Reviewerly/exHarmony huggingface dataset:
Data Augmented Sequential Recommendation Based on Counterfactual Thinking

Sequential recommendation has recently attracted increasing attention from the industry and academic communities. While previous models have achieved remarkable successes, an important problem may still hinder their performances, that is, the sparsity of the real-world data. In this paper, we propose a novel counterfactual data augmentation framework to alleviate the problem of data sparsity. In specific, our framework contains a sampler model and an anchor model. The sampler model aims to generate high-quality user behavior sequences, while the anchor model is trained based on the original and new generated samples, and leveraged to provide the final recommendation list. To implement the sampler model, we first design four types of heuristic methods based on either random or frequency-based strategies. And then, to improve the quality of the generated sequences, we propose two learning-based samplers by discovering the decision boundaries or increasing the sample informativeness. At last, we build an RL based model to automatically determine where to edit the history behaviors and how many items should be replaced. Considering that the sampler model can be imperfect, we, at last, analyze the influence of the noisy information contained in the generated sequences on the anchor model in theory, and design a simple but effective method to better serve the anchor model. We conduct extensive experiments to demonstrate the effectiveness of our model.
Space Target Anomaly Detection Based on Gaussian Mixture Model and Micro-Doppler Features

With the dramatic increase in human space activities, anomaly detection becomes an important issue in passive space target surveillance. In this article, an anomaly detection algorithm based on Gaussian mixture model and radar micro-Doppler features is proposed to detect the abnormal motion status of the space target. By coherent sampling and time-frequency analysis on the radar echo with additive white Gaussian noise corresponding to the normal motion statuses of the target, four micro-Doppler features are extracted and tested for normal distribution. Furthermore, the distribution of the multi-dimensional features and the corresponding parameters are fitted and estimated by Gaussian mixture model and expectation-maximization algorithm. Then, an anomaly detector is derived by solving for decision region using the fitted probability density function and a preset confidence level. Experimental results show that the average anomaly detection rate of the proposed method is 16.7%, 19.1%, and 34.0% higher than the one-class support vector machine, the convex hull, and the convolutional autoencoder-based methods, respectively.
An Efficient Prototype-Based Model for Handwritten Text Recognition with Multi-loss Fusion

Prototype learning has achieved good performance in many fields, showing higher flexibility and generalization. In this paper, we propose an efficient text line recognition method based on prototype learning with feature-level sliding windows for classification. In this framework, we combine weakly supervised discrimination and generation loss for learning feature representations with intra-class compactness and inter-class separability. Then, dynamic weighting and pseudo-label filtering are also adopted to reduce the influence of unreliable pseudo-labels and improve training stability significantly. Furthermore, we introduce consistency regularization to obtain more reliable confidence distributions and pseudo-labels. Experimental results on digital and Chinese handwritten text datasets demonstrate the superiority of our method and justify advantages in transfer learning on small-size datasets.
Efficient Consensus and Elastic Resource Allocation Empowered Blockchain for Vehicular Networks

With the advantages of decentralization and high reliability, the blockchain is widely used in vehicular networks, which has attracted widespread attention from academia and industries. However, when combining blockchain into vehicular networks, there exist three challenges: 1) how to access pre-deployed blockchain when vehicles are disconnected from network infrastructures, 2) how to reduce the communication overhead caused by an increasing network scale while maintaining decentralization of blockchains, 3) how to balance limited vehicle's resources and high throughput of blockchain. To address these challenges, we propose an efficient Device-to-Device (D2D) Network-empowered blockchain framework to receive and process transactions from D2D vehicles. We then propose a grouped and leaderless consensus mechanism to reduce communication overhead as the network scale increases. We also propose an elastic resource allocation scheme using the Lyapunov optimization theory to achieve high throughput given limited resources. Finally, numerical results show that the proposed mechanisms achieve D2D interaction under different network conditions and better performance than existing approaches.
Comparing Visual Search between Physical Environments and VR

Virtual reality is used under the assumption that humans behave similarly in virtual reality and in physical environments. This assumption lacks extensive testing, thus risking unexpected human behavior when working with virtual reality. In the current study, we tested this assumption by comparing the performance on visual search tasks in physical environments and in virtual reality. The participants (n = 29) performed search tasks while standing still and search tasks that required walking in both physical environments and in virtual reality. We compared search speed, accuracy, workload, and cognitive absorption with Bayesian t-tests and factorial ANOVAs. Our results provide weak to moderate evidence that all are similar in virtual reality as in physical environments, even when controlling for virtual reality experience and personal innovativeness. Our findings provide some evidence for the assumption that virtual reality can simulate and replace visual search tasks in physical environments. This knowledge can be used to justify the use of virtual reality to, for example, study the human visual system, train surgeons, and remotely operate ships. We suggest further scrutiny of the underlying assumptions of virtual reality use, for example, by studying more naturalistic scenarios, the role of memory, and interaction with objects.
Language Transformers for Remote Sensing Visual Question Answering

