Reviewerly/exHarmony huggingface dataset:
Resource Allocation for Text Semantic Communications

Semantic communications have shown its great potential to improve the transmission reliability, especially in the low signal-to-noise regime. However, resource allocation for semantic communications still remains unexplored, which is a critical issue in guaranteeing the semantic transmission reliability and the communication efficiency. To fill this gap, we investigate the spectral efficiency in the semantic domain and rethink the semantic-aware resource allocation issue. Specifically, taking text semantic communication as an example, the semantic spectral efficiency (S-SE) is defined for the first time, and is used to optimize resource allocation in terms of channel assignment and the number of transmitted semantic symbols. Additionally, for fair comparison of semantic and conventional communication systems, a transform method is developed to convert the conventional bit-based spectral efficiency to the S-SE. Simulation results demonstrate the validity and feasibility of the proposed resource allocation method, as well as the superiority of semantic communications in terms of the S-SE.
An improved and random synthetic minority oversampling technique for imbalanced data

Imbalanced data learning has become a major challenge in data mining and machine learning. Oversampling is an effective way to re-achieve the balance by generating new samples. However, most oversampling methods cannot perform well in the presence of noises and complicated distribution structures, very easy to generate redundant/unsafe/outlier samples. To handle this problem, we endeavor to propose a novel oversampling method, namely Improved and Random Synthetic Minority Oversampling Technique (IR-SMOTE). The core idea of IR-SMOTE is three-fold: (1) by applying an ascending operation to sort the majority class samples, noise samples in each cluster of minority class after k-means clustering are successfully removed; (2) the number of synthetic samples is adaptively assigned to each cluster in minority class by means of the kernel density estimation technique; and (3) based on the obtained attributes of the temporary synthetic samples in terms of random-SMOTE, a new synthesizing method is developed to generate new samples with a guaranteed diversity. Finally, many comparison experiments have been carried out on 18 well-known data sets, which illustrate the effectiveness and universal applicability of the proposed IR-SMOTE method for imbalanced data classification.
Developing image analysis methods for digital pathology

The potential to use quantitative image analysis and artificial intelligence is one of the driving forces behind digital pathology. However, despite novel image analysis methods for pathology being described across many publications, few become widely adopted and many are not applied in more than a single study. The explanation is often straightforward: software implementing the method is simply not available, or is too complex, incomplete, or dataset-dependent for others to use. The result is a disconnect between what seems already possible in digital pathology based upon the literature, and what actually is possible for anyone wishing to apply it using currently available software. This review begins by introducing the main approaches and techniques involved in analysing pathology images. I then examine the practical challenges inherent in taking algorithms beyond proof-of-concept, from both a user and developer perspective. I describe the need for a collaborative and multidisciplinary approach to developing and validating meaningful new algorithms, and argue that openness, implementation, and usability deserve more attention among digital pathology researchers. The review ends with a discussion about how digital pathology could benefit from interacting with and learning from the wider bioimage analysis community, particularly with regard to sharing data, software, and ideas. © 2022 The Author. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture

The negative effect of financial crimes on financial institutions has grown dramatically over the years. To detect crimes such as credit card fraud, several single and hybrid machine learning approaches have been used. However, these approaches have significant limitations as no further investigation on different hybrid algorithms for a given dataset were studied. This research proposes and investigates seven hybrid machine learning models to detect fraudulent activities with a real word dataset. The developed hybrid models consisted of two phases, state-of-the-art machine learning algorithms were used first to detect credit card fraud, then, hybrid methods were constructed based on the best single algorithm from the first phase. Our findings indicated that the hybrid model Adaboost + LGBM is the champion model as it displayed the highest performance. Future studies should focus on studying different types of hybridization and algorithms in the credit card domain.
Boundary-guided network for camouflaged object detection

