Reviewerly/exHarmony huggingface dataset:
Multi-Scale Transformer Network With Edge-Aware Pre-Training for Cross-Modality MR Image Synthesis

Cross-modality magnetic resonance (MR) image synthesis can be used to generate missing modalities from given ones. Existing (supervised learning) methods often require a large number of paired multi-modal data to train an effective synthesis model. However, it is often challenging to obtain sufficient paired data for supervised training. In reality, we often have a small number of paired data while a large number of unpaired data. To take advantage of both paired and unpaired data, in this paper, we propose a Multi-scale Transformer Network (MT-Net) with edge-aware pre-training for cross-modality MR image synthesis. Specifically, an Edge-preserving Masked AutoEncoder (Edge-MAE) is first pre-trained in a self-supervised manner to simultaneously perform 1) image imputation for randomly masked patches in each image and 2) whole edge map estimation, which effectively learns both contextual and structural information. Besides, a novel patch-wise loss is proposed to enhance the performance of Edge-MAE by treating different masked patches differently according to the difficulties of their respective imputations. Based on this proposed pre-training, in the subsequent fine-tuning stage, a Dual-scale Selective Fusion (DSF) module is designed (in our MT-Net) to synthesize missing-modality images by integrating multi-scale features extracted from the encoder of the pre-trained Edge-MAE. Furthermore, this pre-trained encoder is also employed to extract high-level features from the synthesized image and corresponding ground-truth image, which are required to be similar (consistent) in the training. Experimental results show that our MT-Net achieves comparable performance to the competing methods even using 70% of all available paired data. Our code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lyhkevin/MT-Net</uri> .
Aspect-based sentiment analysis on multi-domain reviews through word embedding

Abstract The finest resource for consumers to evaluate products is online product reviews, and finding such reviews that are accurate and helpful can be difficult. These reviews may sometimes be corrupted, biased, contradictory, or lacking in detail. This opens the door for customer-focused review analysis methods. A method called “Multi-Domain Keyword Extraction using Word Vectors” aims to streamline the customer experience by giving them reviews from several websites together with in-depth assessments of the evaluations. Using the specific model number of the product, inputs are continuously grabbed from different e-commerce websites. Aspects and key phrases in the reviews are properly identified using machine learning, and the average sentiment for each keyword is calculated using context-based sentiment analysis. To precisely discover the keywords in massive texts, word embedding data will be analyzed by machine learning techniques. A unique methodology developed to locate trustworthy reviews considers several criteria that determine what makes a review credible. The experiments on real-time data sets showed better results compared to the existing traditional models.
Graph Knows Unknowns: Reformulate Zero-Shot Learning as Sample-Level Graph Recognition

Zero-shot learning (ZSL) is an extreme case of transfer learning that aims to recognize samples (e.g., images) of unseen classes relying on a train-set covering only seen classes and a set of auxiliary knowledge (e.g., semantic descriptors). Existing methods usually resort to constructing a visual-to-semantics mapping based on features extracted from each whole sample. However, since the visual and semantic spaces are inherently independent and may exist in different manifolds, these methods may easily suffer from the domain bias problem due to the knowledge transfer from seen to unseen classes. Unlike existing works, this paper investigates the fine-grained ZSL from a novel perspective of sample-level graph. Specifically, we decompose an input into several fine-grained elements and construct a graph structure per sample to measure and utilize element-granularity relations within each sample. Taking advantage of recently developed graph neural networks (GNNs), we formulate the ZSL problem to a graph-to-semantics mapping task, which can better exploit element-semantics correlation and local sub-structural information in samples. Experimental results on the widely used benchmark datasets demonstrate that the proposed method can mitigate the domain bias problem and achieve competitive performance against other representative methods.
The Perils of Trial-and-Error Reward Design: Misdesign through Overfitting and Invalid Task Specifications

