Reviewerly/exHarmony huggingface dataset:
Incremental Data-Uploading for Full-Quantum Classification

The data representation in a machine-learning model strongly influences its performance. This becomes even more important for quantum machine learning models implemented on noisy intermediate scale quantum (NISQ) devices. Encoding high dimensional data into a quantum circuit for a NISQ device without any loss of information is not trivial and brings a lot of challenges. While simple encoding schemes (like single qubit rotational gates to encode high dimensional data) often lead to information loss within the circuit, complex encoding schemes with entanglement and data re-uploading lead to an increase in the encoding gate count. This is not well-suited for NISQ devices. This work proposes 'incremental data-uploading', a novel encoding pattern for high dimensional data that tackles these challenges. We spread the encoding gates for the feature vector of a given data point throughout the quantum circuit with parameterized gates in between them. This encoding pattern results in a better representation of data in the quantum circuit with a minimal pre-processing requirement. We show the efficiency of our encoding pattern on a classification task using the MNIST and Fashion-MNIST datasets, and compare different encoding methods via classification accuracy and the effective dimension of the model.
A Fair Federated Learning Framework With Reinforcement Learning

Federated learning (FL) is a paradigm where many clients collaboratively train a model under the coordination of a central server, while keeping the training data locally stored. However, heterogeneous data distributions over different clients remain a challenge to mainstream FL algorithms, which may cause slow convergence, overall performance degradation and unfairness of performance across clients. To address these problems, in this study we propose a reinforcement learning framework, called PG-FFL, which automatically learns a policy to assign aggregation weights to clients. Additionally, we propose to utilize Gini coefficient as the measure of fairness for FL. More importantly, we apply the Gini coefficient and validation accuracy of clients in each communication round to construct a reward function for the reinforcement learning. Our PG-FFL is also compatible to many existing FL algorithms. We conduct extensive experiments over diverse datasets to verify the effectiveness of our framework. The experimental results show that our framework can outperform baseline methods in terms of overall performance, fairness and convergence speed.
RESEARCH PROGRESS OF DEEP LEARNING IN LOW-DOSE CT IMAGE DENOISING

Low-dose computed tomography (CT) will increase noise and artefacts while reducing the radiation dose, which will adversely affect the diagnosis of radiologists. Low-dose CT image denoising is a challenging task. There are essential differences between the traditional methods and the deep learning-based methods. This paper discusses the denoising approaches of low-dose CT image via deep learning. Deep learning-based methods have achieved relatively ideal denoising effects in both subjective visual quality and quantitative objective metrics. This paper focuses on three state-of-the-art deep learning-based image denoising methods, in addition, four traditional methods are used as the control group to compare the denoising effect. Comprehensive experiments show that the deep learning-based methods are superior to the traditional methods in low-dose CT images denoising.
Experimental demonstration of separating the wave‒particle duality of a single photon with the quantum Cheshire cat

As a fundamental characteristic of physical entities, wave‒particle duality describes whether a microscopic entity exhibits wave or particle attributes depending on the specific experimental setup. This assumption is premised on the notion that physical properties are inseparable from the objective carrier. However, after the concept of the quantum Cheshire cats was proposed, which makes the separation of physical attributes from the entity possible, the premise no longer holds. Furthermore, an experimental demonstration of the separation of the wave and particle attributes inspired by this scenario remains scarce. In this work, we experimentally separated the wave and particle attributes of a single photon by exploiting the quantum Cheshire cat concept for the first time. By applying a weak disturbance to the evolution of the system, we achieve an effect similar to the quantum Cheshire cat and demonstrated the separation of the wave and particle attributes via the extraction of weak values. Our work provides a new perspective for the in-depth understanding of wave‒particle duality and promotes the application of weak measurements in fundamentals of quantum mechanics.
Investigating risk assessment in post-pandemic household cryptocurrency investments: an explainable machine learning approach

This study provides an applicable methodological approach applying artificial intelligence (AI)-based supervised machine learning (ML) algorithms in risk assessment of post-pandemic household cryptocurrency investments and identifies the best performed ML algorithm and the most important risk assessment determinants. The empirical findings from analyzing 13 determinants from 1,000 dataset collected from major cryptocurrency communities online suggest that the logistic regression (LR) algorithm outperforms the remaining six ML algorithms by using performance metrics, lift chart, and ROC chart. Moreover, to make the ML algorithm results explainable and tackle the “black box” issue, the top five most important determinants are discovered, which are the interaction between investment amount and investment duration, investment amount, perception of traditional investments, cryptocurrency literacy, and perception of cryptocurrency volatility. The present study contributes to the literature on risk assessment, especially on the household cryptocurrency investments in the post-pandemic era and the body of knowledge on explainable supervised ML algorithms.
Image Characterization and Modeling System (ICAMS): A Geographic Information System for the Characterization and Modeling of Multiscale Remote Sensing Data

