Reviewerly/exHarmony huggingface dataset:
Rethinking the Misalignment Problem in Dense Object Detection

Object detection aims to localize and classify the objects in a given image, and these two tasks are sensitive to different object regions. Therefore, some locations predict high-quality bounding boxes but low classification scores, and some locations are quite the opposite. A misalignment exists between the two tasks, and their features are spatially entangled. In order to solve the misalignment problem, we propose a plug-in Spatial-disentangled and Task-aligned operator (SALT). By predicting two task-aware point sets that are located in each task’s sensitive regions, SALT can reassign features from those regions and align them to the corresponding anchor point. Therefore, features for the two tasks are spatially aligned and disentangled. To minimize the difference between the two regression stages, we propose a Self-distillation regression (SDR) loss that can transfer knowledge from the refined regression results to the coarse regression results. On the basis of SALT and SDR loss, we propose SALT-Net, which explicitly exploits task-aligned point-set features for accurate detection results. Extensive experiments on the MS-COCO dataset show that our proposed methods can consistently boost different state-of-the-art dense detectors by $$\sim $$ 2 AP. Notably, SALT-Net with Res2Net-101-DCN backbone achieves 53.8 AP on the MS-COCO test-dev.
The Auxiliary Role of Virtual Reality in Enhancing the Effects of Disaster News on Empathy and Fear: The Mediating Role of Presence

This study conducted a two-condition between-subjects laboratory experiment (N = 147) to examine the effects of textual disaster news combined with a 360° video in different modes (virtual reality [VR] vs. two dimensional screen) on cognitive/affective empathy and fear. Additionally, the mediating role of presence (social and spatial presence) was considered. Results indicated that the news article presented through an immersive VR mode directly led to greater levels of presence and cognitive empathy but not affective empathy and fear. The effects on social presence further resulted in greater cognitive and affective empathy, not fear. Meanwhile, spatial presence enhanced by VR led to greater levels of emotions (affective empathy and fear) but not cognitive empathy. These findings suggest the dynamic mechanisms of how an immersive VR modality can function as a complementary tool for traditional news to improve audience engagement, which centers on emotion-related outcomes in tandem with cognitive responses.
Efficient experience replay architecture for offline reinforcement learning

Purpose Offline reinforcement learning (RL) acquires effective policies by using prior collected large-scale data, while, in some scenarios, collecting data may be hard because it is time-consuming, expensive and dangerous, i.e. health care, autonomous driving, seeking a more efficient offline RL method. The purpose of the study is to introduce an algorithm, which attempts to sample the high-value transitions in the prioritized buffer, and uniformly sample from the normal experience buffer, improving sample efficiency of offline reinforcement learning, as well as alleviating the “extrapolation error” commonly arising in offline RL. Design/methodology/approach The authors propose a new structure of experience replay architecture, which consists of double experience replies, a prioritized experience replay and a normal experience replay, supplying samples for policy updates in different training phases. At the first training stage, the authors sample from prioritized experience replay according to the calculated priority of each transitions. At the second training stage, the authors sample from the normal experience replay uniformly. The combination of the two experience replies is initialized by the same offline data set. Findings The proposed method eliminates out-of-distribution problem in an offline RL regime, and promotes training by leveraging a new efficient experience replay. The authors evaluate their method on D4RL benchmark, and the results reveal that the algorithm can achieve superior performance over the state-of-the-art offline RL algorithm. The ablation study proves that the authors’ experience replay architecture plays an important role in terms of improving final performance, data-efficiency and training stability. Research limitations/implications Because of the extra addition of prioritized experience replay, the proposed method increases the computational burden and has the risk of changing data distribution due to the combined sample strategy. Therefore, researchers are encouraged to use the experience replay block effectively and efficiently further. Practical implications Offline RL is susceptible to the quality and coverage of pre-collected data, which may be not easy to be collected from specific environment, demanding practitioners to handcraft behavior policy to interact with environment for gathering data. Originality/value The proposed approach focuses on the experience replay architecture for offline RL, and empirically demonstrates the superiority of the algorithm on data efficiency and final performance over conservative Q-learning across diverse D4RL tasks. In particular, the authors compare different variants of their experience replay block, and the experiments show that the stages, when to sample from the priority buffer, play an important role in the algorithm. The algorithm is easy to implement and can be combined with any Q-value approximation-based offline RL methods by minor adjustment.
Q-FiRM: Fidelity-based Rate Maximizing Routes for Quantum Networks