In reinforcement learning (RL), a reward function that aligns exactly with a task's true performance metric is often necessarily sparse. For example, a true task metric might encode a reward of 1 upon success and 0 otherwise. The sparsity of these true task metrics can make them hard to learn from, so in practice they are often replaced with alternative dense reward functions. These dense reward functions are typically designed by experts through an ad hoc process of trial and error. In this process, experts manually search for a reward function that improves performance with respect to the task metric while also enabling an RL algorithm to learn faster. This process raises the question of whether the same reward function is optimal for all algorithms, i.e., whether the reward function can be overfit to a particular algorithm. In this paper, we study the consequences of this wide yet unexamined practice of trial-and-error reward design. We first conduct computational experiments that confirm that reward functions can be overfit to learning algorithms and their hyperparameters. We then conduct a controlled observation study which emulates expert practitioners' typical experiences of reward design, in which we similarly find evidence of reward function overfitting. We also find that experts' typical approach to reward design---of adopting a myopic strategy and weighing the relative goodness of each state-action pair---leads to misdesign through invalid task specifications, since RL algorithms use cumulative reward rather than rewards for individual state-action pairs as an optimization target. Code, data: github.com/serenabooth/reward-design-perils
Towards Global Video Scene Segmentation with Context-Aware Transformer

Videos such as movies or TV episodes usually need to divide the long storyline into cohesive units, i.e., scenes, to facilitate the understanding of video semantics. The key challenge lies in finding the boundaries of scenes by comprehensively considering the complex temporal structure and semantic information. To this end, we introduce a novel Context-Aware Transformer (CAT) with a self-supervised learning framework to learn high-quality shot representations, for generating well-bounded scenes. More specifically, we design the CAT with local-global self-attentions, which can effectively consider both the long-term and short-term context to improve the shot encoding. For training the CAT, we adopt the self-supervised learning schema. Firstly, we leverage shot-to-scene level pretext tasks to facilitate the pre-training with pseudo boundary, which guides CAT to learn the discriminative shot representations that maximize intra-scene similarity and inter-scene discrimination in an unsupervised manner. Then, we transfer contextual representations for fine-tuning the CAT with supervised data, which encourages CAT to accurately detect the boundary for scene segmentation. As a result, CAT is able to learn the context-aware shot representations and provides global guidance for scene segmentation. Our empirical analyses show that CAT can achieve state-of-the-art performance when conducting the scene segmentation task on the MovieNet dataset, e.g., offering 2.15 improvements on AP.
A Novel Integrated Neutrosophic Cosine Operator Based Linear Programming ANP-EDAS MCGDM Strategy to Select Anti-Pegasus Software

In this paper, we have established Cosine Trigonometric Operational Law (CTOL) and constructed Cosine Trigonometric Neutrosophic Weighted Averaging (CT-NWA) operator in the domain of generalized neutrosophic environment, which has been endorsed in a Multi-Criteria Group Decision Making (MCGDM) technique to select the best Anti-Pegasus software. In this study, the expertize recommendations and public opinion are well taken in context of debatable Pegasus issue in the Indian political, bureaucratic and democratic aspects. The problem has been addressed and resolved by using the integrated methodology of Analytical Network Process (ANP) and Evaluation based on Distance from Average Solution (EDAS) strategies. Here, we have optimized the weight factors and the gradation values of the decision makers as well as the sub-criteria of each criterion using Linear Programming (LP) model. Finally, we have performed the comparative analysis from various aspects to justify the reliability of our results.
How Useful Are Educational Questions Generated by Large Language Models?

Controllable text generation (CTG) by large language models has a huge potential to transform education for teachers and students alike. Specifically, high quality and diverse question generation can dramatically reduce the load on teachers and improve the quality of their educational content. Recent work in this domain has made progress with generation, but fails to show that real teachers judge the generated questions as sufficiently useful for the classroom setting; or if instead the questions have errors and/or pedagogically unhelpful content. We conduct a human evaluation with teachers to assess the quality and usefulness of outputs from combining CTG and question taxonomies (Bloom’s and a difficulty taxonomy). The results demonstrate that the questions generated are high quality and sufficiently useful, showing their promise for widespread use in the classroom setting.
Point-Teaching: Weakly Semi-supervised Object Detection with Point Annotations

Point annotations are considerably more time-efficient than bounding box annotations. However, how to use cheap point annotations to boost the performance of semi-supervised object detection is still an open question. In this work, we present Point-Teaching, a weakly- and semi-supervised object detection framework to fully utilize the point annotations. Specifically, we propose a Hungarian-based point-matching method to generate pseudo labels for point-annotated images. We further propose multiple instance learning (MIL) approaches at the level of images and points to supervise the object detector with point annotations. Finally, we propose a simple data augmentation, named Point-Guided Copy-Paste, to reduce the impact of those unmatched points. Experiments demonstrate the effectiveness of our method on a few datasets and various data regimes. In particular, Point-Teaching outperforms the previous best method Group R-CNN by 3.1 AP with 5% fully labeled data and 2.3 AP with 30% fully labeled data on the MS COCO dataset. We believe that our proposed framework can largely lower the bar of learning accurate object detectors and pave the way for its broader applications. The code is available at https://github.com/YongtaoGe/Point-Teaching.
Ant Colony Optimization with Levy-Based Unequal Clustering and Routing (ACO-UCR) Technique for Wireless Sensor Networks

