Reviewerly/exHarmony huggingface dataset:
The Effects of Distance Education Capabilities and Competencies on Learning Outcomes During COVID-19 in Higher Education Institutions

This study aims to determine the relationships among students' self-learning capability, lecturers' teaching competencies, COVID-19 conditions, and students' learning outcomes during online teaching in tourism higher education. This study also investigates the role of distance education tools on the relationship between behavioral factors and learning outcomes. The study adopted a mixed-methods approach, including an experimental design and a quantitative model. A qualitative experimental study to evaluate the students' attitudes and behaviors during distance education was followed by quantitative research. A hypothesized conceptual model was tested using structural equation modeling to analyze the relationships among the factor groups and the mediating role of distance learning tools. As a result, while "students' self-learning capabilities" and "lecturers' teaching competencies" had a positive impact on "students' learning outcomes", "COVID-19 conditions" had a negative impact. All hypotheses were supported.
PSECAS: A physical unclonable function based secure authentication scheme for Internet of Drones

Internet of Drones (IoD) is a thriving technology that has found its application in various fields ranging from civilian to military and industries, empowering users to efficiently perform strenuous operations. However, there are multitudinous security concerns in an IoD environment, such as a drone having to exchange vital information with the control station server and respective users over an open unsecured wireless channel. It is indispensable to ensure the security of this sensitive information, on account of which a Physically Unclonable Function (PUF)-based Secure Authentication Scheme for the Internet of Drones (PSECAS) has been suggested in this paper. The security of the proffered scheme has been ensured under the ROR model and AVISPA simulation. Moreover, informal analysis demonstrates that PSECAS fulfills the necessary security requirements and is safe against various adversarial attacks. Finally, a rigorous comparison of PSECAS with other subsisting schemes exemplifies the robustness and superiority of our scheme.
Image caption generation using a dual attention mechanism

In order to create a statement that accurately captures the main idea of an ambiguous visual, which is said to be a significant and demanding task? Conventional image captioning schemes are categorized into 2 classes: retrieval-oriented schemes and generation-oriented schemes. The image caption generating system should provide precise, fluid, natural, and informative phrases as well as accurately identify the content of the image, such as scene, object, relationship, and properties of the object in the image. However, it can be challenging to accurately express the image's content when creating image captions because not all visual information can be used. In this article, a new image captioning model is introduced that includes 3 main phases like (1) Extraction of Inception V3 features (2) Dual (Visual and Textual) attention generation and (3) generation of image caption. Convolutional Neural Network (CNN) is used to generate visual attention after first deriving initial V3 features. The input texts for the associated images, on the other hand, are analyzed and given to LSTM for the creation of textual attention. To create image captions, Bidirectional LSTM (BI-LSTM) is used to combine textual and visual attention. The Self Improved Electric Fish Optimization (SI-EFO) algorithm is used in particular to optimize the weights of the BI-LSTM. In the end, several measures confirm that the implemented system has improved. The adopted model is 35.21%, 33.76%, 39.52%, 29.69%, 30.12%, 21.49%, and 31.71% better than GAN-RL, LSTM, GRU, EC + GOA, EC + CMBO, EC + DA, EC + EFO models.
DiffMotion: Speech-Driven Gesture Synthesis Using Denoising Diffusion Model

Speech-driven gesture synthesis is a field of growing interest in virtual human creation. However, a critical challenge is the inherent intricate one-to-many mapping between speech and gestures. Previous studies have explored and achieved significant progress with generative models. Notwithstanding, most synthetic gestures are still vastly less natural. This paper presents DiffMotion, a novel speech-driven gesture synthesis architecture based on diffusion models. The model comprises an autoregressive temporal encoder and a denoising diffusion probability Module. The encoder extracts the temporal context of the speech input and historical gestures. The diffusion module learns a parameterized Markov chain to gradually convert a simple distribution into a complex distribution and generates the gestures according to the accompanied speech. Compared with baselines, objective and subjective evaluations confirm that our approach can produce natural and diverse gesticulation and demonstrate the benefits of diffusion-based models on speech-driven gesture synthesis. Project page: https://github.com/zf223669/DiffMotion.
Vibro: Video Browsing with Semantic and Visual Image Embeddings

Vibro represents a powerful tool for interactive video retrieval and browsing and is the winner of the Video Browser Showdown 2022. Following the saying of “never change a winning system” we did not change any of the underlying concepts nor added any new features. Instead, we focused on improving the three existing cornerstones of the software, which are text-to-image search, image-to-image search and browsing results with 2D sorted maps. The changes to these three parts are summarized in this paper, and in addition, an overview of the AVS-mode of vibro is given.
diveXplore at the Video Browser Showdown 2023

