Reviewerly/exHarmony huggingface dataset:
TransER: Hybrid Model and Ensemble-based Sequential Learning for Non-homogenous Dehazing

Image dehazing is one of the most challenging imaging inverse problems that estimates the haze-free images from hazy ones. While recent transformer/convolutional neural network-based methods have shown excellent performance in handling both homogeneous and non-homogeneous dehazing problems, these networks are often trained end-to-end to estimate the haze-free image directly and require a large number of parameters. In this work, we propose a novel, lightweight two-stage deep network for non-homogeneous dehazing. In particular, our proposed method, denoted as TransER, consists of two separate deep neural networks which are TransConv Fusion Dehaze (TFD) model in Stage I and Lightweight Ensemble Reconstruction (LER) network in Stage II. The first model (TFD) using transformer-based encoder and decoders generates two estimates of the haze-free image: a parameter-based dehazed output based on the physical modeling of the problem and a pseudo haze-free output generated directly by the model in an end-to-end fashion. LER in stage II reconstructs the final dehazed output fusing the two estimates from stage I. We incorporate knowledge distillation to develop a teacher network with the same architecture as LER, allowing it to supervise the intermediate features. Extensive experiments performed on challenging real and synthetic scene image datasets (NTIRE 2019-2023, and RESIDE-indoor) demonstrate that TransER can outperform many state-of-the-art competing methods while using a significantly lower number of parameters. The source code is available at https://github.com/trungpsu1210/TransER.
Dynamic Conceptional Contrastive Learning for Generalized Category Discovery

Generalized category discovery (GCD) is a recently proposed open-world problem, which aims to automatically cluster partially labeled data. The main challenge is that the unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. This leads traditional novel category discovery (NCD) methods to be incapacitated for GCD, due to their assumption of unlabeled data are only from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. However, this manner largely ignores underlying relationships between instances of the same concepts (e.g., class, super-class, and sub-class), which results in inferior representation learning. In this paper, we propose a Dynamic Conceptional Contrastive Learning (DCCL)framework, which can effectively improve clustering accuracy by alternately estimating underlying visual conceptions and learning conceptional representation. In addition, we design a dynamic conception generation and update mechanism, which is able to ensure consistent conception learning and thus further facilitate the optimization of DCCL. Extensive experiments show that DCCL achieves new state-of-the-art performances on six generic and fine-grained visual recognition datasets, especially on fine-grained ones. For example, our method significantly surpasses the best competitor by 16.2% on the new classes for the CUB-200 dataset. Code is available at https://github.com/TPCD/DCCL
Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation

Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging problem, due to continuity and exceptionally skeletal precision. The action features are often derivable accurately from the full series of keyframes, and thus, leveraging the global context with transformers has been a promising data-driven embedding approach. However, existing methods are often with inputs of interpolated intermediate frame for continuity using basic interpolation methods with keyframes, which result in a trivial local minimum during training. In this paper, we propose a novel framework to formulate latent motion manifolds with keyframe-based constraints, from which the continuous nature of intermediate token representations is considered. Particularly, our proposed framework consists of two stages for identifying a latent motion subspace, i.e., a keyframe encoding stage and an intermediate token generation stage, and a subsequent motion synthesis stage to extrapolate and compose motion data from manifolds. Through our extensive experiments conducted on both the LaFAN1 and CMU Mocap datasets, our proposed method demonstrates both superior interpolation accuracy and high visual similarity to ground truth motions.
TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning with Structure-Trajectory Prompted Reconstruction for Person Re-Identification

Person re-identification (re-ID) via 3D skeleton data is an emerging topic with prominent advantages. Existing methods usually design skeleton descriptors with raw body joints or perform skeleton sequence representation learning. However, they typically cannot concurrently model different body-component relations, and rarely explore useful semantics from fine-grained representations of body joints. In this paper, we propose a generic Transformer-based Skeleton Graph prototype contrastive learning (TranSG) approach with structure-trajectory prompted reconstruction to fully capture skeletal relations and valuable spatial-temporal semantics from skeleton graphs for person re-ID. Specifically, we first devise the Skeleton Graph Transformer (SGT) to simultaneously learn body and motion relations within skeleton graphs, so as to aggregate key correlative node features into graph representations. Then, we propose the Graph Prototype Contrastive learning (GPC) to mine the most typical graph features (graph prototypes) of each identity, and contrast the inherent similarity between graph representations and different prototypes from both skeleton and sequence levels to learn discriminative graph representations. Last, a graph Structure-Trajectory Prompted Reconstruction (STPR) mechanism is proposed to exploit the spatial and temporal contexts of graph nodes to prompt skeleton graph reconstruction, which facilitates capturing more valuable patterns and graph semantics for person re-ID. Empirical evaluations demonstrate that TranSG significantly outperforms existing state-of-the-art methods. We further show its generality under different graph modeling, RGB-estimated skeletons, and unsupervised scenarios. Our codes are available at https://github.com/Kali-Hac/TranSG.
Measuring the Service Quality of Artificial Intelligence in the Tourism and Hospitality Industry

