Reviewerly/exHarmony huggingface dataset:
City-Scale Multi-Camera Vehicle Tracking based on Space-Time-Appearance Features

Multi-Camera Multi-Vehicle Tracking (MCMVT) is an essential task in the field of city-scale traffic management, which usually consists of three sub-tasks: object detection and re-identification (ReID), single-camera tracking, cross-camera trajectory association. Compared with existing methods, two challenges are considered and addressed in this paper: (1) low-confidence objects could be missed without extra data annotation, (2) precise association of trajectories from different cameras is affected by multiple factors. For the first challenge, a cascaded tracking method based on detection, appearance features and trajectory interpolation is proposed, exploiting potential real targets in low-confidence objects to improve detection and identification recall. For the second challenge, space, time and appearance features are proposed to be the most crucial factors for trajectory association, so a zone-gate and time-decay based matching mechanism is proposed to adjust original appearance matrix to link tracklets more precisely from different cameras. Extensive experimental results validate the effectiveness of the proposed innovative technologies.
Adaptive Feature Consolidation Network for Burst Super-Resolution

Modern digital cameras generally count on image signal processing (ISP) pipelines for producing naturalistic RGB images. Nevertheless, in comparison to DSLR cameras, low-quality images are generally output from portable mobile devices due to their physical limitations. The synthesized low-quality images usually have multiple degradations - low-resolution owing to small camera sensors, mosaic patterns on account of camera filter array and subpixel shifts due to camera motion. Such degradation usually restrain the performance of single image super-resolution methodologies for retrieving high-resolution (HR) image from a single low-resolution (LR) image. Burst image super-resolution aims at restoring a photo-realistic HR image by capturing the abundant information from multiple LR images. Lately, the soaring popularity of burst photography has made multi-frame processing an attractive solution for overcoming the limitations of single image processing. In our work, we thus aim to propose a generic architecture, adaptive feature consolidation network (AFCNet) for multi-frame processing. To alleviate the challenge of effectively modelling the long-range dependency problem, that multi-frame approaches struggle to solve, we utilize encoder-decoder based transformer backbone which learns multi-scale local-global representations. We propose feature alignment module to align LR burst frame features. Further, the aligned features are fused and reconstructed by abridged pseudo-burst fusion module and adaptive group upsampling modules, respectively. Our proposed approach clearly outperforms the other existing state-of-the-art techniques on benchmark datasets. The experimental results illustrate the effectiveness and generality of our proposed framework in upgrading the visual quality of HR images.
Deep Normalized Cross-Modal Hashing with Bi-Direction Relation Reasoning

Due to the continuous growth of large-scale multi-modal data and increasing requirements for retrieval speed, deep cross-modal hashing has gained increasing attention recently. Most of existing studies take a similarity matrix as supervision to optimize their models, and the inner product between continuous surrogates of hash codes is utilized to depict the similarity in the Hamming space. However, all of them merely consider the relevant information to build the similarity matrix, ignoring the contribution of the irrelevant one, i.e., the categories that samples do not belong to. Therefore, they cannot effectively alleviate the effect of dissimilar samples. Moreover, due to the modality distribution difference, directly utilizing continuous surrogates of hash codes to calculate similarity may induce suboptimal retrieval performance. To tackle these issues, in this paper, we propose a novel deep normalized cross-modal hashing scheme with bi-direction relation reasoning, named Bi_NCMH. Specifically, we build the multi-level semantic similarity matrix by considering bi-direction relation, i.e., consistent and inconsistent relation. It hence can holistically characterize relations among instances. Besides, we execute feature normalization on continuous surrogates of hash codes to eliminate the deviation caused by modality gap, which further reduces the negative impact of binarization on retrieval performance. Extensive experiments on two cross-modal benchmark datasets demonstrate the superiority of our model over several state-of-the-art baselines.
Multi stain graph fusion for multimodal integration in pathology

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen’s kappa between machine-derived vs. pathologist consensus scores. Broadly, this paper demonstrates the value of leveraging diverse pathology images for improved ML-powered histologic assessment.
ntHash2: recursive spaced seed hashing for nucleotide sequences