Wireless Sensor Networks (WSN) became a novel technology for ubiquitous livelihood and still remains a hot research topic because of its applicability in diverse domains. Energy efficiency treated as a crucial factor lies in the designing of WSN. Clustering is commonly applied to increase the energy efficiency and reduce the energy utilization. The proper choice of cluster heads (CHs) and cluster sizes is important in a cluster-based WSN. The CHs which are placed closer to base station (BS) are affected by the hot spot issue and it exhausts its energy faster than the usual way. For addressing this issue, a new unequal clustering and routing technique using ant colony optimization (ACO) algorithm is presented. Initially, CHs are chosen and clusters are constructed based on several variables. Next, the ACO algorithm with levy distribution is applied for the selection of optimal paths between two nodes in the network. A comprehensive validation set takes place under diverse situations under the position of BS. The experimental outcome verified the superiority of the presented model under several validation parameters.
Clinical Informatics Approaches to Facilitate Cancer Data Sharing

Objectives: Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data. Methods: We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models. Results: Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation. Conclusions: Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise.
ChatGPT’s Capabilities in Spotting and Analyzing Writing Errors Experienced by EFL Learners

The recent Large Language Models (LLMs) use advanced algorithms to identify areas where sentence structure and word choice can be improved and to detect grammar, syntax, and spelling mistakes in sentences. This study aimed to investigate the effectiveness of the Chat Generative Pre-trained Transformer (ChatGPT) in detecting English as a foreign language (EFL) learners’ writing errors compared to human instructors. This study examines the ChatGPT as a recent and advanced LLM in analyzing and processing EFL learners’ writing issues. This paper provides valuable insights into the potential benefits and challenges of integrating Artificial Intelligence (AI) into EFL writing education. Our results revealed that ChatGPT successfully identified most surface-level errors but could not detect writing errors related to deep structure and pragmatics. Conversely, human teachers could spot most of these issues. These findings suggest that while ChatGPT can be a valuable tool in identifying surface-level errors, it cannot replace human instructors’ expertise and nuanced understanding in detecting errors related to the more complex aspects of writing. The writing error types (data) are statistically analyzed. The descriptive analysis displays valuable insights into the reliability of the data and its potential implications, where the F-score, which measures the statistical model accuracy, is found to be 1.5. In the meantime, the p-value score, which shows the probability of obtaining results as extreme as the detected data, is calculated to be 0.23. The results suggest that the collected data is statistically significant, and further analysis may yield valuable insights.
TMSTC*: A Path Planning Algorithm for Minimizing Turns in Multi-Robot Coverage

Coverage path planning is a major application for mobile robots, which requires robots to move along a planned path to cover the entire map. For large-scale tasks, multirobot systems offer significant advantages. In this letter, we propose Turn-minimizing Multirobot Spanning Tree Coverage Star (TMSTC*), an improved multirobot coverage path planning (mCPP) algorithm based on MSTC*. Our algorithm partitions the map into minimum bricks as tree branches, transforming the problem into finding the maximum independent set of a bipartite graph. We then use a greedy strategy to connect bricks and form a tree, aiming to minimize the number of turns of the corresponding circumnavigating coverage path. Our experimental results show that our approach enables multiple robots to make fewer turns, resulting in faster completion of coverage tasks compared to other popular algorithms.
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution

Exploiting image patches instead of whole images has proved to be a powerful approach to tackling various problems in image processing. Recently, Wasserstein patch priors (WPPs), which are based on the comparison of the patch distributions of the unknown image and a reference image, were successfully used as data-driven regularizers in the variational formulation of superresolution. However, for each input image, this approach requires the solution of a nonconvex minimization problem which is computationally costly. In this paper, we propose to learn two kinds of neural networks in an unsupervised way based on WPP loss functions. First, we show how convolutional neural networks (CNNs) can be incorporated. Once the network, called WPPNet, is learned, it can be very efficiently applied to any input image. Second, we incorporate conditional normalizing flows to provide a tool for uncertainty quantification. Numerical examples demonstrate the very good performance of WPPNets for superresolution in various image classes, even if the forward operator is known only approximately.
The degrees of central bank digital currency adoption across countries: a preliminary analysis