The diveXplore system has been participating in the VBS since 2017 and uses a sophisticated content analysis stack as well as an advanced interface for concept, object, event, and texts search. This year, we perform several changes in order to make the system both much easier to use as well as more efficient. These changes include using shot-based analysis over uniform sampled segments, integration of textual and image transformers (CLIP), as well as optimizing the user interface for faster browsing of video summaries.
R-YOLOv5: A Lightweight Rotational Object Detection Algorithm for Real-Time Detection of Vehicles in Dense Scenes

A lightweight rotational object detection algorithm, R-YOLOv5, is proposed to address the limitations of traditional object detection algorithms that do not consider the diversity of vehicle scales in drone images and fail to obtain information on rotation angles.The proposed algorithm incorporated an angle prediction branch and introduced a circular smooth label (CSL) angle classification method to make it suitable for detection scenarios based on rotational boxes.A cascaded Swin Transformer block (STrB) is used to reduce computational complexity during feature fusion in the backbone network, further enhancing semantic information and global perception capabilities for small objects.A feature enhancement attention module (FEAM) is proposed to improve the utilization of detailed information through local feature self-supervision.An adaptive spatial feature fusion structure (ASFF) is introduced, which employs features extracted from different levels of the backbone network to perform multi-scale feature fusion.The experimental results show that the detection accuracy reaches 84.91% on the Drone-Vehicle dataset and 90.23% on the UCAS-AOD remote sensing dataset.The lightweight model has a parameter count of only 2.02 million and can achieve 82.6 FPS for high-resolution images, which is significantly better than existing lightweight models and more suitable for real-time detection of rotating vehicles in dense scenes, making it suitable for deployment on a large majority of embedded platforms.
Guided Discrimination and Correlation Subspace Learning for Domain Adaptation

As a branch of transfer learning, domain adaptation leverages useful knowledge from a source domain to a target domain for solving target tasks. Most of the existing domain adaptation methods focus on how to diminish the conditional distribution shift and learn invariant features between different domains. However, two important factors are overlooked by most existing methods: 1) the transferred features should be not only domain invariant but also discriminative and correlated, and 2) negative transfer should be avoided as much as possible for the target tasks. To fully consider these factors in domain adaptation, we propose a guided discrimination and correlation subspace learning (GDCSL) method for cross-domain image classification. GDCSL considers the domain-invariant, category-discriminative, and correlation learning of data. Specifically, GDCSL introduces the discriminative information associated with the source and target data by minimizing the intraclass scatter and maximizing the interclass distance. By designing a new correlation term, GDCSL extracts the most correlated features from the source and target domains for image classification. The global structure of the data can be preserved in GDCSL because the target samples are represented by the source samples. To avoid negative transfer issues, we use a sample reweighting method to detect target samples with different confidence levels. A semi-supervised extension of GDCSL (Semi-GDCSL) is also proposed, and a novel label selection scheme is introduced to ensure the correction of the target pseudo-labels. Comprehensive and extensive experiments are conducted on several cross-domain data benchmarks. The experimental results verify the effectiveness of the proposed methods over state-of-the-art domain adaptation methods.
LSTM-GRU Model-Based Channel Prediction for One-Bit Massive MIMO System

In this article, we propose a deep learning (DL) model to estimate the channel matrix for a massive multi-input multi-output (MIMO) system with a one-bit analog-to-digital converter (ADC). In the literature, various DL methods were proposed to estimate the channel matrix; however, they were limited to a small range of noise levels. We are driven to break the data appropriately so that the estimation performance improves with the signal-to-noise ratio (SNR) because the primary premise is that the DL model should perform better in the case of higher SNR. We suggested breaking the received signal into sequences to consider the entire system as multiple sub-systems, where each antenna serves the same number of users considered as a sub-system. Consequently, the model trained the data received by each sub-system sequentially. However, the conventional back-propagation with extensive training, because of the large number of base station (BS) antennas, causes a vanishing gradient, leading to training failure. Hence, we combine long short-term memory (LSTM) with gated recurrent units (GRU) to accelerate the training speed of the network under the hypothesis that LSTM effectively processes MIMO data and overcomes the vanishing gradient problem, whereas GRU accelerates the convergence rate. The results show that our model outperforms other methods at different SNRs and various numbers of BS antennas.
Quora Insincere Questions Classification Using Attention Based Model

