Reviewerly/exHarmony huggingface dataset:
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation

Domain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervision from auxiliary tasks (such as depth estimation) has the potential to heal this shift because many visual tasks are closely related to each other. However, such a supervision is not always available. In this work, we leverage the guidance from self-supervised depth estimation, which is available on both domains, to bridge the domain gap. On the one hand, we propose to explicitly learn the task feature correlation to strengthen the target semantic predictions with the help of target depth estimation. On the other hand, we use the depth prediction discrepancy from source and target depth decoders to approximate the pixel-wise adaptation difficulty. The adaptation difficulty, inferred from depth, is then used to refine the target semantic segmentation pseudo-labels. The proposed method can be easily implemented into existing segmentation frameworks. We demonstrate the effectiveness of our approach on the benchmark tasks SYNTHIA-to-Cityscapes and GTA-to-Cityscapes, on which we achieve the new state-of-the-art performance of 55.0% and 56.6%, respectively. Our code is available at https://qin.ee/corda.
High-Performance Discriminative Tracking with Transformers

End-to-end discriminative trackers improve the state of the art significantly, yet the improvement in robustness and efficiency is restricted by the conventional discriminative model, i.e., least-squares based regression. In this paper, we present DTT, a novel single-object discriminative tracker, based on an encoder-decoder Transformer architecture. By self- and encoder-decoder attention mechanisms, our approach is able to exploit the rich scene information in an end-to-end manner, effectively removing the need for hand-designed discriminative models. In online tracking, given a new test frame, dense prediction is performed at all spatial positions. Not only location, but also bounding box of the target object is obtained in a robust fashion, streamlining the discriminative tracking pipeline. DTT is conceptually simple and easy to implement. It yields state-of-the-art performance on four popular benchmarks including GOT-10k, LaSOT, NfS, and TrackingNet while running at over 50 FPS, confirming its effectiveness and efficiency. We hope DTT may provide a new perspective for single-object visual tracking.
Vision Transformer for Fast and Efficient Scene Text Recognition

Scene text recognition (STR) enables computers to read text in natural scenes such as object labels, road signs and instructions. STR helps machines perform informed decisions such as what object to pick, which direction to go, and what is the next step of action. In the body of work on STR, the focus has always been on recognition accuracy. There is little emphasis placed on speed and computational efficiency which are equally important especially for energy-constrained mobile machines. In this paper we propose ViTSTR, an STR with a simple single stage model architecture built on a compute and parameter efficient vision transformer (ViT). On a comparable strong baseline method such as TRBA with accuracy of 84.3%, our small ViTSTR achieves a competitive accuracy of 82.6% (84.2% with data augmentation) at \(2.4\times \) speed up, using only 43.4% of the number of parameters and 42.2% FLOPS. The tiny version of ViTSTR achieves 80.3% accuracy (82.1% with data augmentation), at \(2.5\times \) the speed, requiring only 10.9% of the number of parameters and 11.9% FLOPS. With data augmentation, our base ViTSTR outperforms TRBA at 85.2% accuracy (83.7% without augmentation) at \(2.3\times \) the speed but requires 73.2% more parameters and 61.5% more FLOPS. In terms of trade-offs, nearly all ViTSTR configurations are at or near the frontiers to maximize accuracy, speed and computational efficiency all at the same time.
Survey on blockchain for future smart grids: Technical aspects, applications, integration challenges and future research

Smart Grid 2.0 is envisaged to automate the operations of the intelligent electricity grid. Blockchain and smart contracts are integrated to facilitate the transformation from DSO-centric operations to consumer-oriented, distributed electricity grid management. The envisaged smart grids, integrated with blockchain would provoke challenges, which would hinder the maximum utilization of Distribute Energy Resources (DERs). This comprehensive review aims at analyzing the applicability of blockchain technology in Smart Grid 2.0, which would facilitate a seamless decentralization process. Further, the paper elaborates the blockchain-based applications of future smart grid operations and the role of blockchain in each scenario. The paper further provides a concise analysis on the blockchain integration challenges, thereby ensure secure and scalable, decentralized operations of future, autonomous electricity networks.
Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.
Edge Intelligence: Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.
Predictive modelling for solar power-driven hybrid desalination system using artificial neural network regression with Adam optimization

