Reviewerly/exHarmony huggingface dataset:
Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele-Health after COVID-19

Telemedicine can be used by medical practitioners to connect with their patients during the recent Coronavirus outbreak, whilst attempting to reduce COVID-19 transmission among patients and clinicians.Amidst the pandemic, Telemedicine has the potential to help by permitting patients to receive supportive care without having to physically visit a hospital by using a conversational artificial intelligence-based application for their treatment.Thus, telehealth will rapidly and radically transform in-person care to remote consultation of patients.Because of this, it developed a Multilingual Conversational Bot based on Natural Language Processing (NLP) to provide free primary healthcare education, information, advice to chronic patients.The study introduces a novel computer application acting as a personal virtual doctor that has been opportunely designed and extensively trained to interact with patients like human beings.This application is based upon a serverless architecture and it aggregates the services of a doctor by providing preventive measures, home remedies, interactive counseling sessions, healthcare tips, and symptoms covering the most prevalent diseases in rural India.The paper proposes a conversational bot "Aapka Chikitsak" on Google Cloud Platform (GCP) for delivering telehealth in India to increase the patient's access to healthcare knowledge and leve rage the potentials of artificial intelligence to bridge the gap of demand and supply of human healthcare providers.This conversational application has resulted in reducing the barriers for access to healthcare facilities and procures intelligent consultations remotely to allow timely care and quality treatment, thereby effectively assisting the society.
QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection

While general object detection with deep learning has achieved great success in the past few years, the performance and efficiency of detecting small objects are far from satisfactory. The most common and effective way to promote small object detection is to use high-resolution images or feature maps. However, both approaches induce costly computation since the computational cost grows squarely as the size of images and features increases. To get the best of two worlds, we propose QueryDet that uses a novel query mechanism to accelerate the inference speed of feature-pyramid based object detectors. The pipeline composes two steps: it first predicts the coarse locations of small objects on low-resolution features and then computes the accurate detection results using high-resolution features sparsely guided by those coarse positions. In this way, we can not only harvest the benefit of high-resolution feature maps but also avoid useless computation for the background area. On the popular COCO dataset, the proposed method improves the detection mAP by 1.0 and mAP-small by 2.0, and the high-resolution inference speed is improved to 3.0× on average. On VisDrone dataset, which contains more small objects, we create a new state-of-the-art while gaining a 2.3× high-resolution acceleration on average. Code is available at https://github.com/ChenhongyiYang/QueryDet=PyTorch.
<scp>ChatGPT</scp> and consumers: Benefits, Pitfalls and Future Research Agenda

Abstract The need of the hour is to encourage research on topics with newness and novelty. In this context, this article discusses multidimensional benefits and potential pitfalls of using artificial intelligence‐based Chat Generative Pre‐trained Transformer (ChatGPT), and provides numerous ideas for future research in consumer studies and marketing in the context of ChatGPT. ChatGPT provides algorithm‐generated conversational responses to text‐based prompts. Since its launch in the late 2022, ChatGPT has generated significant debate surrounding its hallmarks, benefits and potential pitfalls. On the one hand, ChatGPT can offer enhanced consumer engagement, improved customer service, personalization and shopping, social interaction and communication practice, cost‐effectiveness, insights into consumer behaviour and improved marketing campaigns. On the other hand, potential pitfalls include concerns about consumer well‐being, bias, misinformation, lack of context, privacy concerns, ethical considerations and security. The article concludes by outlining a potential future research agenda in the area of ChatGPT and consumer studies. Overall, this article provides valuable insights into the benefits and challenges associated with ChatGPT, shedding light on its potential applications and the need for further research.
A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development

ChatGPT, an artificial intelligence generated content (AIGC) model developed by OpenAI, has attracted world-wide attention for its capability of dealing with challenging language understanding and generation tasks in the form of conversations. This paper briefly provides an overview on the history, status quo and potential future development of ChatGPT, helping to provide an entry point to think about ChatGPT. Specifically, from the limited open-accessed resources, we conclude the core techniques of ChatGPT, mainly including large-scale language models, in-context learning, reinforcement learning from human feedback and the key technical steps for developing Chat-GPT. We further analyze the pros and cons of ChatGPT and we rethink the duality of ChatGPT in various fields. Although it has been widely acknowledged that ChatGPT brings plenty of opportunities for various fields, mankind should still treat and use ChatGPT properly to avoid the potential threat, e.g., academic integrity and safety challenge. Finally, we discuss several open problems as the potential development of ChatGPT.
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