The online platform has evolved into an unparalleled storehouse of information. People use various social question-and-answer websites such as Quora, Form-spring, Stack-Overflow, Twitter, and Beepl to ask questions, clarify doubts, and share ideas and expertise with others. An increase in inappropriate and insincere comments by users without a genuine motive is a major issue with such Q & A websites. Individuals tend to share harmful and toxic content intended to make a statement rather than look for helpful answers. In the world of natural language processing (NLP), Bidirectional Encoder Representations from Transformers (BERT) has been a game-changer. It has dominated performance benchmarks and thereby pushed the limits of researchers’ ability to experiment and produce similar models. This resulted in improvements in language models by introducing lighter models while maintaining efficiency and performance. This study utilized pre-trained state-of-the-art language models for understanding whether posted questions are sincere or insincere with limited computation. To overcome the high computation problem of NLP, the BERT, XLNet, StructBERT, and DeBERTa models were trained on three samples of data. The metrics proved that even with limited resources, recent transformer-based models outscore previous studies with remarkable results. Amongst the four, DeBERTa stands out with the highest balanced accuracy, macro, and weighted f1-score of 80%, 0.83 and 0.96, respectively.
Leveraging Low Code Development of Smart Personal Assistants: An Integrated Design Approach with the SPADE Method

Smart personal assistants (SPAs), such as Alexa for example, promise individualized user interactions owing to their varying interaction possibilities, knowledgeability, and human-like behaviors. To support the widespread adoption and use of SPAs, organizations such as Google or Amazon provide low code environments that support the development of SPAs (e.g., for Google Home or Amazon's Alexa). These so-called low code platforms enable domain experts (e.g., business users without programming skills or experience) to develop SPAs for their purposes. However, using these platforms alone does not guarantee a useful and good conversation with novel SPAs due to non-intuitive design choices. Following a design science research approach, we propose the Smart Personal Assistant for Domain Experts (SPADE) method to address the missing link. This method supports domain experts in the development and contextualization of sophisticated SPAs for various application scenarios and focuses especially on conversational and anthropomorphic design steps. Our proof of concept and proof of value results show that SPADE is useful for supporting domain experts to create effective SPAs in different domains beyond private set-ups.
Edge-YOLO: Lightweight Infrared Object Detection Method Deployed on Edge Devices

Existing target detection algorithms for infrared road scenes are often computationally intensive and require large models, which makes them unsuitable for deployment on edge devices. In this paper, we propose a lightweight infrared target detection method, called Edge-YOLO, to address these challenges. Our approach replaces the backbone network of the YOLOv5m model with a lightweight ShuffleBlock and a strip depthwise convolutional attention module. We also applied CAU-Lite as the up-sampling operator and EX-IoU as the bounding box loss function. Our experiments demonstrate that, compared with YOLOv5m, Edge-YOLO is 70.3% less computationally intensive, 71.6% smaller in model size, and 44.4% faster in detection speed, while maintaining the same level of detection accuracy. As a result, our method is better suited for deployment on embedded platforms, making effective infrared target detection in real-world scenarios possible.
Robust semi-supervised multi-view graph learning with sharable and individual structure

The construction of a high-quality multi-view consensus graph is key to graph-based semi-supervised multi-view learning (GSSMvL) methods. However, most existing GSSMvL methods explore sample relationships in the original multi-view feature space, which obtains a contaminated graph that cannot reveal the underlying manifold structure of the samples. Moreover, traditional GSSMvL methods fail to explore the diverse structures of multi-view features, which may lose their complementary information and lead to a suboptimal graph. In this paper, we propose a novel unified robust semi-supervised multi-view graph learning framework based on the sharable and individual structure (RSSMvSI), which can eliminate the influence of noise and exploit the knowledge of multi-view data in a reasonable manner. Specifically, we first learn clean data by manipulating sparse noise with l2,1 norm. We then simultaneously explore the sharable and individual self-representation subspace on the learned clean multi-view data. The key point is that noisy data does not participate in subspace learning, which improves the robustness of the proposed method. By constructing the optimal consensus graph with the learned sharable and individual subspace, RSSMvSI can better utilize the complementary information of multi-view data and approximate the manifold structure of samples. To the best of our knowledge, this is the first attempt to learn the self-representation subspace on recovered multi-view clean data. Extensive experiments on various real-world multi-view datasets demonstrate the superiority and robustness against state-of-the-art methods.
A small sample data-driven method: User needs elicitation from online reviews in new product iteration