Today artificial intelligence is a major utility technology that can help industry and research in many fields, including solar thermal energy for water desalination purposes. Hence, the main aim of the paper focus on utilizing an artificial intelligent regression model to predict the efficiency of water desalination, which depends on solar thermal energy. The contribution helps manufacturers and research determine the productivity of the designed desalination system in the beta environment before realizing the system. The used desalination system merges and hybrids in our experimental modelling solar still and air humidification and dehumidification. Regards the prediction of performance and productivity, a wide range of regression algorithms, including multilayer perceptron (MLP), decision trees (DT), and Bayesian ridge regression (BRR), were applied. Also, the MLP is optimized to enhance the prediction by employing the Adam optimization to propose OMLP. The OMLP is compared to regular MLP, DT, and BRR using a variety of evaluation metrics, including statistical measures, the goodness of fit, and regression accuracy. The OMLP achieves better performance of dependent variables; YHDH, YSS, Tswh,in, Tswh,o, Ta,H,in, Ta,H,o, and Φa,H,in using whole independent variables by RMSE ∈ [0.07171, 8.05636], Max Error ∈ [0.10108, 14.19858], and CRM ∈ [0.0202, 0.0329] and using top three correlated independent variables by MAE ∈ [0. 0514, 5.777], RMSE ∈ [0.07171, 7.0559], and CRM ∈ [0.0029, 0.0498].
Vehicle Selection and Resource Optimization for Federated Learning in Vehicular Edge Computing

As a distributed deep learning paradigm, federated learning (FL) provides a powerful tool for the accurate and efficient processing of on-board data in vehicular edge computing (VEC). However, FL involves the training and transmission of model parameters, which consumes the vehicles’ precious energy resources and takes up much time. It is a departure from many applications with severe real-time requirements in VEC. And the capabilities and data quality of each vehicle are distinct that will affect the performance of training the model. Therefore, it is crucial to select the appropriate vehicles to participate in learning tasks and optimize resource allocation under learning time and energy consumption constraints. In this paper, taking the vehicle position and velocity into consideration, we formulate a min-max optimization problem to jointly optimize the on-board computation capability, transmission power, and local model accuracy to achieve the minimum cost in the worst case of FL. Specifically, we propose a greedy algorithm to select vehicles with higher image quality dynamically, and it keeps the system’s overall cost to a minimum in FL. The formulated optimization problem is a nonlinear programming problem, so we decompose it into two subproblems. For the resource allocation problem, we use the Lagrangian dual problem and the subgradient projection method to approximate the optimal value iteratively. For the local model accuracy problem, we develop an adaptive harmony algorithm for heuristic search. The simulation results show that our proposed algorithms have well convergence and effectiveness and achieve a tradeoff between cost and fairness.
An Automated Word Embedding with Parameter Tuned Model for Web Crawling