The tourism industry is rapidly adopting artificial intelligence (AI) to enhance customer experiences and improve service delivery. However, the adoption of AI has raised concerns about concierge chatbots, digital assistance, proactiveness, anthropomorphism, and security, and its impact on overall customer satisfaction. Therefore, the aim of this research is to measure the service quality of AI in the tourism industry, with primary data collected in Pondicherry. This study is a quantitative research study that utilized a survey method to collect primary data. A total of 350 respondents were targeted, with 307 valid responses obtained. The data were analyzed using confirmatory factor analysis and structural equation modeling. The study highlights that AI technology has a significant positive impact on the service quality of the tourism industry. This study contributes to the literature by providing empirical evidence to service quality in the context of AI technology and the importance of AI technology to enhance service quality and customer satisfaction.
Analysis of Trend in Meteorological and Hydrological Time-series using Mann-Kendall and Sen’s Slope Estimator Statistical Test in Akwa Ibom State, Nigeria

The monthly, seasonal, annual, and decadal trends of seven hydro-meteorological variables were analysed for stations in Akwa Ibom State, Nigeria, controlled by the Nigerian Meteorological Agency (NiMet) and the Cross River Basin Development Authority (CRBDA) from 1972 to 2021. At the 5% statistical significance level, the non-parametric Mann-Kendall and Sen's slope estimator techniques were used to detect if there was a positive or negative trend and the magnitude of the trend in hydro-meteorological data. In the present study, there was a significant statistically increasing (positive) trend in mean seasonal and annual rainfall, maximum temperature, minimum temperature, and runoff. However, there was a significant statistically decreasing (negative) trend in average annual relative humidity, solar radiation, and potential evapotranspiration. The magnitudes of the trends were 19.39mm/year, 0.0314oC/year, 0.013oC/year, -0.104%/year, -8.78MJ/m2/year, -1.440mm/year, and 0.028m3/s/year for annual rainfall, maximum temperature, minimum temperature, relative humidity, solar radiation, PET, and runoff, respectively. The rising trends in precipitation, temperature, and runoff in this research area show that this region is subject to climatic variability. The results of the Mann-Kendall and Sen's slope estimator statistical tests revealed the consistency of performance in the detection of the trend for the hydro-meteorological variables.
Towards Trust of Explainable AI in Thyroid Nodule Diagnosis

The ability to explain the prediction of deep learning models to end-users is an important feature to leverage the power of artificial intelligence (AI) for the medical decision-making process, which is usually considered non-transparent and challenging to comprehend. In this paper, we apply state-of-the-art eXplainable artificial intelligence (XAI) methods to explain the prediction of the black-box AI models in the thyroid nodule diagnosis application. We propose new statistic-based XAI methods, namely Kernel Density Estimation and Density map, to explain the case of no nodule detected. XAI methods’ performances are considered under a qualitative and quantitative comparison as feedback to improve the data quality and the model performance. Finally, we survey to assess doctors’ and patients’ trust in XAI explanations of the model’s decisions on thyroid nodule images.
Hide and Seek—Trace Element Incorporation and Diffusion in Olivine

Olivine, once overlooked as a host of trace elements, is becoming increasingly important for our understanding of the kinetic and equilibrium behaviour of these elements. Much of our understanding of trace element substitution and diffusion in geological materials comes as a result of experimental and petrological studies of olivine. Here, we consider trace element concentrations and incorporation mechanisms, and how these relate to diffusive behaviour. If we understand trace element behaviour in olivine, we have a powerful tool kit that can be directly applied to address many problems in petrology and volcanology. Perhaps more importantly, what we have learned from olivine can be applied to other minerals and aid us in addressing other far-reaching questions from across the Earth sciences.
PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain?