Spaced seeds are robust alternatives to k-mers in analyzing nucleotide sequences with high base mismatch rates. Hashing is also crucial for efficiently storing abundant sequence data. Here, we introduce ntHash2, a fast algorithm for spaced seed hashing that can be integrated into various bioinformatics tools for efficient sequence analysis with applications in genome research.ntHash2 is up to 2.1× faster at hashing various spaced seeds than the previous version and 3.8× faster than conventional hashing algorithms with naïve adaptation. Additionally, we reduced the collision rate of ntHash for longer k-mer lengths and improved the uniformity of the hash distribution by modifying the canonical hashing mechanism.ntHash2 is freely available online at github.com/bcgsc/ntHash under an MIT license.Supplementary data are available at Bioinformatics online.
Annealing-Based Quantum Computing for Combinatorial Optimal Power Flow

This paper proposes the use of annealing-based quantum computing for solving combinatorial optimal power flow problems. Quantum annealers provide a physical computing platform which utilises quantum phase transitions to solve specific classes of combinatorial problems. These devices have seen rapid increases in scale and performance, and are now approaching the point where they could be valuable for industrial applications. This paper shows how an optimal power flow problem incorporating linear multiphase network modelling, discrete sources of energy flexibility, renewable generation placement/sizing and network upgrade decisions can be formulated as a quadratic unconstrained binary optimisation problem, which can be solved by quantum annealing. Case studies with these components integrated with the IEEE European Low Voltage Test Feeder are implemented using D-Wave Systems’ 5,760 qubit Advantage quantum processing unit and hybrid quantum-classical solver.
Complementary Attention-Driven Contrastive Learning With Hard-Sample Exploring for Unsupervised Domain Adaptive Person Re-ID

Unsupervised domain adaptive (UDA) methods for person re-identification (Re-ID) aim to transfer the knowledge of the labeled source domain to the unlabeled target domain without further annotations, which is challenging due to the drift of label distribution and the missing of target domain labels. Improving the clustering accuracy of pseudo-labels can help the model fit the target domain. However, the errors of pseudo-label noise will be accumulated during training, which is harmful to the model performance. Moreover, the hard samples can lead to a large gap between intra-class features and a small gap between inter-class features. To address these problems, this paper proposes a complementary attention-driven contrastive learning with hard-sample exploring (CACHE) algorithm. In CACHE, on one hand, the complementary attention module is used to improve the discriminability of the features. The obtained discriminative features can reduce noisy pseudo-labels and improve the clustering accuracy of pseudo labels; On the other hand, we explore the hard samples based on the instance relationship and cluster relationship for contrastive learning. This way can make the cluster more compact. Extensive experiments on three large-scale person re-identification benchmarks demonstrate the effectiveness of the proposed method, which significantly outperforms state-of-the-art methods in terms of mAP and CMC.
Domain Adaptive Remaining Useful Life Prediction With Transformer

Prognostic health management (PHM) has become a crucial part in building highly automated systems, whose primary task is to precisely predict the remaining useful life (RUL) of the system. Recently, deep models including convolutional neural network (CNN) and long short-term memory (LSTM) have been widely-used to predict RUL. However, these models generally require sufficient labeled training data to guarantee fair performance, whereas a limited amount of labeled data overwhelmed by the abundance of unlabeled ones is the normality in industry, and the cost of full data annotation can be unaffordable. To attack this challenge, domain adaptation seeks to transfer the knowledge from a well-labeled source domain to another unlabeled target domain by mitigating their domain gap. In this paper, we leverage domain adaptation for RUL prediction and propose a novel method by aligning distributions at both the feature level and the semantic level. The proposed method facilitates a large improvement of model performance as well as faster convergence. Besides, we propose to use Transformer as backbone, which can capture long-term dependency more efficiently than the widely-used recurrent neural network (RNN), and is thus critical for boosting the robustness of the model. We test our model on CMAPSS dataset and its newly published variant N-CMAPSS provided by NASA, achieving state-of-the-art results on both source-only RUL prediction and domain adaptive RUL prediction tasks.
Accurate mobile-app fingerprinting using flow-level relationship with graph neural networks