Efficient routing of information between end-nodes is a key enabler for secure quantum networks and quantum secret key sharing, which rely on creating and sustaining entangled states over time. However, such pairwise entanglements degrade due to channel loss and the storage of the entangled photons at the network nodes. The state of entanglement in turn impacts fidelity, a metric which quantifies the degree of similarity between a pair of quantum states. In this paper, we propose a routing solution that satisfies threshold fidelity requirements imposed by a receiver on the quantum information received from multiple transmitter nodes. Our solution selects intermediate repeaters from a pool of such nodes within the network to maximize the sum-rate of quantum information transfer. To this extent, we first provide expressions for the fidelity loss between adjacent nodes as well as for the end-to-end quantum data rate. Then, we propose a novel two-stage routing solution that (i) identifies the k-shortest paths for each transmitter using fidelity as cost metric and (ii) (heuristically) assigns a path for each transmitter depending on whether the repeater nodes have a single or multiple available memory units. Simulation results demonstrate that our proposed fidelity-based routing solution satisfies a wide range of fidelity requirements [0.6-0.79] while maximizing the quantum information transfer rate, outperforming the existing distance- and hop-based routing approaches.
Lightweight Remote-Sensing Image Super-Resolution via Re-Parameterized Feature Distillation Network

Recent deep learning based-works have made remarkable progress in Remote-Sensing Image Super-Resolution (RSISR). However, the complicated network architecture as well as a huge amount of parameters increase computational cost, hindering their practical deployment. To alleviate this problem, we propose a novel Re-parameterized Feature Distillation Network (ReFDN) for lightweight and efficient RSISR tasks. Feature distillation, refinement, condensation, and enhancement are efficiently integrated into the re-parameterized feature distillation block named ReFDB for lighter and stronger feature extraction. With the help of elaborate re-parameterized convolution (ReConv) design, we further boost the feature refinement capability without extra inference costs. Additionally, we design an efficient channel and spatial attention module (ECSA) to enhance the important objects and regions of the intermediate features adaptively. Conducted on both commonly used datasets and additional Google Earth data, the experimental results demonstrate our method can achieve a good trade-off between SR performance and network complexity. Our code will be publicly available at https://github.com/DaxingZ/ReFDN.
Quantum circuit simulation of superchannels

Abstract Quantum simulation is one of the central discipline to demonstrate the power of quantum computing. In recent years, the theoretical framework of quantum superchannels has been developed and applied widely as the extension of quantum channels. In this work, we study the quantum circuit simulation task of superchannels. We develop a quantum superchannel simulation algorithm based on the convex decomposition into sum of extreme superchannels. We demonstrate the algorithm by numerical simulation of qubit superchannels with high accuracy, making it applicable to current experimental platforms. Our study stands as an expansion of the superchannel theory to the field of quantum simulation and algorithm, as well as an extension of quantum simulation from channels and open-system dynamics to superchannels and processes with manifest quantum memory effects.
Sentiment Analysis of COVID-19 Tweets Using BiLSTM and CNN-BiLSTM

In a society where people express almost every thought they have on social media, analysing social media for sentiment has become very significant in order to understand what the masses are thinking. Especially microblogging website like twitter, where highly opinionated individuals come together to discuss ongoing socioeconomic and political events happening in their respective countries or happening around the world. For analysing such vast amounts of data generated every day, a model with high efficiency, i.e., less running time and high accuracy, is needed. Sentiment analysis has become extremely useful in this regard. A model trained on a dataset of tweets can help determine the general sentiment of people towards a particular topic. This paper proposes a bidirectional long short-term memory (BiLSTM) and a convolutional bidirectional long short-term memory (CNN-BiLSTM) to classify tweet sentiment; the tweets were divided into three categories—positive, neutral and negative. Specialized word embeddings such as Word2Vec or term frequency—inverse document frequency (tf-idf) were avoided. The aim of this paper is to analyse the performance of deep neural network (DNN) models where traditional classifiers like logistic regression and decision trees fail. The results show that the BiLSTM model can predict with an accuracy of 0.84, and the CNN-BiLSTM model can predict with an accuracy of 0.80.
Real-Time Target Detection System for Animals Based on Self-Attention Improvement and Feature Extraction Optimization