Remote sensing visual question answering (RSVQA) opens new avenues to promote the use of satellites data, by interfacing satellite image analysis with natural language processing. Capitalizing on the remarkable advances in natural language processing and computer vision, RSVQA aims at finding an answer to a question formulated by a human user about a remote sensing image. This is achieved by extracting representations from images and questions, and then fusing them in a joint representation. Focusing on the language part of the architecture, this study compares and evaluates the adequacy to the RSVQA task of two language models, a traditional recurrent neural network (Skip-thoughts) and a recent attentionbased Transformer (BERT). We study whether large transformer models are beneficial to the task and whether fine-tuning is needed for these models to perform at their best. Our findings show that the models benefit from fine-tuning language models and that RSVQA with BERT is slightly but consistently better when properly fine-tuned.
Identifying Ambiguous Similarity Conditions via Semantic Matching

Rich semantics inside an image result in its ambiguous relationship with others, i.e., two images could be similar in one condition but dissimilar in another. Given triplets like “aircraft” is similar to “bird” than “train”, Weakly Supervised Conditional Similarity Learning (WS-CSL) learns multiple embeddings to match semantic conditions without explicit condition labels such as “can fly”. However, similarity relationships in a triplet are uncertain except providing a condition. For example, the previous comparison becomes invalid once the conditional label changes to “is vehicle”. To this end, we introduce a novel evaluation criterion by predicting the comparison's correctness after assigning the learned embeddings to their optimal conditions, which measures how much WS-CSL could cover latent semantics as the supervised model. Furthermore, we propose the Distance Induced Semantic COndition VER-ification Network (DiscoverNet), which characterizes the instance-instance and triplets-condition relations in a “decompose-and-fuse” manner. To make the learned embeddings cover all semantics, Discovernet utilizes a set module or an additional regularizer over the correspondence between a triplet and a condition. DiscoverNet achieves state-of-the-art performance on benchmarks like UT-Zappos-50k and Celeb-A w.r.t. different criteria.
Comprehensive Mobile Traffic Characterization Based on a Large-Scale Mobile Traffic Dataset

Mobile traffic has accounted for a principal part of network traffic with the ways of accessing the Internet shifting to mobile devices. Subsequently, mobile traffic analysis becomes the focus of research. However, there is a lack of studies on mobile traffic characterization, while the traffic characteristics are important for obtaining a clear understanding of mobile networks. To make up for this research gap, this paper provides a comprehensive overview and characterization of current mobile application traffic. The properties of mobile traffic are described from four perspectives based on a large-scale mobile traffic dataset, including basic information (the destination IP, the destination Port, and the protocol), domain name usage, HTTP/TLS protocol usage, and the traffic flow. Besides, the properties of mobile traffic shown by different application categories are also analyzed simultaneously. Finally, a discussion is provided on how the presented observations work on various traffic analysis tasks in mobile networks.
Evaluating the Object-Centered User Interface in Head-Worn Mixed Reality Environment

User interface (UI) in head-worn mixed reality (MR) can be divided into two categories: user-centered and object-centered. Many studies have focused on user-centered UI, but few have explored object-centered UI. In this research, we explored the design of object-centered UI in head-worn MR environment. First, (b) we proposed three design considerations for object-centered UI in head-worn MR environment, and then verified them through two user studies with four UIs, where one is pure object-centered UI, one is pure user-centered UI, the others are hybrid UIs. In the first study, we conducted a user experience (UX) research on the access comfort, convenience and unobstructed sight degree of each UI, and demonstrated users’ preferences for the object-centered UI. In the second study, we designed a real application scenario to further evaluate the design considerations in a complex MR environment, and assessed its usability. We found positive user experience qualities of object-centered UI. Finally, we summarized four design recommendations, which could guide the UI design for the interaction with real-world objects in the future of everyday life with head-worn MR.
Few-Shot aerial image classification with deep economic network and teacher knowledge

Deep learning has achieved excellent achievements and has become the mainstream in the field of aerial image classification. While obtaining remarkable success, deep learning-based approaches are notoriously dependent on large amounts of labelled data. Few-shot learning uses existing knowledge to learn from few samples and quickly generalizes to new tasks. In this work, we proposed the few-shot learning with deep economic network and teacher knowledge for aerial image classification. Firstly, we performed simplification twice to reduce large-scale parameters and computational effort in deep networks. In the first simplification, the redundancy in feature inputs’ main components is reduced, and the implicit information in redundant components is extracted instead of directly discarding the redundant components. The channel and spatial redundancies in deeper layers’ inputs are reduced in the second simplification. Secondly, the teacher knowledge guides the random sampling and uses limited samples to improve classification performance. We conducted extensive experiments on NWPU-RESISC45, RSD46-WHU, and UC Merced datasets. The experimental results reveal that the proposed method has better classification accuracy, fewer network parameters, and less computational effort. Experiments on miniImageNet, FC100, CUB, and cross-domain datasets show that our method also maintains advanced classification accuracy on few-shot image classification benchmarks.
Projected Model Counting: Beyond Independent Support

