics, sociology, etc. However, this data contains sensitive information about individuals, which can result in identity theft, financial loss, stress and depression, embarrassment, abuse, etc. Therefore, one must ensure rigorous management of individuals' privacy. We propose, an advanced data privacy management architecture composed of three layers. The data management layer consists of de-identification and anonymisation, the access management layer for re-enforcing data access based on the concepts of Role-Based Access Control and the Chinese Wall Security Policy, and the roles layer for regulating different users. The proposed system architecture is validated on health care datasets.
Multiscan Shape Estimation for Extended Object Tracking

Extended Object Tracking (EOT) is a advantageous technique for achieving situational awareness in autonomous vehicle systems. The EOT problem is to both estimate the movement and spatial dimensions of an object using high-resolution measurements. In the case of laser measurements or other types of measurements that correspond to points on the object’s boundary, the true measurement model of the EOT problem is based on an implicit equation for the measurement coordinates. This intrinsic implicity is often not addressed directly in several EOT models found in the literature. In this paper, the EOT problem is reformulated as a least square minimization problem without compromising the original implicit measurement model by introducing an extra variable for each measurement. In addition, this new least squares formulation allows considering measurements and state variables for a whole time window, and not just a single time step. An EOT algorithm based on solving the derived least squares minimization problem is proposed and tested with simulated scenarios.
A Semantics-Aware Normalizing Flow Model for Anomaly Detection

Anomaly detection in computer vision aims to detect outliers from input image data. Examples include texture defect detection and semantic discrepancy detection. However, existing methods are limited in detecting both types of anomalies, especially for the latter. In this work, we propose a novel semantics-aware normalizing flow model to address the above challenges. First, we employ the semantic features extracted from a backbone network as the initial input of the normalizing flow model, which learns the mapping from the normal data to a normal distribution according to semantic attributes, thus enhances the discrimination of semantic anomaly detection. Second, we design a new feature fusion module in the normalizing flow model to integrate texture features and semantic features, which can substantially improve the fitting of the distribution function with input data, thus achieving improved performance for the detection of both types of anomalies. Extensive experiments on five well-known datasets for semantic anomaly detection show that the proposed method outperforms the state-of-the-art baselines. The codes will be available at https://github.com/SYLan2019/SANF-AD.
Privacy-Protected Facial Expression Recognition Augmented by High-Resolution Facial Images

Cloud-based expression recognition from high-resolution facial images may put the subjects’ privacy at risk. We identify two kinds of privacy leakage, the appearance leakage in which the visual appearances of subjects are disclosed and the identity-pattern leakage in which the identity information of subjects is dug out. To address both leakages, we propose privacy-protected facial expression recognition from low-resolution facial images with the help of high-resolution facial images. Specifically, to prevent appearance leakage, we propose to extract identity-invariant representations from downsampled images, from which the visually distinguishable appearances cannot be recovered. To prevent identity-pattern leakage, we propose to eliminate the identity information from the extracted representations by leveraging the disentangled representations of high-resolution images as privileged information. After training, our method can fully capture identity-invariant representations from downsampled images for expression recognition without the requirement of high-resolution samples. These privacy-protected representations can be safely transmitted through the Internet. Experimental results in different scenarios demonstrate that the proposed method protects privacy without significantly inhibiting facial expression recognition.
Survey and Comparison of Nature Inspired Optimization Algorithms

Nature Inspired Meta-heuristics algorithms have made a profound contribution to the field of optimization ever since they have been initiated in the late 1980s and have gained immense popularity in the last decade. The aim of this research survey is to study and compare the performance of different types of nature inspired meta-heuristics algorithms by the means of evaluating them over several benchmark functions and compare the best so far value obtained by the algorithms. Every algorithm is run multiple times to ensure diversity in results and are compared using four different measures.
Flexible Multi-Target Tracking with Track Management Using Dirichlet and Gaussian Processes

A key challenge for multi-target trackers is being able to track both agile and simply moving targets effectively. This is only furthered by the standard difficulties of automated track initiation and deletion. This paper proposes a solution to this, where unknown associations and track management are handled by a Dirichlet process prior, and Gaussian processes model the dynamics of the targets. The promising performance of the proposed tracker is demonstrated on both synthetic and real radar data against a selection of other methods.
Letter Embedding Guidance Diffusion Model for Scene Text Editing

Scene text editing(STE) aims to modify the text in the scene image to the target text while retaining the original style. Existing models are based on GAN, where the source image and the target text are input only once during the generation process, and this approach could not fully obtain the style of the source image and content of the target text. In this paper, we propose an STE method based on the classifier-free guidance diffusion model. To our best knowledge, our model is the first work that developed diffusion models to handle the STE task. Specifically, we divide the STE task into multiple steps and extract style information and text content information in each step. In addition, we introduce the letter embedding method as guidance. We experimentally prove that our method outperforms other STE models in terms of overall realism and maintaining glyphs.
Multimodal Semantic Fusion for Vehicle Driving Based on Residual Connections and Attention Mechanism

Cameras and 3DLidar are indispensable sensors in vehicle driving, in which cameras can provide rich color and texture information, and 3DLidar can capture environmental information with higher precision and longer distances, especially in depth measurement. Sensors can complement each other in the detection of the environment, so how to efficiently fuse the two modal information has become a research hotspot. In this paper, a module based on residual network and attention mechanism is designed to fuse the features of the dual-stream network, enhance the deep features through the direct connection, and change the feature combination form to achieve a better road environment semantic segmentation effect.
Skeleton-Based Action Recognition Using Residual Activation Fish-Shaped Network for Human-Robot Interaction

In the field of human-robot interaction, action recognition is a challenging problem. In this paper, a residual activation fish-shaped network is proposed for action recognition, which contains 3 parts of fish tail, fish body and fish head. A multi-feature input model with physiognomic Cartesian motion features and intrinsic Geometric features is constructed, which can eliminate the influence of changes in camera depth of field and observation orientation. An extended residual convolution structure is designed to utilize global information to refine coupled useful sub-features, and learn a structured semantic representations on skeletons of each frame. Experimental results show that the proposed method achieves an accuracy of 80.96% on the JHMDB dataset, 95.02% on SHREC14, and 93.16% on SHREC28. In addition, a human-robot interaction experiment is conducted, which verifies the effectiveness of the proposed action recognition method.