Eliciting user needs from mass online reviews is playing a significant role in the product iteration process. Efficient user needs elicitation does achieve considerable benefits for maintaining higher competitiveness and a speedier lifecycle. However, there is inevitably an online review scarcity about new products due to the short time on the market and low buyer recognition compared with commonly used products. This paper proposes a small sample data-driven method for user needs elicitation from online reviews in new product iteration. In the first stage, a scraped initial online review dataset is pre-processed roughly to improve the data quality. And then, reviews are classified into multiple categories according to different topics using ERNIE. In the second stage, each topic-based dataset is reprocessed in detail. Thereafter, the key user needs set is determined and facilitated by extracting key product information phrases from every single dataset using improved SIFRank. Moreover, the case study of a smart cat feeder is carried out to demonstrate the feasibility and potential of the ERNIE-ISIFRank methodology. Finally, comparative experiments are conducted to verify the advantages of the proposed method which is primarily based on the pre-trained language model to enhance the deep understanding of the semantics of online reviews. The experimental results confirm that the proposed method can assist in identifying key user needs with high efficiency.
Style Uncertainty Based Self-Paced Meta Learning for Generalizable Person Re-Identification

Domain generalizable person re-identification (DG ReID) is a challenging problem, because the trained model is often not generalizable to unseen target domains with different distribution from the source training domains. Data augmentation has been verified to be beneficial for better exploiting the source data to improve the model generalization. However, existing approaches primarily rely on pixel-level image generation that requires designing and training an extra generation network, which is extremely complex and provides limited diversity of augmented data. In this paper, we propose a simple yet effective feature based augmentation technique, named Style-uncertainty Augmentation (SuA). The main idea of SuA is to randomize the style of training data by perturbing the instance style with Gaussian noise during training process to increase the training domain diversity. And to better generalize knowledge across these augmented domains, we propose a progressive learning to learn strategy named Self-paced Meta Learning (SpML) that extends the conventional one-stage meta learning to multi-stage training process. The rationality is to gradually improve the model generalization ability to unseen target domains by simulating the mechanism of human learning. Furthermore, conventional person Re-ID loss functions are unable to leverage the valuable domain information to improve the model generalization. So we further propose a distance-graph alignment loss that aligns the feature relationship distribution among domains to facilitate the network to explore domain-invariant representations of images. Extensive experiments on four large-scale benchmarks demonstrate that our SuA-SpML achieves state-of-the-art generalization to unseen domains for person ReID.
Current status of machine learning in thyroid cytopathology

The implementation of Digital Pathology has allowed the development of computational Pathology. Digital image-based applications that have received FDA Breakthrough Device Designation have been primarily focused on tissue specimens. The development of Artificial Intelligence-assisted algorithms using Cytology digital images has been much more limited due to technical challenges and a lack of optimized scanners for Cytology specimens. Despite the challenges in scanning whole slide images of cytology specimens, there have been many studies evaluating CP to create decision-support tools in Cytopathology. Among different Cytology specimens, thyroid fine needle aspiration biopsy (FNAB) specimens have one of the greatest potentials to benefit from machine learning algorithms (MLA) derived from digital images. Several authors have evaluated different machine learning algorithms focused on thyroid cytology in the past few years. The results are promising. The algorithms have mostly shown increased accuracy in the diagnosis and classification of thyroid cytology specimens. They have brought new insights and demonstrated the potential for improving future cytopathology workflow efficiency and accuracy. However, many issues still need to be addressed to further build on and improve current MLA models and their applications. To optimally train and validate MLA for thyroid cytology specimens, larger datasets obtained from multiple institutions are needed. MLAs hold great potential in improving thyroid cancer diagnostic speed and accuracy that will lead to improvements in patient management.
Ebublio: Edge-Assisted Multiuser 360° Video Streaming

As one of the most important manifestations of virtual reality (VR), 360° panoramic videos in recent years have experienced booming development due to the desire for immersive and interactive experiences. Compared to traditional videos, 360° videos are featured with uncertain user Field of View (FoV), more sensitive delay tolerance, and much higher bandwidth requirement, bringing unprecedented challenges to 360° video streaming. Meanwhile, the development of 5G and mobile edge computing starts to pave the way for high-bandwidth low-latency video streaming. Some preliminary works focus on either individual FoV prediction or multiuser Quality of Experience (QoE) oriented cache strategy design, while how to design a holistic solution toward optimizing the overall user QoE with considerations over fairness and long-term system cost remains a nontrivial problem. In this article, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ebublio</monospace> , a novel intelligent edge caching framework to address the aforementioned challenges in 360° video streaming. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ebublio</monospace> consists of a collaborative FoV prediction (CFP) module and a long-term tile caching optimization (LTO) module to jointly optimize the long-term user QoE and system cost. The former module integrates the features of video content, user trajectory, and other users’ records for combined prediction. The latter one employs the Lyapunov framework and a subgradient optimization approach toward the optimal caching replacement policy. Our trace-driven evaluation demonstrates the superiority of our framework, with about 42% improvement in FoV prediction, and 36% improvement in QoE at similar traffic consumption.