Reviewerly/exHarmony huggingface dataset:
BES-Net: Boundary Enhancing Semantic Context Network for High-Resolution Image Semantic Segmentation

This paper focuses on the high-resolution (HR) remote sensing images semantic segmentation task, whose goal is to predict semantic labels in a pixel-wise manner. Due to the rich complexity and heterogeneity of information in HR remote sensing images, the ability to extract spatial details (boundary information) and semantic context information dominates the performance in segmentation. In this paper, based on the frequently used fully convolutional network framework, we propose a boundary enhancing semantic context network (BES-Net) to explicitly use the boundary to enhance semantic context extraction. BES-Net mainly consists of three modules: (1) a boundary extraction module for extracting the semantic boundary information, (2) a multi-scale semantic context fusion module for fusing semantic features containing objects with multiple scales, and (3) a boundary enhancing semantic context module for explicitly enhancing the fused semantic features with the extracted boundary information to improve the intra-class semantic consistency, especially in those pixels containing boundaries. Extensive experimental evaluations and comprehensive ablation studies on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of BES-Net, yielding an overall improvement of 1.28/2.36/0.72 percent in mF1/mIoU/OA over FCN_8s when the BE and MSF modules are combined by the BES module. In particular, our BES-Net achieves a state-of-the-art performance of 91.4% OA on the ISPRS Vaihingen dataset and 92.9%/91.5% mF1/OA on the ISPRS Potsdam dataset.
Efficient E-Mail Spam Detection Strategy Using Genetic Decision Tree Processing with NLP Features

In this modern era, each and everything is computerized, and everyone has their own smart gadgets to communicate with others around the globe without any range limitations. Most of the communication pathways belong to smart applications, call options in smartphones, and other multiple ways, but e-mail communication is considered the main professional communication pathway, which allows business people as well as commercial and noncommercial organizations to communicate with one another or globally share some important official documents and reports. This global pathway attracts many attackers and intruders to do a scam with such innovations; in particular, the intruders generate false messages with some attractive contents and post them as e-mails to global users. This kind of unnecessary and not needed advertisement or threatening mails is considered as spam mails, which usually contain advertisements, promotions of a concern or institution, and so on. These mails are also considered or called junk mails, which will be reflected as the same category. In general, e-mails are the usual way of message delivery for business oriented as well as any official needs, but in some cases there is a necessity of transferring some voice instructions or messages to the destination via the same e-mail pathway. These kinds of voice-oriented e-mail accessing are called voice mails. The voice mail is generally composed to deliver the speech aspect instructions or information to the receiver to do some particular tasks or convey some important messages to the receiver. A voice-mail-enabled system allows users to communicate with one another based on speech input which the sender can communicate to the receiver via voice conversations, which is used to deliver voice information to the recipient. These kinds of mails are usually generated using personal computers or laptops and exchanged via general e-mail pathway, or separate paid and nonpaid mail gateways are available to deal with certain mail transactions. The above-mentioned e-mail spam is considered in many past researches and attains some solutions, but in case of voice-based e-mail aspect, there will be no options to manage such kind of security parameters. In this paper, a hybrid data processing mechanism is handled with respect to both text-enabled and voice-enabled e-mails, which is called Genetic Decision Tree Processing with Natural Language Processing (GDTPNLP). This proposed approach provides a way of identifying the e-mail spam in both textual e-mails and speech-enabled e-mails. The proposed approach of GDTPNLP provides higher spam detection rate in terms of text extraction speed, performance, cost efficiency, and accuracy. These all will be explained in detail with graphical output views in the Results and Discussion.
Low-Light Image Enhancement via Self-Reinforced Retinex Projection Model

Low-light image enhancement aims to improve the quality of images captured under low-lightening conditions, which is a fundamental problem in computer vision and multimedia areas. Although many efforts have been invested over the years, existing illumination-based models tend to generate unnatural-looking results (e.g., over-exposure). It is because that the widely-adopted illumination adjustment (e.g., Gamma Correction) breaks down the favorable smoothness property of the original illumination derived from the well-designed illumination estimation model. To settle this issue, a great-efficiency and high-quality Self-Reinforced Retinex Projection (SRRP) model is developed in this paper, which contains optimization modules of both illumination and reflectance layers. Specifically, we construct a new fidelity term with the self-reinforced function for the illumination optimization to eliminate the dependence of the illumination adjustment to obtain a desired illumination with the excellent smoothing property. By introducing a flexible feasible constraint, we obtain a reflectance optimization module with projection. Owing to its flexibility, we can extend our model to an enhanced version by integrating a data-driven denoising mechanism as the projection, which is able to effectively handle the generated noises/artifacts in the enhanced procedure. In the experimental part, on one side, we make ample comparative assessments on multiple benchmarks with considerable state-of-the-art methods. These evaluations fully verify the outstanding performance of our method, in terms of the qualitative and quantitative analyses and execution efficiency. On the other side, we also conduct extensive analytical experiments to indicate the effectiveness and advantages of our proposed model.
Cross-Level Contrastive Learning and Consistency Constraint for Semi-Supervised Medical Image Segmentation