In this chapter, the authors describe the design and implementation of a Geographic Information System (GIS) software module developed to measure, characterize, and model multiscale remotely sensed data. Integral to the GIS software package is the capability for selecting and applying several different spatial analysis techniques for use in analyzing multiscaled remote sensing data. In addition to the fractal analysis, another important attribute of Image Characterization and Modeling System (ICAMS) is the development and implementation of specialized functions for image characterization that are not easily available in any existing GIS/remote sensing software, such as variogram analysis, multiscale analysis, and boundary delineation. ICAMS has three specialized image characterization functions: the calculation of the NDVI and temperature; the delineation of land/water interface and vegetated/non-vegetated boundaries; and the generation of multiscale, multiresolution data through an aggregation routine.
MSRL-Net: A multi-level semantic relation-enhanced learning network for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to analyze the sentiment polarity of a given text towards several specific aspects. For implementing the ABSA, one way is to convert the original problem into a sentence semantic matching task, using pre-trained language models, such as BERT. However, for such a task, the intra- and inter-semantic relations among input sentence pairs are often not considered. Specifically, the semantic information and guidance of relations revealed in the labels, such as positive, negative and neutral, have not been completely exploited. To address this issue, we introduce a self-supervised sentence pair relation classification task and propose a model named Multi-level Semantic Relation-enhanced Learning Network (MSRL-Net) for ABSA. In MSRL-Net, after recasting the original ABSA task as a sentence semantic matching task, word dependency relations and word-sentence relations are utilized to enhance the word-level semantic representation for the sentence semantic matching task, while sentence semantic relations and sentence pairs relations are utilized to enhance the sentence-level semantic representation for sentence pair relation classification. Empirical experiments on SemEval 2014 Task 4, SemEval 2016 Task 5 and SentiHood show that MSRL-Net significantly outperforms other baselines such as BERT in terms of accuracy, Macro-F1 and AUC.
Enhancing the reliability of image classification using the intrinsic features

Usually, the features extracted by deep learning models include both the intrinsic features and the related features, where the features from the category-related object itself are called the intrinsic features and the features from the background are called the related features. We argue that the classification relying on the intrinsic features is reliable, so the reliability of classification can be defined as the dependency on the intrinsic features. However, utilizing the intrinsic features in image classification has not been extensively studied. One reason is that the widely used classification accuracy cannot directly represent the reliability. Thus, we propose a novel metric called Reliable Classification Ratio (RCR) to estimate the reliability of the classification results, and as a complementary metric to the classification accuracy. Utilizing RCR and the accuracy, it is found that the classification results of the deep learning models heavily rely on the related features, which demonstrates that the results are unreliable. To deal with the problem, we propose a contrastive training method that regards the background as an extra category. The image that includes the category-related object and the image that excludes the category-related object are both used for feature learning, which guides deep learning models to make better use of the intrinsic features in the classification. Experiments prove that the proposed method significantly improves the reliability while maintaining classification accuracy.
PharmKE: Knowledge Extraction Platform for Pharmaceutical Texts Using Transfer Learning

Even though named entity recognition (NER) has seen tremendous development in recent years, some domain-specific use-cases still require tagging of unique entities, which is not well handled by pre-trained models. Solutions based on enhancing pre-trained models or creating new ones are efficient, but creating reliable labeled training for them to learn on is still challenging. In this paper, we introduce PharmKE, a text analysis platform tailored to the pharmaceutical industry that uses deep learning at several stages to perform an in-depth semantic analysis of relevant publications. The proposed methodology is used to produce reliably labeled datasets leveraging cutting-edge transfer learning, which are later used to train models for specific entity labeling tasks. By building models for the well-known text-processing libraries spaCy and AllenNLP, this technique is used to find Pharmaceutical Organizations and Drugs in texts from the pharmaceutical domain. The PharmKE platform also incorporates the NER findings to resolve co-references of entities and examine the semantic linkages in each phrase, creating a foundation for further text analysis tasks, such as fact extraction and question answering. Additionally, the knowledge graph created by DBpedia Spotlight for a specific pharmaceutical text is expanded using the identified entities. The obtained results with the proposed methodology result in about a 96% F1-score on the NER tasks, which is up to 2% better than those of the fine-tuned BERT and BioBERT models developed using the same dataset. The ultimate benefits of the platform are that pharmaceutical domain specialists may more easily identify the knowledge extracted from the input texts thanks to the platform’s visualization of the model findings. Likewise, the proposed techniques can be integrated into mobile and pervasive systems to give patients more relevant and comprehensive information from scanned medication guides. Similarly, it can provide preliminary insights to patients and even medical personnel on whether a drug from a different vendor is compatible with the patient’s prescription medication.
ChannelFed: Enabling Personalized Federated Learning via Localized Channel Attention