Few-shot object detection has made substantial progress by representing novel class objects using the feature representation learned upon a set of base class objects. However, an implicit contradiction between novel class classification and representation is unfortunately ignored. On the one hand, to achieve accurate novel class classification, the distributions of either two base classes must be far away from each other (max-margin). On the other hand, to precisely represent novel classes, the distributions of base classes should be close to each other to reduce the intra-class distance of novel classes (min-margin). In this paper, we propose a class margin equilibrium (CME) approach, with the aim to optimize both feature space partition and novel class reconstruction in a systematic way. CME first converts the few-shot detection problem to the few-shot classification problem by using a fully connected layer to decouple localization features. CME then reserves adequate margin space for novel classes by introducing simple-yet-effective class margin loss during feature learning. Finally, CME pursues margin equilibrium by disturbing the features of novel class instances in an adversarial min-max fashion. Experiments on Pascal VOC and MS-COCO datasets show that CME significantly improves upon two baseline detectors (up to 3 ~ 5% in average), achieving state-of-the-art performance. Code is available at https://github.com/BohaoLee/CME.
Fire Hawk Optimizer: a novel metaheuristic algorithm

Abstract This study proposes the Fire Hawk Optimizer (FHO) as a novel metaheuristic algorithm based on the foraging behavior of whistling kites, black kites and brown falcons. These birds are termed Fire Hawks considering the specific actions they perform to catch prey in nature, specifically by means of setting fire. Utilizing the proposed algorithm, a numerical investigation was conducted on 233 mathematical test functions with dimensions of 2–100, and 150,000 function evaluations were performed for optimization purposes. For comparison, a total of ten different classical and new metaheuristic algorithms were utilized as alternative approaches. The statistical measurements include the best, mean, median, and standard deviation of 100 independent optimization runs, while well-known statistical analyses, such as Kolmogorov–Smirnov, Wilcoxon, Mann–Whitney, Kruskal–Wallis, and Post-Hoc analysis, were also conducted. The obtained results prove that the FHO algorithm exhibits better performance than the compared algorithms from literature. In addition, two of the latest Competitions on Evolutionary Computation (CEC), such as CEC 2020 on bound constraint problems and CEC 2020 on real-world optimization problems including the well-known mechanical engineering design problems, were considered for performance evaluation of the FHO algorithm, which further demonstrated the superior capability of the optimizer over other metaheuristic algorithms in literature. The capability of the FHO is also evaluated in dealing with two of the real-size structural frames with 15 and 24 stories in which the new method outperforms the previously developed metaheuristics.
Challenges and opportunities in quantum machine learning

At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics. Nevertheless, challenges remain regarding the trainability of QML models. Here we review current methods and applications for QML. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with QML.
MOTR: End-to-End Multiple-Object Tracking with Transformer

Temporal modeling of objects is a key challenge in multiple-object tracking (MOT). Existing methods track by associating detections through motion-based and appearance-based similarity heuristics. The post-processing nature of association prevents end-to-end exploitation of temporal variations in video sequence. In this paper, we propose MOTR, which extends DETR [6] and introduces “track query” to model the tracked instances in the entire video. Track query is transferred and updated frame-by-frame to perform iterative prediction over time. We propose tracklet-aware label assignment to train track queries and newborn object queries. We further propose temporal aggregation network and collective average loss to enhance temporal relation modeling. Experimental results on DanceTrack show that MOTR significantly outperforms state-of-the-art method, ByteTrack [42] by 6.5% on HOTA metric. On MOT17, MOTR outperforms our concurrent works, TrackFormer [18] and TransTrack [29], on association performance. MOTR can serve as a stronger baseline for future research on temporal modeling and Transformer-based trackers. Code is available at https://github.com/megvii-research/MOTR .
Improving Shadow Suppression for Illumination Robust Face Recognition