In this paper, we propose a wildlife detection algorithm based on improved YOLOv5s by combining six real wildlife images of different sizes and forms as datasets. Firstly, we use the RepVGG model to simplify the network structure that integrates the ideas of VGG and ResNet. This RepVGG introduces a structural reparameterization approach to ensure model flexibility while reducing the computational effort. This not only enhances the ability of model feature extraction but also speeds up the model computation, further improving the model’s real-time performance. Secondly, we use the sliding window method of the Swin Transformer module to divide the feature map to speed up the convergence of the model and improve the real-time performance of the model. Then, it introduces the C3TR module to segment the feature map, expand the perceptual field of the feature map, solve the problem of backpropagation gradient disappearance and gradient explosion, and enhance the feature extraction and feature fusion ability of the model. Finally, the model is improved by using SimOTA, a positive and negative sample matching strategy, by introducing the cost matrix to obtain the highest accuracy with the minimum cost. The experimental results show that the improved YOLOv5s algorithm proposed in this paper improves mAP by 3.2% and FPS by 11.9 compared with the original YOLOv5s algorithm. In addition, the detection accuracy and detection speed of the improved YOLOv5s model in this paper have obvious advantages in terms of the detection effects of other common target detection algorithms on the animal dataset in this paper, which proves that the improved effectiveness and superiority of the improved YOLOv5s target detection algorithm in animal target detection.
Implementing Behavioral Biometrics With TRUST

Companies using behavioral biometrics to monitor employee performance risk creating a dangerous tension in the workplace. Implementing behavioral biometrics with TRUST (transparency, respect, understanding, sharing, and timing) may be the solution.
Student’s Perception Towards the Use of Jeopardy Game in English Learning

The present study aims to determine the student's perception towards the use of Jeopardy Game, and also investigates how the teacher uses Jeopardy Game in English learning. This research study was used a qualitative study conducted in-state senior high school level in Surabaya. A close-ended questionnaire was used to obtain the data. The results show that Jeopardy Game makes English learning more alive, comfortable, enjoyable, fun, and interesting. It also boosts the student's motivation in learning English. Jeopardy Game also enhances the student's vocabulary knowledge, recalls the previous materials, improves the student's speaking skill and develop the student's critical thinking. This study suggests using Jeopardy Game in the learning process since many benefits in increasing the student's engagement in the classroom.&#x0D; Keywords: Classroom Games, English Learning, Jeopardy Game, Student’s Perception
Multi-Scale Feature Interactive Fusion Network for RGBT Tracking

The fusion tracking of RGB and thermal infrared image (RGBT) is paid wide attention to due to their complementary advantages. Currently, most algorithms obtain modality weights through attention mechanisms to integrate multi-modalities information. They do not fully exploit the multi-scale information and ignore the rich contextual information among features, which limits the tracking performance to some extent. To solve this problem, this work proposes a new multi-scale feature interactive fusion network (MSIFNet) for RGBT tracking. Specifically, we use different convolution branches for multi-scale feature extraction and aggregate them through the feature selection module adaptively. At the same time, a Transformer interactive fusion module is proposed to build long-distance dependencies and enhance semantic representation further. Finally, a global feature fusion module is designed to adjust the global information adaptively. Numerous experiments on publicly available GTOT, RGBT234, and LasHeR datasets show that our algorithm outperforms the current mainstream tracking algorithms.
An Ensemble Approach for Aspect-Level Sentiment Classification Using Deep Learning Methods

Fine-grained or aspect-level sentiment classification (ALSC) has attracted considerable attention from natural language processing experts and researchers in recent years. The focus of research in this area is on the development of efficient deep learning techniques which aim to correctly detect the polarity of sentiments of aspects of a product or service that are important to customers. Many of the deep learning methods perform well but are computationally costly. Thus, there is a need to propose an approach that is both accurate and computationally efficient. In our work, we propose an ensemble of deep learning methods for ALSC. The base classifiers selected in our approach are computationally less expensive but their performance is good. Our ensemble approach is based on the principle of meta-learning. The proposed ensemble approach is evaluated across 4 benchmark datasets of ALSC literature. The F1 score obtained by the proposed ensemble is better than each of the base classifiers as well as other recently proposed deep learning methods. Furthermore, we evaluate our proposed ensemble for statistical significance using the Bayesian sign-rank test. The results of the Bayesian sign-rank test reveal that the proposed ensemble approach outperforms the base classifiers in a statistically significant way.
SQL Injection and Its Detection Using Machine Learning Algorithms and BERT

SQL Injection attacks target the database of applications to extract private information or inject malicious code. In this paper, we attempt to present a well-researched and practiced methodology to detect SQL Injection attacks accurately. These kinds of attacks are a very common means of network security attacks which can cause inestimable loss to the database. Building measures against them is a current research hotspot. Considering the possible complexity of queries involved and the need for a quick and efficient detection system in place, turning to machine learning techniques to combat and detect such attacks is the right choice. This is why we have undertaken the task of analyzing a number of machine and deep learning algorithms on a vast dataset of 41,770 points (consisting of both malicious and normal queries). We aim at finding a system that is accurate and fine-tuned for the best possible results and test each of the algorithms on various performance metrics to identify the one that performs the best. BERT outperforms the rest with a validation accuracy of 99.98%.
Intra-Class Universal Adversarial Attacks on Deep Learning-Based Modulation Classifiers