Identifying mobile applications (apps) from encrypted network traffic (also known as app fingerprinting) plays an important role in areas like network management, advertising analysis, and quality of service. Existing methods mainly extract traffic features from packet-level information (e.g. packet size sequence) and build up classifiers to obtain good performance. However, the packet-level information suffers from small discrimination for the common traffic across apps (e.g. advertising traffic) and rapidly changing for the traffic before and after apps’ updating. As a result, their performance declines in these two real scenes. In this paper, we propose FG-Net, a novel app fingerprinting based on graph neural network (GNN). FG-Net leverages a novel kind of information: flow-level relationship, which is distinctive between different apps and stable across apps’ versions. We design an information-rich graph structure, named FRG, to embed both raw packet-level information and flow-level relationship of traffic concisely. With FRG, we transfer the problem of mobile encrypted traffic fingerprinting into a task of graph representation learning, and we designed a powerful GNN-based traffic fingerprint learner. We conduct comprehensive experiments on both public and private datasets. The results show the FG-Net outperforms the SOTAs in classifying traffic with about 18% common traffic. Without retraining, FG-Net obtains the most robustness against the updated traffic and increases the accuracy by 5.5% compared with the SOTAs.
AI Chatbots Learning Model in English Speaking Skill: Alleviating Speaking Anxiety, Boosting Enjoyment, and Fostering Critical Thinking

One of the most significant barriers to English learning for EFL (English as a Foreign Language) students is a lack of practice environments. Additionally, in the (EFL) classroom, emotions such as speaking anxiety and language enjoyment may influence students’ performance. It is well established that students’ achievement is harmed by foreign language anxiety. Artificial Intelligence (AI) advancements present an opportunity to solve this issue. AI chatbots are capable of deciphering the meanings of users’ comments and responding appropriately and have been built to engage with users using natural language. Language teachers and researchers have recommended the use of chatbots as tools of learning that would help to facilitate the implementation of language learning by acting as a speaking partner or tutor. In this research design, AI chatbot is employed in Casual Conversation Course as self-regulated learning for facilitating students’ speaking performance and interactions during the learning process in a university speaking classroom. By a preceding conversation with AI chatbot, it will alleviate speaking anxiety, boost learning enjoyment, and foster students’ critical thinking. In-depth interviews were delivered to four interviewees to ascertain the designed learning model from the teachers’ viewpoint. The result indicates that the proposed model is expected to be able to aid the learning process and so achieve the course’s objectives more effectively than conventional models.
A novel insurance claim blockchain scheme based on zero-knowledge proof technology

It is crucial to ensure the privacy and authenticity of patients’ medical data in the medical insurance claim process, but in the current medical insurance claim process, there are some problems such as low efficiency, complex service, unreliable data and data leakage. Therefore, considering the privacy and sensitivity of patients’ medical data, we can improve the current issues by employing blockchain, smart contracts and zero-knowledge proof technology. In this paper, we propose a novel medical insurance claim scheme based on smart contracts, blockchain and zero-knowledge proof. Our scheme mainly involves two scenarios: medical insurance purchasing and medical insurance claiming. In the privacy-preserving transaction phases of the two scenarios, we can ensure the legitimacy and privacy of the transactions between the patients and the insurance companies by using a non-interactive zero-knowledge proof and the homomorphic encryption algorithm under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. In the identity privacy-preserving phases of the two scenarios, we can ensure the legitimacy and the privacy of patients’ identities by integrating Schnorr protocol and Fiat–Shamir heuristic method. The security analysis, the computation cost and the communication cost of our scheme are given. Compared with our referred schemes, the performance evaluation shows that our scheme not only meets the requirements of the legality of the medical insurance claim, but also ensures the authenticity and privacy of the patients’ medical data. Moreover, the experimental results demonstrate that our scheme is feasible and has an acceptable time overhead.
Visual object tracking via non-local correlation attention learning

Siamese-based trackers have achieved remarkable advancements in performance of visual object tracking. The similarity matrix computed is crucial to Siamese-based tracker. However, the similarity matrix is lack of long-range dependency information which may lead to tracking drift on challenging scenes, like significant deformation, background clutter and occlusion. To address the above issue, this paper proposes a Siamese network with non-local correlation attention (SiamNCA). First, a non-local correlation attention module is proposed to integrate the long-range information into the similarity matrix, and give each sample in the search patch a weight based on their similarity to the template. Second, bi-directional features fusion module is introduced to fuse different similarity matrixes obtained with different level features. Finally, comprehensive experiments on representative tracking benchmarks, including OTB2015, VOT-2018, LaSOT and GOT-10k, reveal that the two modules can improve the performance of the baseline method in challenging scenes, and SiamNCA achieves state-of-art. For the average running speed, SiamNCA can achieve 43 FPS in real time.
YOLOv5-R: lightweight real-time detection based on improved YOLOv5