In recent years, web crawling has gained a significant attention due to the drastic advancements in the World Wide Web. Web Search Engines have the issue of retrieving massive quantity of web documents. One among the web crawlers is the focused crawler, that intends to selectively gather web pages from the Internet. But the efficiency of the focused crawling can easily be affected by the environment of web pages. In this view, this paper presents an Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) model for focused web crawling. The proposed model involves different processes namely pre-processing, Incremental Skip-gram Model with Negative Sampling (ISGNS) based word embedding, bidirectional long short-term memory-based classification and bird swarm optimization based hyperparameter tuning. The SGNS training desires to go over the complete training data to pre-compute the noise distribution before performing Stochastic Gradient Descent (SGD) and the ISGNS technique is derived for the word embedding process. Besides, the cosine similarity is computed from the word embedding matrix to generate a feature vector which is fed as input into the Bidirectional Long Short-Term Memory (BiLSTM) for the prediction of website relevance. Finally, the Birds Swarm Optimization-Bidirectional Long Short-Term Memory (BSO-BiLSTM) based classification model is used to classify the webpages and the BSO algorithm is employed to determine the hyperparameters of the BiLSTM model so that the overall crawling performance can be considerably enhanced. For validating the enhanced outcome of the presented model, a comprehensive set of simulations are carried out and the results are examined in terms of different measures. The Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) technique has attained a higher harvest rate of 85% when compared with the other techniques. The experimental results highlight the enhanced web crawling performance of the proposed model over the recent state of art web crawlers.
Retinex-inspired color correction and detail preserved fusion for underwater image enhancement

Many practical underwater applications need to address color cast, blurring, and low contrast of underwater images for display and further analysis. To cope with the aforementioned issues, a novel underwater image enhancement method based on Retinex-inspired color correction and detail preserved fusion technology is proposed. First, we employ a Retinex-inspired color correction strategy to remove the color cast induced by underwater light scattering. We then build on blending three images directly derived from a local contrast-enhanced version, a detail version, and a global contrast-enhanced version of the corrected underwater image. The local and global contrast-enhanced versions promote the transfer of color and contrast to the output image. Meanwhile, the detail version improves the edges and details of the output image. Qualitative and quantitative comparisons demonstrated that the proposed method achieves superior enhancement performance for underwater images of different scenes.
A stable meaningful image encryption scheme using the newly-designed 2D discrete fractional-order chaotic map and Bayesian compressive sensing

In order to safely and covertly protect the digital image information during public channel transmission and localized storage, a stable image visually secure encryption algorithm by using 2D discrete fractional-order chaotic map (FOCM), Bayesian compressive sensing (BCS), and discrete V transform (DVT) is presented in this paper. First, the key-controlled measurement matrix constructed by the infinite collapse-Chebyshev-coupling chaotic map is employed to measure the scrambled wavelet packet coefficients of plain image in parallel. Then, the fractional-order chaotic map designed through piece-wise constant arguments method is employed to generate the secret code streams with assistance of the counter mode, and under its control, the compressed image is re-encrypted into the intermediate secret image. Next, the DVT-based embedding technology is proposed to stochastically embed the secret data into the non-secret-involved host image. Eventually, experimental results show that under the premise of ensuring security, the proposed algorithm improves the visual security around 1∼9 dB, and stabilizes its compression performance within 1 dB, compared with other existing related encryption algorithms.
Saliency-Associated Object Tracking

Most existing trackers based on deep learning perform tracking in a holistic strategy, which aims to learn deep representations of the whole target for localizing the target. It is arduous for such methods to track targets with various appearance variations. To address this limitation, another type of methods adopts a part-based tracking strategy which divides the target into equal patches and tracks all these patches in parallel. The target state is inferred by summarizing the tracking results of these patches. A potential limitation of such trackers is that not all patches are equally informative for tracking. Some patches that are not discriminative may have adverse effects. In this paper, we propose to track the salient local parts of the target that are discriminative for tracking. In particular, we propose a fine-grained saliency mining module to capture the local saliencies. Further, we design a saliency-association modeling module to associate the captured saliencies together to learn effective correlation representations between the exemplar and the search image for state estimation. Extensive experiments on five diverse datasets demonstrate that the proposed method performs favorably against state-of-the-art trackers.
Full-Duplex Strategy for Video Object Segmentation