Most existing adversarial attack methods generally rely on ideal assumptions, which is unreasonable for practical applications. In this paper, a practical threat model which utilizes adversarial attacks for anti-eavesdropping is proposed and a physical intra-class universal adversarial perturbation (IC-UAP) crafting method against DL-based wireless signal classifiers is then presented. First, an IC-UAP algorithm is proposed based on the threat model to craft a stronger UAP attack against the samples in a given class from a batch of samples in the class. Then, we develop a physical attack algorithm based on the IC-UAP method, in which perturbations are optimized under random shifting to enhance the robustness of IC-UAPs against the unsynchronization between adversarial attacks and attacked signals. Finally, the numerical results corroborate the effectiveness of the proposed approach based on the benchmark dataset.
Upcycling historical data collections. A paradigm for digital history?

Purpose Upcycling is conceptualised as a digital historical research practice aimed at increasing the scientific value of historical data collections produced in print or in electronic form between the eighteenth and the late twentieth centuries. The concept of upcycling facilitates data rescue and reuse as well as the study of information creation processes deployed by previous generations of researchers. Design/methodology/approach Based on a selection of two historical reference works and two legacy collections, an upcycling workflow consisting of three parts (input, processing and documentation and output) is developed. The workflow facilitates the study of historical information creation processes based on paradata analysis and targets the cognitive processes that precede and accompany the creation of historical data collections. Findings The proposed upcycling workflow furthers the understanding of computational methods and their role in historical research. Through its focus on the information creation processes that precede and accompany historical research, the upcycling workflow contributes to historical data criticism and digital hermeneutics. Originality/value Many historical data collections produced between the eighteenth and the late twentieth century do not comply with the principles of FAIR data. The paper argues that ignoring the work of previous generations of researchers is not an option, because it would make current research practices more vulnerable and would result in losing access to the experiences and knowledge accumulated by previous generations of scientists. The proposed upcycling workflow takes historical data collections seriously and makes them available for future generations of researchers.
A social media Data-Driven analysis for transport policy response to the COVID-19 pandemic outbreak in Wuhan, China

Non-pharmacological interventions (NPI) such as social distancing and lockdown are essential in preventing and controlling emerging pandemic outbreaks. Many countries worldwide implemented lockdowns during the COVID-19 outbreaks. However, due to the lack of prior experience and knowledge about the pandemic, it is challenging to deal with short-term polices decision-making due to the highly stochastic and dynamic nature of the COVID-19. Thus, there is a need for the exploration of policy decision analysis to help agencies to adjust their current policies and adopt quickly. In this study, an analytical methodology is developed to analysis urban transport policy response for pandemic control based on social media data. Compared to traditional surveys or interviews, social media can provide timely data based on the feedback from public in terms of public demands, opinions, and acceptance of policy implementations. In particular, a sentiment-aware pre-trained language model is fine-tuned for sentiment analysis of policy. The Latent Dirichlet Allocation (LDA) model is used to classify documents, e.g., posts collected from social media, into specific topics in an unsupervised manner. Then, entropy weights method (EWM) is used to extract public policy demands based on the classified topics. Meanwhile, a Jaccard distance-based approach is proposed to conduct the response analysis of policy adjustments. A retrospective analysis of transport policies during the COVID-19 pandemic in Wuhan, China is presented using the developed methodology. The results show that the developed policymaking support methodology can be an effective tool to evaluate the acceptance of anti-pandemic policies from the public's perspective, to assess the balance between policies and people's demands, and to further perform the response analysis of a series of policy adjustments based on online feedback.
Distributed Spatial Transformer for Object Tracking in Multi-Camera

Today’s video surveillance devices are almost ubiquitous and ready to collect data. Tracking targets in surveillance applications can be a challenging task. However, the work of manual surveillance is tiresome. A camera can only record information within the area it records, and that area is quite limited. Therefore, in order to monitor critical areas, it is necessary to setup many cameras in different places. As a result, personnel monitoring of all cameras remains a more difficult task. By re-identifying individuals captured by several cameras, this work presents a framework for tracking people in the presence of many cameras, while giving some potential methods to achieve fast people detection and tracking in multi-view cameras. By employing spatial transformations to achieve real-time multi-view tracking capability, the scheme is feasible and has been implemented.