In reality, deploying the traditional object detection algorithm on mobile and embedded devices is difficult due to the limited memory and computation resources. To solve this problem, we propose a lightweight, real-time detection algorithm, YOLOv5-R. First, inserting the efficient channel attention (ECA) module into the Ghost module achieves the information interaction between channels and suppresses the redundant features. And applying dense connections to it further improves feature reuse and network performance, forming a Ghost-ECA-Dense (GED) module. Utilizing the GED module to construct the F-GED, which is the backbone with fewer parameters and better performance, replace CSPDarknet53 in YOLOv5. Second, the redundant operations are replaced with Ghost modules and depthwise separable convolution in the neck and head of YOLOv5, and YOLOv5-R is constructed, which significantly reduces the network size and improves the inference speed. Finally, YOLOv5-R is deployed into the AI embedded device Jetson Nano for TensorRT acceleration. The experimental results indicated that the performance of YOLOv5-R outperformed the YOLOv5s. Specifically, the mean average precision was increased from 69.9% to 72.7%, the number of floating-point operations per second and parameters are reduced by 25% and 47.2%, respectively. The model weight was only 7.52 MB, and frames per second achieved 37, which can provide real-time detection.
Translating Speech to Indian Sign Language Using Natural Language Processing

Language plays a vital role in the communication of ideas, thoughts, and information to others. Hearing-impaired people also understand our thoughts using a language known as sign language. Every country has a different sign language which is based on their native language. In our research paper, our major focus is on Indian Sign Language, which is mostly used by hearing- and speaking-impaired communities in India. While communicating our thoughts and views with others, one of the most essential factors is listening. What if the other party is not able to hear or grasp what you are talking about? This situation is faced by nearly every hearing-impaired person in our society. This led to the idea of introducing an audio to Indian Sign Language translation system which can erase this gap in communication between hearing-impaired people and society. The system accepts audio and text as input and matches it with the videos present in the database created by the authors. If matched, it shows corresponding sign movements based on the grammar rules of Indian Sign Language as output; if not, it then goes through the processes of tokenization and lemmatization. The heart of the system is natural language processing which equips the system with tokenization, parsing, lemmatization, and part-of-speech tagging.
Xiao-Shih: A Self-Enriched Question Answering Bot With Machine Learning on Chinese-Based MOOCs

This article introduces Xiao-Shih, the first intelligent question answering bot on Chinese-based massive open online courses (MOOCs). Question answering is critical for solving individual problems. However, instructors on MOOCs must respond to many questions, and learners must wait a long time for answers. To address this issue, Xiao-Shih integrates many novel natural language processing and machine learning approaches to achieve state-of-the-art performance. Furthermore, Xiao-Shih has a built-in self-enriched mechanism for expanding the knowledge base through open community-based question answering. This article proposes a novel approach, known as spreading question similarity (SQS), which iterates similar keywords on our keyword networks to find duplicate questions. Compared with BERT, an advanced neural language model, the results showed that SQS outperforms BERT on recall and accuracy above a prediction probability threshold of 0.8. After training, Xiao-Shih achieved a perfect correct rate. Furthermore, Xiao-Shih outperforms Jill Watson 1.0, which is a noted question answering bot, on answer rate with the self-enriched mechanism.
Comparative Evaluation and Comprehensive Analysis of Machine Learning Models for Regression Problems

Abstract Artificial intelligence and machine learning applications are of significant importance almost in every field of human life to solve problems or support human experts. However, the determination of the machine learning model to achieve a superior result for a particular problem within the wide real-life application areas is still a challenging task for researchers. The success of a model could be affected by several factors such as dataset characteristics, training strategy and model responses. Therefore, a comprehensive analysis is required to determine model ability and the efficiency of the considered strategies. This study implemented ten benchmark machine learning models on seventeen varied datasets. Experiments are performed using four different training strategies 60:40, 70:30, and 80:20 hold-out and five-fold cross-validation techniques. We used three evaluation metrics to evaluate the experimental results: mean squared error, mean absolute error, and coefficient of determination (R2 score). The considered models are analyzed, and each model's advantages, disadvantages, and data dependencies are indicated. As a result of performed excess number of experiments, the deep Long-Short Term Memory (LSTM) neural network outperformed other considered models, namely, decision tree, linear regression, support vector regression with a linear and radial basis function kernels, random forest, gradient boosting, extreme gradient boosting, shallow neural network, and deep neural network. It has also been shown that cross-validation has a tremendous impact on the results of the experiments and should be considered for the model evaluation in regression studies where data mining or selection is not performed.