Contrastive Language–Image Pre-training (CLIP) has shown remarkable success in learning with cross-modal supervision from extensive amounts of image–text pairs collected online. Thus far, the effectiveness of CLIP has been investigated primarily in general-domain multimodal problems. In this work, we evaluate the effectiveness of CLIP for the task of Medical Visual Question Answering (MedVQA). We present PubMedCLIP, a fine-tuned version of CLIP for the medical domain based on PubMed articles. Our experiments conducted on two MedVQA benchmark datasets illustrate that PubMedCLIP achieves superior results improving the overall accuracy up to 3% in comparison to the state-of-the-art Model-Agnostic Meta-Learning (MAML) networks pre-trained only on visual data. The PubMedCLIP model with different back-ends, the source code for pre-training them and reproducing our MedVQA pipeline is publicly available at https://github.com/sarahESL/PubMedCLIP.
GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models

Providing natural language instructions in prompts is a useful new paradigm for improving task performance of large language models in a zero-shot setting. Recent work has aimed to improve such prompts via manual rewriting or gradient-based tuning. However, manual rewriting is time-consuming and requires subjective interpretation, while gradient-based tuning can be extremely computationally demanding for large models and may not be feasible for API-based models. In this work, we introduce Gradient-free Instructional Prompt Search (GrIPS), a gradient-free, edit-based search approach for improving task instructions for large language models. GrIPS takes in instructions designed for humans and automatically returns an improved, edited prompt, while allowing for API-based tuning. With InstructGPT models, GrIPS improves the average task performance by up to 4.30 percentage points on eight classification tasks from the Natural Instructions dataset (with similar improvements for OPT, BLOOM, and FLAN-T5). We see improvements for both instruction-only prompts and instruction + k-shot examples prompts. Notably, GrIPS outperforms manual rewriting and purely example-based prompts while controlling for the available compute and data budget. Further, performance of GrIPS is comparable to select gradient-based tuning approaches. Qualitatively, we show our edits can simplify instructions and at times make them incoherent but nonetheless improve accuracy.
A Discerning Several Thousand Judgments: GPT-3 Rates the Article + Adjective + Numeral + Noun Construction

Knowledge of syntax includes knowledge of rare, idiosyncratic constructions. LLMs must overcome frequency biases in order to master such constructions. In this study, I prompt GPT-3 to give acceptability judgments on the English-language Article + Adjective + Numeral + Noun construction (e.g., “a lovely five days”). I validate the prompt using the CoLA corpus of acceptability judgments and then zero in on the AANN construction. I compare GPT- 3’s judgments to crowdsourced human judgments on a subset of sentences. GPT-3’s judgments are broadly similar to human judgments and generally align with proposed constraints in the literature but, in some cases, GPT-3’s judgments and human judgments diverge from the literature and from each other.
XR and the Metaverse: New Opportunities in Education

This chapter highlights the importance of XR and Metaverse in the educational sector. The theoretical background and real-life cases in the educational sector indicate that XR and metaverse can give students an experience closest to experiential learning without leaving the classroom. It also reveals that both metaverse and XR deliver an immersive experience that can create meaningful situations relevant to learners and their lives.
Research on Path Planning and Path Tracking Control of Autonomous Vehicles Based on Improved APF and SMC

Path planning and tracking control is an essential part of autonomous vehicle research. In terms of path planning, the artificial potential field (APF) algorithm has attracted much attention due to its completeness. However, it has many limitations, such as local minima, unreachable targets, and inadequate safety. This study proposes an improved APF algorithm that addresses these issues. Firstly, a repulsion field action area is designed to consider the velocity of the nearest obstacle. Secondly, a road repulsion field is introduced to ensure the safety of the vehicle while driving. Thirdly, the distance factor between the target point and the virtual sub-target point is established to facilitate smooth driving and parking. Fourthly, a velocity repulsion field is created to avoid collisions. Finally, these repulsive fields are merged to derive a new formula, which facilitates the planning of a route that aligns with the structured road. After path planning, a cubic B-spline path optimization method is proposed to optimize the path obtained using the improved APF algorithm. In terms of path tracking, an improved sliding mode controller is designed. This controller integrates lateral and heading errors, improves the sliding mode function, and enhances the accuracy of path tracking. The MATLAB platform is used to verify the effectiveness of the improved APF algorithm. The results demonstrate that it effectively plans a path that considers car kinematics, resulting in smaller and more continuous heading angles and curvatures compared with general APF planning. In a tracking control experiment conducted on the Carsim–Simulink platform, the lateral error of the vehicle is controlled within 0.06 m at both high and low speeds, and the yaw angle error is controlled within 0.3 rad. These results validate the traceability of the improved APF method proposed in this study and the high tracking accuracy of the controller.
Rosetta: Enabling Robust TLS Encrypted Traffic Classification in Diverse Network Environments with TCP-Aware Traffic Augmentation