Given a system of constraints over a set X of variables, projected model counting asks us to count satisfying assignments of the constraint system projected on a subset $$\mathcal {P}$$ of X. A key idea used in modern projected counters is to first compute an independent support, say $$\mathcal {I}$$ , that is often a small subset of $$\mathcal {P}$$ , and to then count models projected on $$\mathcal {I}$$ instead of on $$\mathcal {P}$$ . While this has been effective in scaling performance of counters, the question of whether we can benefit by projecting on variables beyond $$\mathcal {P}$$ has not been explored. In this paper, we study this question and show that contrary to intuition, it can be beneficial to project on variables even beyond $$\mathcal {P}$$ . In several applications, a good upper bound of the projected model count often suffices. We show that in several such cases, we can identify a set of variables, called upper bound support (UBS), that is not necessarily a subset of $$\mathcal {P}$$ , and yet counting models projected on UBS guarantees an upper bound of the projected model count. Theoretically, a UBS can be exponentially smaller than the smallest independent support. Our experiments show that even otherwise, UBS-based projected counting can be faster than independent support-based projected counting, while yielding bounds of high quality. Based on extensive experiments, we find that UBS-based projected counting can solve many problem instances that are beyond the reach of a state-of-the-art independent support-based projected model counter.
An Accelerated GPU Library for Homomorphic Encryption Operations of BFV Scheme

This paper presents an accelerated and parallelized GPU implementation for homomorphic encryption operations of the Brakerski-Fan-Vercauteren (BFV) scheme. We improved the run-time performance by optimizing homomorphic multiplication, relinearization, rotation, and addition using Number Theoretic Transform (NTT) and Barrett Reduction and utilizing a Compute Unified Device Architecture (CUDA). To the best of our knowledge, this implementation performs the fastest homomorphic operations in the literature. We used the Simple Encrypted Arithmetic Library (SEAL) version 3.6.6 BFV scheme for implementation on a GPU. Our implementation achieved $13.39\times, 47.01\times, 39.6\times$, and $33.71\times$ speedup compared to SEAL running on CPU for addition, multiplication, relinearization, and rotation, respectively for a modulus size of 438-bits and ring degree of 16,384. For the same modulus size and ring degree, this implementation performed one homomorphic multiplication in 1 ms, a relinearization operation in 0.4 ms, a rotation in 0.5 ms, and an addition in 0.017 ms, which demonstrates significant performance improvement over state-of-the-art.
A Vision Transformer Based Scene Text Recognizer with Multi-grained Encoding and Decoding

Recently, vision Transformer (ViT) has attracted more and more attention, many works introduce the ViT into concrete vision tasks and achieve impressive performance. However, there are only a few works focused on the applications of the ViT for scene text recognition. This paper takes a further step and proposes a strong scene text recognizer with a fully ViT-based architecture. Specifically, we introduce multi-grained features into both the encoder and decoder. For the encoder, we adopt a two-stage ViT with different grained patches, where the first stage extracts extent visual features with 2D fine-grained patches and the second stage aims at the sequence of contextual features with 1D coarse-grained patches. The decoder integrates Connectionist Temporal Classification (CTC)-based and attention-based decoding, where the two decoding schemes introduce different grained features into the decoder and benefit from each other with a deep interaction. To improve the extraction of fine-grained features, we additionally explore self-supervised learning for text recognition with masked autoencoders. Furthermore, a focusing mechanism is proposed to let the model target the pixel reconstruction of the text area. Our proposed method achieves state-of-the-art or comparable accuracies on benchmarks of scene text recognition with a faster inference speed and nearly $$50\%$$ reduction of parameters compared with other recent works.
Modelling a Reinforcement Learning Agent For Mountain Car Problem Using Q – Learning With Tabular Discretization