Compared with the traditional object segmentation/detection, camouflaged object detection is much more difficult due to the indefinable boundaries and high intrinsic similarities between the camouflaged regions and the background. Although various algorithms have been proposed to solve the issue, these methods still suffer from coarse boundaries and are not competent to identify the camouflaged objects from the background in complex scenarios. In this paper, we propose a novel boundary-guided network to address this challenging problem in a coarse-to-fine manner. Specifically, we design a locating module to infer the initial location of the camouflaged objects by exploiting local detailed cues and global contextual information. Moreover, a boundary-guided fusion module is proposed to explore the complementary relationship between the camouflaged regions and their boundaries. By leveraging the boundary feature, we can not only generate prediction maps with sharper boundaries but also effectively eliminate background noises. Equipped with the two key modules, our BgNet is capable of segmenting camouflaged regions accurately and quickly. Extensive experimental results on four widely used benchmark datasets demonstrate that the proposed BgNet runs at a real-time speed (36 FPS) on a single NVIDIA Titan XP GPU and outperforms 17 state-of-the-art competing algorithms in terms of six standard evaluation metrics. Source code will be publicly available at https://github.com/clelouch/BgNet upon paper acceptance.
Federated Learning for Electronic Health Records

In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data to obtain sufficient statistical power for certain hypothesis testings as well as predictive and subgroup studies. The wide adoption of electronic health records (EHRs) has made multi-institutional collaboration much more feasible. However, concerns over infrastructures, regulations, privacy, and data standardization present a challenge to data sharing across healthcare institutions. Federated Learning (FL), which allows multiple sites to collaboratively train a global model without directly sharing data, has become a promising paradigm to break the data isolation. In this study, we surveyed existing works on FL applications in EHRs and evaluated the performance of current state-of-the-art FL algorithms on two EHR machine learning tasks of significant clinical importance on a real world multi-center EHR dataset.
MTANet: Multitask-Aware Network With Hierarchical Multimodal Fusion for RGB-T Urban Scene Understanding

Understanding urban scenes is a fundamental ability requirement for assisted driving and autonomous vehicles. Most of the available urban scene understanding methods use red-green-blue (RGB) images; however, their segmentation performances are prone to degradation under adverse lighting conditions. Recently, many effective artificial neural networks have been presented for urban scene understanding and have shown that incorporating RGB and thermal (RGB-T) images can improve segmentation accuracy even under unsatisfactory lighting conditions. However, the potential of multimodal feature fusion has not been fully exploited because operations such as simply concatenating the RGB and thermal features or averaging their maps have been adopted. To improve the fusion of multimodal features and the segmentation accuracy, we propose a multitask-aware network (MTANet) with hierarchical multimodal fusion (multiscale fusion strategy) for RGB-T urban scene understanding. We developed a hierarchical multimodal fusion module to enhance feature fusion and built a high-level semantic module to extract semantic information for merging with coarse features at various abstraction levels. Using the multilevel fusion module, we exploited low-, mid-, and high-level fusion to improve segmentation accuracy. The multitask module uses boundary, binary, and semantic supervision to optimize the MTANet parameters. Extensive experiments were performed on two benchmark RGB-T datasets to verify the improved performance of the proposed MTANet compared with state-of-the-art methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Sparse and skew hashing of K-mers

A dictionary of k-mers is a data structure that stores a set of n distinct k-mers and supports membership queries. This data structure is at the hearth of many important tasks in computational biology. High-throughput sequencing of DNA can produce very large k-mer sets, in the size of billions of strings-in such cases, the memory consumption and query efficiency of the data structure is a concrete challenge.To tackle this problem, we describe a compressed and associative dictionary for k-mers, that is: a data structure where strings are represented in compact form and each of them is associated to a unique integer identifier in the range [0,n). We show that some statistical properties of k-mer minimizers can be exploited by minimal perfect hashing to substantially improve the space/time trade-off of the dictionary compared to the best-known solutions.https://github.com/jermp/sshash.Supplementary data are available at Bioinformatics online.
Identity Model for Blockchain-Based Land Registry System: A Comparison

The land registry system is one of the essential components of any governance model required to ascertain the ownership records uniquely. This paper reviews the existing literature and provides a detailed literature review consisting of 3 stages based on three research questions (RQ) that highlight the step by step evaluation and analysis. We selected 48 primary articles out of 477 extracted from different scientific databases based on criteria and RQ defined in the research method section. The majority of these papers focus on assessing the identity issues related to the land registry system and reviewing the existing identity models to find the best possible identity model to resolve the identified identity problems in the land registry. This paper examines the current land registry model and its shortcomings. It explains the various blockchain types and their characteristics. It further evaluates the usability of blockchain technology in different aspects of the land registry. Identity management is one of such weaknesses in the blockchain-based land registry model that has been assessed in detail. Identity issues of blockchain-based models have been further evaluated on defined criteria. The paper ends with a discussion on possible identity models and their comparative analysis to ascertain the most suitable identity model to resolve the identity issues of land registry systems.
Pedestrian safety using the Internet of Things and sensors: Issues, challenges, and open problems