This study empirically examines factors affecting the different stages of CBDC adoption using a sample of 55 countries engaged in CBDC projects from 2014 to 2021. The findings indicate that anti-money laundering and terrorist financing and the financial market development, inflation and technological factors are critical determinants of CBDC adoption at different stages.
Approximate reconstructability of quantum states and noisy quantum secret sharing schemes

We introduce and analyze approximate quantum secret sharing in a formal cryptographic setting, wherein a dealer encodes and distributes a quantum secret to players such that authorized structures (sets of subsets of players) can approximately reconstruct the quantum secret and omnipotent adversarial agents controlling nonauthorized subsets of players are approximately denied the quantum secret. In particular, viewing the map encoding the quantum secret shares for players in an authorized structure as a quantum channel, we show that approximate reconstructability of the quantum secret by these players is possible if and only if the information leakage, given in terms of a certain entanglement-assisted capacity of the complementary quantum channel to the players outside the structure and the environment, is small.
A Novel Artificial Spider Monkey Based Random Forest Hybrid Framework for Monitoring and Predictive Diagnoses of Patients Healthcare

Early diagnosis of diseases such as cancer, cardiovascular, diabetes, HIV, AIDS, Lyme, and tuberculosis can enable timely treatment, which enhances efficacy and helps reduce the severity and mortality while lowering overall healthcare costs. Predictive analytics is an emerging approach that comprehends trends in patient data to identify patterns in patient data, detect subtle signs of diseases, and enable early diagnosis for more effective treatment. However, traditional predictive diagnosis approaches rely on statistical models that are contingent on the data’s average pattern and are unable to capture the data’s inherent patterns, leading to ineffective and inaccurate diagnoses. In this paper, we introduce a novel Artificial Spider Monkey-based Random Forest (ASM-RF) hybrid framework that combines the predictive analytics of a Random Forest algorithm with Artificial Intelligence to evaluate patient health data, spot patterns, diagnose modest indications, and automate intelligent decisions for enhancing the healthcare system. The proposed ASM-RF hybrid framework employs a fitness function to evaluate the spider monkey’s performance at the classification layer and update accuracy and recall, resulting in more accurate patient disease diagnoses and automating timely treatment decisions for improving the overall healthcare system. Moreover, we use Identity-based Encryption (IBE), which enables the encryption of data with private and public keys coupled with users’ identities as the encryption keys, assisting in enhancing the security of the healthcare system. A dataset collected from three different IoT sensor devices, ten participants, and twelve activities is employed to simulate the proposed ASM-RF hybrid framework, which is then contrasted to cutting-edge predictive diagnostic algorithms in terms of accuracy, precision, Area under the Curve, execution time, recall, and F-measure. The proposed method exhibits superior performance when compared to conventional methods, as evidenced by its exceptional accuracy (99.52%), precision (99.12%), Area Under the Curve (AUC) (99.00%), recall (99.22%), F-measure (97.12%), and significantly reduced execution time (6s).
From the desks of ROS maintainers: A survey of modern &amp; capable mobile robotics algorithms in the robot operating system 2

The Robot Operating System 2 (ROS 2) is rapidly impacting the intelligent machines sector — on space missions, large agriculture equipment, multi-robot fleets, and more. Its success derives from its focused design and improved capabilities targeting product-grade and modern robotic systems. Following ROS 2’s example, the mobile robotics ecosystem has been fully redesigned based on the transformed needs of modern robots and is experiencing active development not seen since its inception. This paper comes from the desks of the key ROS Navigation maintainers to review and analyze the state of the art of robotics navigation in ROS 2. This includes new systems without parallel in ROS 1 or other similar mobile robotics frameworks. We discuss current research products and historically robust methods that provide differing behaviors and support for most every robot type. This survey consists of overviews, comparisons, and expert insights organized by the fundamental problems in the field. Some of these implementations have yet to be described in literature and many have not been benchmarked relative to others. We end by providing a glimpse into the future of the ROS 2 mobile robotics ecosystem.