2D face analysis techniques, such as face landmarking, face recognition and face verification, are reasonably dependent on illumination conditions which are usually uncontrolled and unpredictable in the real world. The current massive data-driven approach, e.g., deep learning-based face recognition, requires a huge amount of labeled training face data that hardly cover the infinite lighting variations that can be encountered in real-life applications. An illumination robust preprocessing method thus remains a very interesting but also a significant challenge in reliable face analysis. In this paper we propose a novel model driven approach to improve lighting normalization of face images. Specifically, we propose to build the underlying reflectance model which characterizes interactions between skin surface, lighting source and camera sensor, and elaborate the formation of face color appearance. The proposed illumination processing pipeline enables generation of the Chromaticity Intrinsic Image (CII) in a log chromaticity space which is robust to illumination variations. Moreover, as an advantage over most prevailing methods, a photo-realistic color face image is subsequently reconstructed, which eliminates a wide variety of shadows whilst retaining the color information and identity details. Experimental results under different scenarios and using various face databases show the effectiveness of the proposed approach in dealing with lighting variations, including both soft and hard shadows, in face recognition.
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization

Modern deep neural networks are typically highly overparameterized. Pruning techniques are able to remove a significant fraction of network parameters with little loss in accuracy. Recently, techniques based on dynamic reallocation of non-zero parameters have emerged, allowing direct training of sparse networks without having to pre-train a large dense model. Here we present a novel dynamic sparse reparameterization method that addresses the limitations of previous techniques such as high computational cost and the need for manual configuration of the number of free parameters allocated to each layer. We evaluate the performance of dynamic reallocation methods in training deep convolutional networks and show that our method outperforms previous static and dynamic reparameterization methods, yielding the best accuracy for a fixed parameter budget, on par with accuracies obtained by iteratively pruning a pre-trained dense model. We further investigated the mechanisms underlying the superior generalization performance of the resultant sparse networks. We found that neither the structure, nor the initialization of the non-zero parameters were sufficient to explain the superior performance. Rather, effective learning crucially depended on the continuous exploration of the sparse network structure space during training. Our work suggests that exploring structural degrees of freedom during training is more effective than adding extra parameters to the network.
Overcoming quantum decoherence with plasmonics

The use of nanoscale plasmonic metamaterials can optimize photon-matter interactions
HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records

Deep learning methods especially recurrent neural network based models have demonstrated early success in disease risk prediction on longitudinal patient data. Existing works follow a strong assumption to implicitly assume the stationary disease progression during each time period, and thus, take a homogeneous way to decay the information from previous time steps for all patients. However,in reality, disease progression is non-stationary. Besides, the key time steps for a target disease vary among patients. To leverage time information for risk prediction in a more reasonable way, we propose a new hierarchical time-aware attention network, named HiTANet, which imitates the decision making process of doctors inrisk prediction. Particularly, HiTANet models time information in local and global stages. The local evaluation stage has a time aware Transformer that embeds time information into visit-level embed-ding and generates local attention weight for each visit. The global synthesis stage further adopts a time-aware key-query attention mechanism to assign global weights to different time steps. Finally, the two types of attention weights are dynamically combined to generate the patient representations for further risk prediction. We evaluate HiTANet on three real-world datasets. Compared with the best results among twelve competing baselines, HiTANet achieves over 7% in terms of F1 score on all datasets, which demonstrates the effectiveness of the proposed model and the necessity of modeling time information in risk prediction task.
Measurement of Text Similarity: A Survey

Text similarity measurement is the basis of natural language processing tasks, which play an important role in information retrieval, automatic question answering, machine translation, dialogue systems, and document matching. This paper systematically combs the research status of similarity measurement, analyzes the advantages and disadvantages of current methods, develops a more comprehensive classification description system of text similarity measurement algorithms, and summarizes the future development direction. With the aim of providing reference for related research and application, the text similarity measurement method is described by two aspects: text distance and text representation. The text distance can be divided into length distance, distribution distance, and semantic distance; text representation is divided into string-based, corpus-based, single-semantic text, multi-semantic text, and graph-structure-based representation. Finally, the development of text similarity is also summarized in the discussion section.
Region Graph Embedding Network for Zero-Shot Learning