Pedestrian safety has emerged recently as a public health challenge worldwide. People are being physically harmed due to losing focus on their surroundings and putting safety at risk. Though pedestrian safety is a shared responsibility, researchers suggest that distractions by smart devices and reduced cognitive skills are major causes of accidents. There is a scope to assist pedestrians through amplifying cognitive skills using heterogeneous Internet of Things (IoT) and sensors. These technologies could discover and warn users about unanticipated events such as just-in-time warnings about the hazards, distractions, extreme weather calamities, and potential impending dangers. An automated personalized agent helps monitor, diagnose problems, and protect people in an urban environment. Researchers have proposed various systems and implemented them in multiple domains. In this survey, we assessed, analyzed, and compared the most recent research on pedestrian safety. We identified the challenges, research gaps, and future directions toward using technology to improve pedestrian safety.
A comprehensive survey on 3D face recognition methods

3D face recognition (3DFR) has emerged as an effective means of characterizing facial identity over the past several decades. Depending on the types of techniques used in recognition, these methods are categorized into traditional and modern. The former generally extract distinctive facial features (e.g. global, local, and hybrid features) for matching, whereas the latter rely primarily on deep learning to perform 3DFR in an end-to-end way. Many literature surveys have been carried out reviewing either traditional or modern methods alone, while only a few studies are conducted simultaneously on both of them. This survey presents a state-of-the-art for 3DFR covering both traditional and modern methods, focusing on the techniques used in face processing, feature extraction, and classification. In addition, we review some specific face recognition challenges, including pose, illumination, expression variations, self-occlusion, and spoofing attack. The commonly used 3D face datasets have been summarized as well.
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity

Current adversarial attack research reveals the vulnerability of learning-based classifiers against carefully crafted perturbations. However, most existing attack methods have inherent limitations in cross-dataset generalization as they rely on a classification layer with a closed set of categories. Furthermore, the perturbations generated by these methods may appear in regions easily perceptible to the human visual system (HVS). To circumvent the former problem, we propose a novel algorithm that attacks semantic similarity on feature representations. In this way, we are able to fool classifiers without limiting attacks to a specific dataset. For imperceptibility, we introduce the low-frequency constraint to limit perturbations within high-frequency components, ensuring perceptual similarity between adversarial examples and originals. Extensive experiments on three datasets (CIFAR-10, CIFAR-100, and ImageNet-1K) and three public online platforms indicate that our attack can yield misleading and transferable adversarial examples across architectures and datasets. Additionally, visualization results and quantitative performance (in terms of four different metrics) show that the proposed algorithm generates more imperceptible perturbations than the state-of-the-art methods. Code is made available at https://github.com/LinQinLiang/SSAH-adversarial-attack.
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

This work studies learning from a synergy process of 3D Morphable Models (3DMM) and 3D facial landmarks to predict complete 3D facial geometry, including 3D alignment, face orientation, and 3D face modeling. Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks. 3D landmarks can be extracted and refined from face meshes built by 3DMM parameters. We next reverse the representation direction and show that predicting 3DMM parameters from sparse 3D landmarks improves the information flow. Together we create a synergy process that utilizes the relation between 3D landmarks and 3DMM parameters, and they collaboratively contribute to better performance. We extensively validate our contribution on full tasks of facial geometry prediction and show our superior and robust performance on these tasks for various scenarios. Particularly, we adopt only simple and widely-used network operations to attain fast and accurate facial geometry prediction. Codes and data: https: //choyingw.github.io/works/SynergyNet/.
Learnable Online Graph Representations for 3D Multi-Object Tracking

Autonomous systems that operate in dynamic environments require robust object tracking in 3D as one of their key components. Most recent approaches for 3D multi-object tracking (MOT) from LIDAR use object dynamics together with a set of handcrafted features to match detections of objects across multiple frames. However, manually designing such features and heuristics is cumbersome and often leads to suboptimal performance. In this work, we instead strive towards a unified and learning based approach to the 3D MOT problem. We design a graph structure to jointly process detection and track states in an online manner. To this end, we employ a Neural Message Passing network for data association that is fully trainable. Our approach provides a natural way for track initialization and handling of false positive detections, while significantly improving track stability. We demonstrate the merit of the proposed approach in the nuScenes tracking challenge 2021 with a state-of-the-art performance of 65.6&#x0025; AMOTA with 58&#x0025; fewer ID-switches, resulting in the best LIDAR only submission and an overall second place.
Quantum Secure Direct Communication with Private Dense Coding Using a General Preshared Quantum State