Semi-supervised learning (SSL), which aims at leveraging a few labeled images and a large number of unlabeled images for network training, is beneficial for relieving the burden of data annotation in medical image segmentation. According to the experience of medical imaging experts, local attributes such as texture, luster and smoothness are very important factors for identifying target objects like lesions and polyps in medical images. Motivated by this, we propose a cross-level contrastive learning scheme to enhance representation capacity for local features in semi-supervised medical image segmentation. Compared to existing image-wise, patch-wise and point-wise contrastive learning algorithms, our devised method is capable of exploring more complex similarity cues, namely the relational characteristics between global and local patch-wise representations. Additionally, for fully making use of cross-level semantic relations, we devise a novel consistency constraint that compares the predictions of patches against those of the full image. With the help of the cross-level contrastive learning and consistency constraint, the unlabelled data can be effectively explored to improve segmentation performance on two medical image datasets for polyp and skin lesion segmentation respectively. Code of our approach is available here.
Joint-Bone Fusion Graph Convolutional Network for Semi-Supervised Skeleton Action Recognition

In recent years, graph convolutional networks (GCNs) play an increasingly critical role in skeleton-based human action recognition. However, most GCN-based methods still have two main limitations: 1) They only consider the motion information of the joints or process the joints and bones separately, which are unable to fully explore the latent functional correlation between joints and bones for action recognition. 2) Most of these works are performed in the supervised learning way, which heavily relies on massive labeled training data. To address these issues, we propose a semi-supervised skeleton-based action recognition method which has been rarely exploited before. We design a novel correlation-driven joint-bone fusion graph convolutional network (CD-JBF-GCN) as an encoder and use a pose prediction head as a decoder to achieve semi-supervised learning. Specifically, the correlation-driven joint-bone fusion graph convolution (CD-JBF-GC) can explore the motion transmission between the joint stream and the bone stream, so as to promote both streams to learn more discriminative feature representations. The pose prediction based auto-encoder in the self-supervised training fashion allows the network to learn motion representation from the unlabeled data, which is essential for action recognition. Extensive experiments on two popular datasets, i.e. NTU-RGB+D and Kinetics-Skeleton, demonstrate that our model achieves the state-of-the-art performance for semi-supervised skeleton-based action recognition and is also useful for fully-supervised methods.
Learning Program Semantics with Code Representations: An Empirical Study

Program semantics learning is the core and fundamental for various code intelligent tasks e.g., vulnerability detection, clone detection. A considerable amount of existing works propose diverse approaches to learn the program semantics for different tasks and these works have achieved state-of-the-art performance. However, currently, a comprehensive and systematic study on evaluating different program representation techniques across diverse tasks is still missed. From this starting point, in this paper, we conduct an empirical study to evaluate different program representation techniques. Specifically, we categorize current mainstream code representation techniques into four categories i.e., Feature-based, Sequence-based, Tree-based, and Graph-based program representation technique and evaluate its performance on three diverse and popular code intelligent tasks i.e., Code Classification, Vulnerability Detection, and Clone Detection on the public released benchmark. We further design three research questions (RQs) and conduct a comprehensive analysis to investigate the performance. By the extensive experimental results, we conclude that (1) The graph-based representation is superior to the other selected techniques across these tasks. (2) Compared with the node type information used in tree-based and graph-based representations, the node textual information is more critical to learning the program semantics. (3) Different tasks require the task-specific semantics to achieve their highest performance, however combining various program semantics from different dimensions such as control dependency, data dependency can still produce promising results.
Neural-Network Decoders for Quantum Error Correction Using Surface Codes: A Space Exploration of the Hardware Cost-Performance Tradeoffs

Quantum Error Correction (QEC) is required in quantum computers to mitigate the effect of errors on physical qubits. When adopting a QEC scheme based on surface codes, error decoding is the most computationally expensive task in the classical electronic back-end. Decoders employing neural networks (NN) are well-suited for this task but their hardware implementation has not been presented yet. This work presents a space exploration of fully-connected feed-forward NN decoders for small distance surface codes. The goal is to optimize the neural network for high decoding performance, while keeping a minimalistic hardware implementation. This is needed to meet the tight delay constraints of real-time surface code decoding. We demonstrate that hardware based NN-decoders can achieve high decoding performance comparable to other state-of-the-art decoding algorithms whilst being well below the tight delay requirements $(\approx 440\ \mathrm{ns})$ of current solid-state qubit technologies for both ASIC designs $(<30\ \mathrm{ns})$ and FPGA implementations $(<90\ \mathrm{ns})$. These results designates NN-decoders as fitting candidates for an integrated hardware implementation in future large-scale quantum computers.
Improving query expansion using pseudo-relevant web knowledge for information retrieval