As the majority of Internet traffic is encrypted by the Transport Layer Security (TLS) protocol, recent advances leverage Deep Learning (DL) models to conduct encrypted traffic classification. We propose Rosetta to enable robust TLS encrypted traffic classification for existing DL models. It leverages TCP-aware traffic augmentation mechanisms and self-supervised learning to understand implicit TCP semantics, and hence extracts robust features of TLS flows. Extensive experiments show that Rosetta can significantly improve the classification performance of existing DL models on TLS traffic in diverse network environments.
Internet of Things Performance Evaluation from Three Decades in Education

Objective: This research tends to find the complete opportunity, novelties, and state-of-the-art for further researchers to make an easier way to conduct research in IoT, especially in educational fields worldwide.&#x0D; &#x0D; Method: This research is bibliometric and employs qualitative analysis. Based on the review and analysis of the IoT in the educational field, it is concluded that in the range year of three-decade IoT publications.&#x0D; &#x0D; Results: The exciting results are that the publication trends tend to increase yearly. Future research can use IoT throughout every educational institution and analyze the impact. In the fewer keywords, the research opportunities are the development of learning techniques and the emergence of the IoT as a learning system. Other keywords are used to find novelties and recommend the implementation, development, upgrade, installation, or further research. Researchers may identify the critical study paths of researchers in each set era, the issues most important to Scopus' IoT, and the researchers responsible for the most significant influence. As a result, it also aids in narrowing down the future trends that may be established in these domains of study.&#x0D; &#x0D; Conclusion: The research opportunities are developing learning techniques and the emergence of the IoT as a learning system. Other keywords are used to find novelties and recommend the implementation, development, upgrade, installation, or further research.
Linear programming based computational technique for leukemia classification using gene expression profile

Cancer is a serious public health concern worldwide and is the leading cause of death. Blood cancer is one of the most dangerous types of cancer. Leukemia is a type of cancer that affects the blood cell and bone marrow. Acute leukemia is a chronic condition that is fatal if left untreated. A timely, reliable, and accurate diagnosis of leukemia at an early stage is critical to treating and preserving patients' lives. There are four types of leukemia, namely acute lymphocytic leukemia, acute myelogenous leukemia, chronic lymphocytic in extracting, and chronic myelogenous leukemia. Recognizing these cancerous development cells is often done via manual analysis of microscopic images. This requires an extraordinarily skilled pathologist. Leukemia symptoms might include lethargy, a lack of energy, a pale complexion, recurrent infections, and easy bleeding or bruising. One of the challenges in this area is identifying subtypes of leukemia for specialized treatment. This Study is carried out to increase the precision of diagnosis to assist in the development of personalized plans for treatment, and improve general leukemia-related healthcare practises. In this research, we used leukemia gene expression data from Curated Microarray Database (CuMiDa). Microarrays are ideal for studying cancer, however, categorizing the expression pattern of microarray information can be challenging. This proposed study uses feature selection methods and machine learning techniques to predict and classify subtypes of leukemia in gene expression data CuMiDa (GSE9476). This research work utilized linear programming (LP) as a machine-learning technique for classification. Linear programming model classifies and predicts the subtypes of leukemia Bone_Marrow_CD34, Bone Marrow, AML, PB, and PBSC CD34. Before using the LP model, we selected 25 features from the given dataset of 22283 features. These 25 significant features were the most distinguishing for classification. The classification accuracy of this work is 98.44%.
Finite-Size Security for Discrete-Modulated Continuous-Variable Quantum Key Distribution Protocols

Discrete-modulated (DM) continuous-variable quantum key distribution (CV-QKD) protocols are promising candidates for commercial implementations of quantum communication networks due to their experimental simplicity. While tight security analyses in the asymptotic limit exist, proofs in the finite-size regime are still subject to active research. We present a composable finite-size security proof against independently and identically distributed collective attacks for a general DM CV-QKD protocol. We introduce a new energy testing theorem to bound the effective dimension of Bob's system and rigorously prove security within Renner's ϵ-security framework and address the issue of acceptance sets in protocols and their security proof. We want to highlight that our method also allows for nonunique acceptance statistics, which is necessary in practise. Finally, we extend and apply a numerical security proof technique to calculate tight lower bounds on the secure key rate. To demonstrate our method, we apply it to a quadrature phase-shift keying protocol for both untrusted, ideal and trusted, nonideal detectors. The results show that our security proof method yields secure finite-size key rates under experimentally viable conditions up to at least 72 km transmission distance.1 MoreReceived 23 January 2023Revised 10 August 2023Accepted 8 September 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040306Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum communication, protocols & technologyQuantum information theoryQuantum Information, Science & Technology