One vital challenge in federated learning (FL) is the statistical heterogeneity of data in different clients, which negatively affects the performance of the finally obtained model. One common approach to address this problem, called as personalized federated learning (PFL), is to train a personalized model for each client. A key design issue in PFL-based methods is determining which parts of the model should be personalized for each client. For example, one popular method in PFL is to personalize the batch normalization layers. In this paper, we propose ChannelFed, a new PFL-based method which personalizes the channel attention module. ChannelFed is designed based on the following observation: Channel attention assigns different weights to channels for different classes of data, which can be utilized to exploit knowledge of heterogeneous data from different clients. By keeping the channel attention module localized, ChannelFed enables clients to concentrate on client-specific channels. ChannelFed implements normalization across samples in the channel attention module to better fit for statistical heterogeneity scenarios. Experiments on CIFAR-10, Fashion-MNIST, and CIFAR-100 datasets demonstrate that ChannelFed outperforms other PFL methods under statistical heterogeneity scenarios.
The Scientific Investigation of Unidentified Aerial Phenomena (UAP) Using Multimodal Ground-Based Observatories

(Abridged) Unidentified Aerial Phenomena (UAP) have resisted explanation and have received little formal scientific attention for 75 years. A primary objective of the Galileo Project is to build an integrated software and instrumentation system designed to conduct a multimodal census of aerial phenomena and to recognize anomalies. Here we present key motivations for the study of UAP and address historical objections to this research. We describe an approach for highlighting outlier events in the high-dimensional parameter space of our census measurements. We provide a detailed roadmap for deciding measurement requirements, as well as a science traceability matrix (STM) for connecting sought-after physical parameters to observables and instrument requirements. We also discuss potential strategies for deciding where to locate instruments for development, testing, and final deployment. Our instrument package is multimodal and multispectral, consisting of (1) wide-field cameras in multiple bands for targeting and tracking of aerial objects and deriving their positions and kinematics using triangulation; (2) narrow-field instruments including cameras for characterizing morphology, spectra, polarimetry, and photometry; (3) passive multistatic arrays of antennas and receivers for radar-derived range and kinematics; (4) radio spectrum analyzers to measure radio and microwave emissions; (5) microphones for sampling acoustic emissions in the infrasonic through ultrasonic frequency bands; and (6) environmental sensors for characterizing ambient conditions (temperature, pressure, humidity, and wind velocity), as well as quasistatic electric and magnetic fields, and energetic particles. The use of multispectral instruments and multiple sensor modalities will help to ensure that artifacts are recognized and that true detections are corroborated and verifiable.
An Efficient Multi-Objective Evolutionary Zero-Shot Neural Architecture Search Framework for Image Classification

Neural Architecture Search (NAS) has recently shown a powerful ability to engineer networks automatically on various tasks. Most current approaches navigate the search direction with the validation performance-based architecture evaluation methodology, which estimates an architecture’s quality by training and validating on a specific large dataset. However, for small-scale datasets, the model’s performance on the validation set cannot precisely estimate that on the test set. The imprecise architecture evaluation can mislead the search to sub-optima. To address the above problem, we propose an efficient multi-objective evolutionary zero-shot NAS framework by evaluating architectures with zero-cost metrics, which can be calculated with randomly initialized models in a training-free manner. Specifically, a general zero-cost metric design principle is proposed to unify the current metrics and help develop several new metrics. Then, we offer an efficient computational method for multi-zero-cost metrics by calculating them in one forward and backward pass. Finally, comprehensive experiments have been conducted on NAS-Bench-201 and MedMNIST. The results have shown that the proposed method can achieve sufficiently accurate, high-throughput performance on MedMNIST and 20[Formula: see text]faster than the previous best method.
Drone Detection using YOLO and SSD A Comparative Study