The advancement of Reinforcement Learning (RL) algorithms has shown great success in the field of adaptive and responsive video games. Open AI gym models are open-source visual interfaces for representative environments for modelling robust reinforcement learning tasks. This work aims at solving the mountain car problem involving the "MountainCar-v0" environment used from the OpenAI gym collection framework. Using the Q - learning algorithm, we present a novel algorithm using Tabular-Discretization for solving the considered problem. The gym environment we have considered has a default reward threshold, which is the reward value that an agent must earn before the task gets completed. The proposed model achieved a value close to the threshold parameter set for the experiment. Hyperparameter tuning has been carried out in the latter, to better understand the convergence of the model in terms of the variance of all 4 hyperparameters taken into account- learning rate, decay rate, epsilon, and the discount factor. This proposed method of Tabular Q-Learning is both memory and time efficient in terms of other existing algorithms such as SARSA and Online Q-Learning.
Detection Of Spam Messages In E-Messaging Platform Using Machine Learning

In recent years, the internet has surpassed print as the dominant source of knowledge Individuals who don’t have enough time to actually read the newspaper resort to social networking sites to keep up with current events. Nowadays, there is an increased no of spam messages sent by an unauthorized user to everyone. It will mislead the user into some malware attack and trick the user to steal some information. Spam communications contain dangerous links that can harm our system and access system data. Spam messages are the same as genuine professional messages and they are targeted at people who are not aware of these frauds. Thus, the Detection of spam messages in E-messaging platforms using machine learning models has become a must in the current system. Machine learning techniques are used to detect spam communications on E-messaging platforms like you tube, Messenger, and SMS. The SMS Spam dataset for Short Messaging Service gives an accuracy of 98%, YouTube-spam-collection-v1 for YouTube gives an accuracy of 92.10% and spam_ham_dataset for E-mail gives the accuracy of 94.20% is achieved respectively. Classification of spam messages varies on the different digital messaging platforms. As an outcome, we’ll use this to identify junk mail communications from numerous E-mail platforms in real-time. It also classifies the spam messages sent by bots. The Naive Bayes algorithm is used for classification.
A Novel SQL Injection Detection Using Bi-LSTM and TF-IDF

SQL injection is one of the biggest threats to websites. A survey shows more than 300,000 attacks, 24,6% are SQL injection. Detection SQL injection is a complicated task because attackers can continue to change the query structure. Traditionally, SQL injection is detected using a deny list of several keywords, which is commonly used for SQL injection. However, this method is not effective anymore because the attacks have increased significantly. This research develops a technique to detect SQL injection using TF-IDF and Bi-LSTM to produce high accuracy. Experiment results indicate that the proposed method improves SQL injection detection with accuracy, precision, recall, and F1-score reaching 0.99.
A Machine Translation System from Indian Sign Language to English Text

Sign language recognition and translation is a crucial step towards improving communication between the deaf and the rest of the society. According to the Indian Sign Language Research and Training Centre (ISLRTC), India has around 300 certified human interpreters. With such a shortage of human interpreters, an alternative service is desired that helps people to achieve smooth communicate with deaf. In this study, an approach is presented that translates ISL sentences in English text using MobileNetV2 model and neural machine translation (NMT). The system features ISL corpus created from Brown corpus using ISL grammar rules. The approach converts the ISL videos into ISL gloss sequence using MobileNetV2 model and recognised ISL gloss sequence is then fed to machine translation module. MobileNetV2 was proven best-suited model for recognition of ISL sentences and NMT gives better result than statistical machine translation (SMT) to convert ISL gloss sequence into English text. The automatic and human evaluation of the proposed approach gives 83.3% and 86.1% accuracy, respectively.
Learning Moving-Object Tracking with FMCW LiDAR

In this paper, we propose a learning-based moving-object tracking method utilizing the newly developed LiDAR sensor, Frequency Modulated Continuous Wave (FMCW) LiDAR. Compared with most existing commercial LiDAR sensors, FMCW LiDAR can provide additional Doppler velocity information to each 3D point of the point clouds. Benefiting from this, we can generate instance labels as ground truth in a semi-automatic manner. Given the labels, we propose a contrastive learning framework, which pulls together the features from the same instance in embedding space and pushes apart the features from different instances, to improve the tracking quality. Extensive experiments are conducted on the recorded driving data, and the results show that our method outperforms the baseline methods by a large margin.
A Real-Time Polyp Detection Framework for Colonoscopy Video

Colorectal cancer is one of the most common malignant tumors in the world. Endoscopy is the best screening method for colorectal cancer, which uses a micro camera to enter the colorectal and check whether there are polyps on the internal mucosa. In order to assist doctors to work more accurately and efficiently, a real-time polyp detection framework for colonoscopy video is proposed in this paper. The swin transformer block is integrated into the CNN-based YOLOv5m network to enhance the local and global information of the feature map. Then, in order to reduce the influence of factors such as light changes and reflection, we use the ensemble prediction of time series to improve the temporal continuity of the detection results. The experimental results show that compared with the baseline network, the precision rate of our method is improved by 5.3% and the recall rate is improved by 3.5%. And compared with recent research, our method achieves a good trade-off between detection speed and accuracy.