In the field of information retrieval, query expansion (QE) has long been used as a technique to deal with the fundamental issue of word mismatch between a user's query and the target information. In the context of the relationship between the query and expanded terms, existing weighting techniques often fail to appropriately capture the term-term relationship and term to the whole query relationship, resulting in low retrieval effectiveness. Our proposed QE approach addresses this by proposing three weighting models based on (1) tf-itf, (2) k-nearest neighbor (kNN) based cosine similarity, and (3) correlation score. Further, to extract the initial set of expanded terms, we use pseudo-relevant web knowledge consisting of the top N web pages returned by the three popular search engines namely, Google, Bing, and DuckDuckGo, in response to the original query. Among the three weighting models, tf-itf scores each of the individual terms obtained from the web content, kNN-based cosine similarity scores the expansion terms to obtain the term-term relationship, and correlation score weighs the selected expansion terms with respect to the whole query. The proposed model, called web knowledge based query expansion (WKQE), achieves an improvement of 25.89% on the MAP score and 30.83% on the GMAP score over the unexpanded queries on the FIRE dataset. A comparative analysis of the WKQE techniques with other related approaches clearly shows significant improvement in the retrieval performance. We have also analyzed the effect of varying the number of pseudo-relevant documents and expansion terms on the retrieval effectiveness of the proposed model.
Game Difficulty Adaptation and Experience Personalization: A Literature Review

This paper attempts to provide a detailed overview of the latest advancements in the field of game difficulty adaptation and experience personalization within the years 2005–2021 from the perspective of dynamic difficulty adjustment and procedural content generation. To this end, a systematic review process was followed in order to search in the database Scopus for relevant work within the addressed research scope. This process resulted in 109 relevant papers, which in turn were taxonomized according to the type of adaptation/personalization approach they employ in order to tailor games to each individual player. The aim of this survey is to address research concepts and problems that are related to the dynamic modeling of the players’ characteristics, the impact of adaptive games on their experience and the dissemination of the relevant research. Findings of this review revealed that the adaptive and personalized games can improve and enhance the experience of the players/students and the rehabilitation of patients through the dynamic modeling of distinct personality characteristics, e.g., personal interests and exhibited emotions.
Assessing blockchain technology adoption in the Norwegian oil and gas industry using Bayesian Best Worst Method

Despite the promising features of blockchain, such as enhancing efficiency, transparency, immutability, cost savings, and traceability, the technology is still not widely adopted across industries. The oil and gas industry uses state-of-the-art engineering solutions for oil and gas exploration but substantially lags behind in using innovative digital technologies that can improve operational excellence. This study proposes a multi-criteria decision-making (MCDM) framework for assessing blockchain adoption strategies. The framework builds on critical factors for blockchain adoption and four adoption strategies — single use, localization, substitution, and transformation. Data were collected from ten experts in the Norwegian oil and gas industry using a structured web survey. The Bayesian Best Worst Method (BWM), a probabilistic MCDM method, was used for analysis. The results suggest that three sub-criteria, which are lack of expertise about technology, lack of supply chain partner collaboration, and reducing operation cost, have the most impact on the adoption process. As for blockchain adoption alternatives, the fourth phase, that is, transformation, is the most preferred in the context of the Norwegian oil and gas industry. The proposed framework lays the foundation for companies to understand the critical elements that need improvement to accelerate the blockchain technology adoption process.
A higher-order zeroing neural network for pseudoinversion of an arbitrary time-varying matrix with applications to mobile object localization

The hyperpower family of iterative methods with arbitrary convergence order is one of the most used methods for estimating matrix inverses and generalized inverses, whereas the zeroing neural network (ZNN) is a type of neural dynamics developed to solve time-varying problems in science and engineering. Since the discretization of ZNN dynamics leads to the Newton iterative method for solving the matrix inversion and generalized inversion, this study proposes and investigates a family of ZNN dynamical models known as higher-order ZNN (HOZNN) models, which are defined on the basis of correlation with hyperpower iterations of arbitrary order. Because the HOZNN dynamical system requires error function powers, it is only applicable to square error functions. In this paper, we extend the original HOZNN dynamic flows to arbitrary time-dependent real matrices, both square and rectangular, and sign-bi-power activation is used to investigate the finite-time convergence of arbitrary order HOZNN dynamics. The proposed models are theoretically and numerically tested under three activation functions, and an application in solving the angle-of-arrival (AoA) localization problem demonstrates the effectiveness of the proposed design.
A high-capacity QRD-based blind color image watermarking algorithm incorporated with AI technologies