Unmanned Ariel Vehicles (UAVs) are one of the fastest-growing technologies with a wide range of uses. UAVs have become more accessible to the masses over the recent years. However, both negligent and malevolent usage of UAVs can pose a significant risk. Drone technology today poses a catastrophic threat to the governments, companies, and the general public. Drones have been used for espionage and military strikes in the past, and as the market for UAVs grows, more of these attacks can be expected in the future. Combat drones or UCAVs have changed how dangerous operations were earlier carried out. They can also be used to remotely launch targeted attacks or transport heavy weaponry. For this reason, it is essential for an organization to adopt automated drone detection and tracking mechanisms. Ultra-high-definition radar, microphones, cameras, and radio frequency (RF) sensing are among the technologies being considered and used for UAVs. Such detecting methods, on the other hand, entail substantial maintenance and configuration expenses. Because they aren't portable, they can't be implemented on a broad scale. The advancements in GPU technology have given a huge boost to deep learning; a field which wasn't considered to be feasible due to its high computing power requirements. Deep Learning has given rise to many new areas of research; especially Computer Vision (CV).
Efficient On-Device Session-Based Recommendation

On-device session-based recommendation systems have been achieving increasing attention on account of the low energy/resource consumption and privacy protection while providing promising recommendation performance. To fit the powerful neural session-based recommendation models in resource-constrained mobile devices, tensor-train decomposition and its variants have been widely applied to reduce memory footprint by decomposing the embedding table into smaller tensors, showing great potential in compressing recommendation models. However, these model compression techniques significantly increase the local inference time due to the complex process of generating index lists and a series of tensor multiplications to form item embeddings. The resultant on-device recommender fails to provide real-time responses and recommendations. To improve the online recommendation efficiency, we propose to learn compositional encoding-based compact item representations. Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item. Then the composition of the codeword embedding vectors from different embedding matrices (i.e., codebooks) forms the item embedding. Since the size of codebooks can be extremely small, the recommender model is thus able to fit in resource-constrained devices and save the codebooks for fast local inference. Besides, to prevent the loss of model capacity caused by compression, we propose a bidirectional self-supervised knowledge distillation framework. Extensive experimental results on two benchmark datasets demonstrate that compared with existing methods, the proposed on-device recommender not only achieves an 8x inference speedup with a large compression ratio but also shows superior recommendation performance. The code is released at https://github.com/xiaxin1998/EODRec.
Automatic Design of Metaheuristics for Practical Engineering Applications

It is common to find multiple metaheuristics to solve continuous optimization problems. However, choosing what optimizer may obtain the best results for a given task requires exhaustive evaluations that are highly application-dependent. Besides, it is necessary to find sufficiently good tuning parameters to achieve satisfactory performance with the selected approach. In this context, the automatic design of algorithms, particularly those based on heuristics, has been increasing in popularity in the previous years due to its undoubted relevance nowadays. This paper explores a novel approach based on hyper-heuristics to carefully select population-based search operators and their tuning parameters to generate metaheuristics capable of dealing with a given practical engineering problem. The proposed strategy is assessed using three highly relevant and illustrative problems: training Artificial Neural Networks, designing PID controllers, and modeling a calorimetric phenomenon based on fractional calculus. In addition, we implement three well-known optimization metaheuristics to compare achieved solutions via the proposed hyper-heuristic strategy, namely Particle Swarm Optimization, Genetic Algorithm, and Cuckoo Search. Results from extensive numerical tests prove that the customized metaheuristics are generally superior to the three well-known algorithms, taking only a few iterations to converge to an optimal solution. This is an excellent indicator of alleviating the effort and expertise required to choose the proper methodology when dealing with real-valued optimization problems.
ENDE-GNN: An Encoder-decoder GNN Framework for Sketch Semantic Segmentation

Sketch semantic segmentation serves as an important part of sketch interpretation. Recently, some researchers have obtained significant results using graph neural networks (GNN) for this task. However, existing GNN-based methods usually neglect the drawing order of sketches thus missing out the sequence information inherent to sketches. Towards solving this problem to achieve better performance on sketch semantic segmentation, we propose an encoder-decoder GNN framework named ENDE-GNN. Working with an auxiliary decoder, our ENDE-GNN guides the GNN backbone network to not only extract the inter-stroke and intra-stroke features, but also pays attention to the drawing order of sketches. This decoder acts during training only, preventing any additional overhead during testing. The proposed ENDE-GNN obtains state-of-the-art per-formances on three public sketch semantic segmentation datasets, namely SPG, SketchSeg-150K, and CreativeSketch. We further evaluate the effectiveness of ENDE-GNN via ablation studies and visualizations. Codes are available at https://github.com/PRIS-CV/ENDE_For_SSS.