Most of the existing Zero-Shot Learning (ZSL) approaches learn direct embeddings from global features or image parts (regions) to the semantic space, which, however, fail to capture the appearance relationships between different local regions within a single image. In this paper, to model the relations among local image regions, we incorporate the region-based relation reasoning into ZSL. Our method, termed as Region Graph Embedding Network (RGEN), is trained end-to-end from raw image data. Specifically, RGEN consists of two branches: the Constrained Part Attention (CPA) branch and the Parts Relation Reasoning (PRR) branch. CPA branch is built upon attention and produces the image regions. To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch. To train our model, we introduce both a transfer loss and a balance loss to contrast class similarities and pursue the maximum response consistency among seen and unseen outputs, respectively. Extensive experiments on four datasets well validate the effectiveness of the proposed method under both ZSL and generalized ZSL settings.
BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense arrangement, posing enormous challenges to detect ships quickly and accurately. To address these issues above, a novel YOLO-based arbitrary-oriented SAR ship detector using bi-directional feature fusion and angular classification (BiFA-YOLO) is proposed in this article. First of all, a novel bi-directional feature fusion module (Bi-DFFM) tailored to SAR ship detection is applied to the YOLO framework. This module can efficiently aggregate multi-scale features through bi-directional (top-down and bottom-up) information interaction, which is helpful for detecting multi-scale ships. Secondly, to effectively detect arbitrary-oriented and densely arranged ships in HR SAR images, we add an angular classification structure to the head network. This structure is conducive to accurately obtaining ships’ angle information without the problem of boundary discontinuity and complicated parameter regression. Meanwhile, in BiFA-YOLO, a random rotation mosaic data augmentation method is employed to suppress the impact of angle imbalance. Compared with other conventional data augmentation methods, the proposed method can better improve detection performance of arbitrary-oriented ships. Finally, we conduct extensive experiments on the SAR ship detection dataset (SSDD) and large-scene HR SAR images from GF-3 satellite to verify our method. The proposed method can reach the detection performance with precision = 94.85%, recall = 93.97%, average precision = 93.90%, and F1-score = 0.9441 on SSDD. The detection speed of our method is approximately 13.3 ms per 512 × 512 image. In addition, comparison experiments with other deep learning-based methods and verification experiments on large-scene HR SAR images demonstrate that our method shows strong robustness and adaptability.
Anomaly detection in time series

Detecting anomalous subsequences in time series data is an important task in areas ranging from manufacturing processes over finance applications to health care monitoring. An anomaly can indicate important events, such as production faults, delivery bottlenecks, system defects, or heart flicker, and is therefore of central interest. Because time series are often large and exhibit complex patterns, data scientists have developed various specialized algorithms for the automatic detection of such anomalous patterns. The number and variety of anomaly detection algorithms has grown significantly in the past and, because many of these solutions have been developed independently and by different research communities, there is no comprehensive study that systematically evaluates and compares the different approaches. For this reason, choosing the best detection technique for a given anomaly detection task is a difficult challenge. This comprehensive, scientific study carefully evaluates most state-of-the-art anomaly detection algorithms. We collected and re-implemented 71 anomaly detection algorithms from different domains and evaluated them on 976 time series datasets. The algorithms have been selected from different algorithm families and detection approaches to represent the entire spectrum of anomaly detection techniques. In the paper, we provide a concise overview of the techniques and their commonalities; we evaluate their individual strengths and weaknesses and, thereby, consider factors, such as effectiveness, efficiency, and robustness. Our experimental results should ease the algorithm selection problem and open up new research directions.
Early Action Prediction by Soft Regression

We propose a novel approach for predicting on-going action with the assistance of a low-cost depth camera. Our approach introduces a soft regression-based early prediction framework. In this framework, we estimate soft labels for the subsequences at different progress levels, jointly learned with an action predictor. Our formulation of soft regression framework 1) overcomes a usual assumption in existing early action prediction systems that the progress level of on-going sequence is given in the testing stage; and 2) presents a theoretical framework to better resolve the ambiguity and uncertainty of subsequences at early performing stage. The proposed soft regression framework is further enhanced in order to take the relationships among subsequences and the discrepancy of soft labels over different classes into consideration, so that a Multiple Soft labels Recurrent Neural Network (MSRNN) is finally developed. For real-time performance, we also introduce a new RGB-D feature called "local accumulative frame feature (LAFF)", which can be computed efficiently by constructing an integral feature map. Our experiments on three RGB-D benchmark datasets and an unconstrained RGB action set demonstrate that the proposed regression-based early action prediction model outperforms existing models significantly and also show that the early action prediction on RGB-D sequence is more accurate than that on RGB channel.