In this study, a high-capacity QR decomposition (QRD) based blind watermarking algorithm with artificial intelligence (AI) technologies for color images was proposed. Watermarking implementation involves dividing the host image into non-overlapping blocks of size 4 × 4 pixels and then applying the QRD to each block. Within each block, a two-bit watermark can be embedded by manipulating the relationship between paired elements drawn from the first column of the orthogonal matrix in the ORD. Through orthonormal restoration and iterative regulation, a perfect watermark retrieval can be guaranteed in the absence of image attacks. On top of the high-capacity watermarking, the proposed algorithm also exploits two AI technologies, namely, particle swarm optimization (PSO) and super-resolution convolutional neural network (SRCNN). The PSO seeks the optimal parameters for enhancing the imperceptibility and robustness, while the SRCNN facilitates the visual recognition of extracted watermarks. In comparison with previous matrix decomposition-based watermarking algorithms, the proposed algorithm exhibits a superior performance in imperceptibility and robustness while operating at the rate of 1/8 bit per pixel. Moreover, the SRCNN refinement contributes an improvement of 0.191 to image quality in terms of mean structural similarity index measure (MSSIM).
Reality Check of Metaverse: A First Look at Commercial Social Virtual Reality Platforms

Metaverse has grasped the news headlines recently. While being heavily advocated by the industry, there are great in-terests from academia as it demands various technological support from both hardware and software research. There has been an extensive visionary discussion of metaverse lately, but there are few studies on its technical challenges and user experience in practice. To fill this critical gap, in this paper, we take a first look at Workrooms, arguably a premature metaverse product released by Meta (a.k.a. Face-book). The goal of our study is to gain an in-depth under-standing of the current state of the metaverse and identify potential issues for improvement. Through extensive meas-urement studies, we dissect the underlying network support and demand of Workrooms. Our investigation reveals sev-eral interesting findings. For example, Workrooms utilizes different network flows to transmit virtual content and real-time multimedia content separately. This might be a prin-cipled approach that should be adopted in general. On the other hand, the current design of Workrooms faces imper-ative scalability challenges that should be addressed in any large-scale metaverse platform.
A notary group-based cross-chain mechanism

As an emerging distributed technology, blockchain has begun to penetrate into many fields such as finance, healthcare, supply chain, intelligent transportation. However, the interoperability and value exchange between different independent blockchain systems is restricting the expansion of blockchain. In this paper, a notary group-based cross-chain interaction model is proposed to achieve the interoperability between different blockchains. Firstly, a notary election mechanism is proposed to choose one notary from the notary group to act as a bridge for cross-chain transactions. Secondly, a margin pool is introduced to limit the misconduct of the elected notary and ensure the value transfer between the involved blockchains. Moreover, a reputation based incentive mechanism is used to encourage members of the notary group to participate in cross-chain transactions. Ethereum-based experiments demonstrate that the proposed mechanism can provide an acceptable performance for cross-chain transactions and provide a higher security level than ordinary cross-chain mechanisms.
Discriminative error prediction network for semi-supervised colon gland segmentation

Pixel-wise error correction of initial segmentation results provides an effective way for quality improvement. The additional error segmentation network learns to identify correct predictions and incorrect ones. The performance on error segmentation directly affects the accuracy on the test set and the subsequent self-training with the error-corrected pseudo labels. In this paper, we propose a novel label rectification method based on error correction, namely ECLR, which can be directly added after the fully-supervised segmentation framework. Moreover, it can be used to guide the semi-supervised learning (SSL) process, constituting an error correction guided SSL framework, called ECGSSL. Specifically, we analyze the types and causes of segmentation error, and divide it into intra-class error and inter-class error caused by intra-class inconsistency and inter-class similarity problems in segmentation, respectively. Further, we propose a collaborative multi-task discriminative error prediction network (DEP-Net) to highlight two error types. For better training of DEP-Net, we propose specific mask degradation methods representing typical segmentation errors. Under the fully-supervised regime, the pre-trained DEP-Net is used to directly rectify the initial segmentation results of the test set. While, under the semi-supervised regime, a dual error correction method is proposed for unlabeled data to obtain more reliable network re-training. Our method is easy to apply to different segmentation models. Extensive experiments on gland segmentation verify that ECLR yields substantial improvements based on initial segmentation predictions. ECGSSL shows consistent improvements over a supervised baseline learned only from labeled data and achieves competitive performance compared with other popular semi-supervised methods.