Appearance and motion are two important sources of information in video object segmentation (VOS). Previous methods mainly focus on using simplex solutions, lowering the upper bound of feature collaboration among and across these two cues. In this paper, we study a novel framework, termed the FSNet (Full-duplex Strategy Network), which designs a relational cross-attention module (RCAM) to achieve the bidirectional message propagation across embedding subspaces. Furthermore, the bidirectional purification module (BPM) is introduced to update the inconsistent features between the spatial-temporal embeddings, effectively improving the model robustness. By considering the mutual restraint within the full-duplex strategy, our FSNet performs the cross-modal feature-passing (i.e., transmission and receiving) simultaneously before the fusion and decoding stage, making it robust to various challenging scenarios (e.g., motion blur, occlusion) in VOS. Extensive experiments on five popular benchmarks (i.e., DAVIS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</inf> , FBMS, MCL, SegTrack-V2, and DAVSOD <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</inf> ) show that our FSNet outperforms other state-of-the-arts for both the VOS and video salient object detection tasks.
Mining Latent Classes for Few-shot Segmentation

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e., potential novel classes are treated as background during training phase. Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. Based on conventional episodic training on support-query pairs, we introduce an additional mining branch that exploits latent novel classes via transferable sub-clusters, and a new rectification technique on both background and fore-ground categories to enforce more stable prototypes. Over and above that, our transferable sub-cluster has the ability to leverage extra unlabeled data for further feature enhancement. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms previous state-of-the-art by a large margin of 3.7% mIOU on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> and 7.0% mIOU on COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> at the cost of 74% fewer parameters and 2.5x faster inference speed. The source code is available at https://github.com/LiheYoung/MiningFSS.
Analysis of Blockchain's enablers for improving sustainable supply chain transparency in Africa cocoa industry

Blockchain technology can improve supply chain transparency to solve many problems in agricultural supply chains, such as lack of social responsibility and poor environmental performance. However, how to enable the transparency and sustainability of supply chain through blockchain technology is indeed a complex decision-making problem. Based on the technology-organization-environment (TOE) theoretical framework, this paper develops a hierarchical enablers framework for improving sustainable supply chain transparency (SSCT) by blockchain technology in the cocoa industry. Then, the best-worst method (BWM) is used to evaluate the weights of main enablers and sub-enablers by a real case of cocoa supply chain in an emerging African economy. The case results indicate the most important main enabler is “Technical Characteristics”, and the top three sub-enablers are: “blockchain smart contract”, “blockchain security,” and “tracking product components”, respectively. The framework and method of this study can help decision-makers and supply chain managers develop strategies to promote the effective application of blockchain to improve SSCT. As far as we know, this paper is one of the first studies to evaluate blockchain's enablers for SSCT, especially in the context of African emerging economies.
Design and DSP implementation of a fractional-order detuned laser hyperchaotic circuit with applications in image encryption

In this paper, a fractional-order hyperchaotic detuned laser system (FHDLS) is proposed, and a novel image cryptosystem with high-security performance is designed by combining FHDLS, improved shuffling algorithm, and DNA mutation diffusion algorithm. Firstly, the complex dynamics of FHDLS is researched by phase diagram, bifurcation diagram, Lyapunov exponential spectrum, and spectral entropy (SE) complexity. The attractor coexistence phenomenon of the system is analyzed from different initial values for given parameters. Meanwhile, the randomness of the proposed FHDLS is verified by the correlation. Then, the FHDLS is implemented by the analog circuit and the digital hardware (Digital Signal Processing, DSP), respectively. Finally, make use of these excellent characteristics, and perturb the initial values and chaotic state of the system through Secure Hash Algorithm 256 (SHA-256). Then the pixel-level and bit-level replacement and transformation operations are performed on the image respectively, to implement a chaos-based image cryptosystem. The regimen performs one scrambled and diffusion together and one diffusion operation to achieve maximum confusion and diffusion. To demonstrate the safety performance of the proposed image cryptographic regimen, standard security analyses is adopted, including histogram, correlation, information entropy, security key, anti-differential attack, robustness, etc., and it is compared with the existing advanced algorithms. The experimental results indicate that the regimen works well in encryption and can resist various attacks effectively, and the FHDLS has wide application prospects in image encryption.