For real-world quantum communication, how perfect does a system actually need to be? Here researchers propose a private dense-coding framework using a general preshared quantum state, for quantum secure direct communication that unifies a large number of protocols aimed at secure classical communication using quantum resources. This study provides a detailed performance analysis for the framework in both the asymptotic and nonasymptotic cases, and a better upper bound on information leakage in the finite-length setting. For practical application, the authors also propose an efficient method for implementation, under certain assumptions.
A topic modeled unsupervised approach to single document extractive text summarization

Automatic Text Summarization (ATS) is an essential field in natural language processing that attempts to condense large text documents so that users can assimilate information quickly. It finds uses in medical document summarization, review generation, and opinion mining. This work investigated an unsupervised extractive summarization approach that combined clustering with topic modeling to reduce topic bias. Latent Dirichlet Allocation was used for topic modeling, while K-Medoids clustering was employed for summary generation. The approach was evaluated on three datasets—Wikihow, CNN/DailyMail, and the DUC2002 Corpus. The Recall Oriented-Understudy for Gisting Evaluation (ROUGE) metrics were used for comparative analysis against recently reported techniques, specifically ROUGE-1 (R-1), ROUGE-2 (R-2), and ROUGE-L (R-L). The suggested framework offered scores of 34.80%, 9.13%, and 32.30% on the Wikihow Dataset, 43.90%, 19.01%, and 41.50% on the CNN/DailyMail Dataset, and 49.35%, 31.53%, and 41.72% on the DUC2002 Corpus (R-1, R-2, R-L respectively). These reported metrics are found to be superior when compared to similar recent works. Further, execution time of the proposed method was also recorded and compared with counterparts, which established its superior speed. Based on these promising outcomes, it was concluded that an unsupervised extractive summarization approach with greater subtopic focus significantly improves over generic topic modeling semantic and deep learning approaches.
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation

Domain adaptive semantic segmentation aims to learn a model with the supervision of source domain data, and produce satisfactory dense predictions on unlabeled target domain. One popular solution to this challenging task is self-training, which selects high-scoring predictions on target samples as pseudo labels for training. However, the produced pseudo labels often contain much noise because the model is biased to source domain as well as majority categories. To address the above issues, we propose to di-rectly explore the intrinsic pixel distributions of target do-main data, instead of heavily relying on the source domain. Specifically, we simultaneously cluster pixels and rectify pseudo labels with the obtained cluster assignments. This process is done in an online fashion so that pseudo labels could co-evolve with the segmentation model without extra training rounds. To overcome the class imbalance problem on long-tailed categories, we employ a distribution align-ment technique to enforce the marginal class distribution of cluster assignments to be close to that of pseudo labels. The proposed method, namely Class-balanced Pixel-level Self-Labeling (CPSL), improves the segmentation performance on target domain over state-of-the-arts by a large margin, especially on long-tailed categories. The source code is available at ht tps: / / gi thub. com/lslrh/CPSL.
Blockchain in oil and gas industry: Applications, challenges, and future trends

Today's systems, approaches, and technologies leveraged for managing oil and gas supply chain operations fall short of providing operational transparency, traceability, audit, security, and trusted data provenance features. Also, a large portion of the existing systems are centralized, manual, and highly disintegrated, which make them vulnerable to manipulation and the single point of failure problem. In this survey, we explore the potential opportunities and applications of blockchain technology in managing the exploration, production, and supply chain and logistics operations in the oil and gas industry as it can offer traceability, immutability, transparency, and audit features in a decentralized, trusted, and secure manner. We discuss state-of-the-art blockchain-based schemes, research projects, business initiatives, and case studies to highlight the practicability of blockchain in the oil and gas industry. We present the potential opportunities brought about by blockchain technology in various use cases and application scenarios. We present several systems that use blockchain-based smart contracts to automate critical services such as petroleum product tracking and tracing, international trade document protection, and coordination of purchasing and bidding activities for granting oil exploration rights to petroleum exploration and development companies. Finally, we present open challenges acting as future research directions.