Reviewerly/exHarmony huggingface dataset:
A Fast Level-Set Curve-Flow Formulation by Logarithmic Step-Size Control for Brain Ultrasound Image Segmentation

Segmentation of different regions in intra-operative brain ultrasound (iBUS) images is often required for assiting the neuro-surgeon. Traditional level-set and active contour-based semi-automatic image segmentation approaches suffer from low accuracy and slow convergence. This paper presents a novel semi-automatic level-set approach for segmenting hyper-echoic (HE), hypo-echoic, and anechoic regions with minimal user intervention. Three hyper-echoic regions <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">longitudinal fissure, choroid plexus, and tumor</i> and two anechoic regions, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ventricle and resection cavity</i> are segmented using a patch-based level-set approach. This method is a combination of three procedures: a) level-set based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unidirectional level-set curve flow</i> (ULSCF), b) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bidirectional level-set curve flow</i> (BLSCF) using a logarithmic patch size control, and c) cubic B-spline-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contour smoothing</i> . Zero level-set curve is derived using a patch-based intensity thresholding method of the desired region. The imperfection on the blocky edges produced during the patch-based ULSCF, are minimized using the BLSCF step that uses local region splitting approach. Slope and curvature discontinuities of the resulting boundaries after BLSCF are eliminated using a cubic B-spline based contour smoothing. The performance of the proposed method outperforms other state-of-the-art level-set and active contour methods, and the desired result is obtained within reasonable time required for online monitoring during surgery.
Word Sense Disambiguation Based on RegNet With Efficient Channel Attention and Dilated Convolution

Word sense disambiguation (WSD) is one of key problems in field of natural language processing. Ambiguous word often has different meanings in different contexts. WSD is the process of determining semantic category of ambiguous word according to its context. It has a great impact on machine translation, speech recognition, topic detection, search engine, text classification, emotion analysis and so on. It is important for WSD task to extract effective discriminative features and design high-quality disambiguation models. This paper proposes a WSD method based on RegNet with Efficient Channel Attention (ECA) and dilated convolution (ECADCRegNet). Ambiguous word is viewed as the center, and two units on the left and two units on the right of ambiguous word are selected. Then, word, part of speech and semantic categories of these four units are used as disambiguation features. Firstly, disambiguation features are vectorized and the result is input into RegNet space. Secondly, dilated convolution is used to extract discriminative features from words, parts of speech and semantic categories for constructing feature matrix. And ECA is introduced into the feature extraction layer. Thirdly, softmax function is used to calculate the probability of ambiguous word under each semantic category and the category with the largest one is selected. Training corpus of SemEval-2007: Task#5 is adopted to optimize the proposed network. Test corpus of Semeval-2007: Task#5 is used to test the performance of the proposed network. Experimental results show that average accuracy of the proposed method achieves 81.83%, which is higher than those of other methods.
Design Recommendations for Gate Security Systems and Health Status: A Systematic Review

Gate security systems use authentication methods to operate hardware components that grant or deny access to restricted areas. Each context has specific requirements to determine user admissibility. There are currently no design recommendations available for these systems despite their significance. Most research proposes designs based on their recommended authentication scheme without providing general guidance on constructing these systems. This study follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and conducts a systematic literature review, combining recent research on smart gates. Studies published between 2016 and 2023 are analyzed and evaluated to identify their main components and authentication schemes. A total of 52 research papers published in various journals and conferences were collected. After conducting the review, three main design themes were identified: smartphones, tags, and biometrics. These themes were the focal point of the study. Out of all the designs, 66% only consider using one-factor authentication. These designs primarily rely on biometric-based methods. During the COVID-19 crisis, some designs used biometric authorization instead of identity authentication to incorporate health status. The focus was detecting whether the person wore a face mask and had a normal body temperature. Furthermore, the review reveals that most studies disregard the system’s hardware components and focus on authorization. Also, only 25% of the studies have conducted an implementation for their design and produced results evaluating their performance. The study concludes that a successful smart gate design must consider and balance cost, usability, and security. Furthermore, health status needs to be verified as an additional layer of protection after determining existing authentication requirements.
When Should Recommenders Account for Low QoS?

Content recommendation systems, also known as recommenders, are pervasive and have significant impact on user demands over the Internet. Platforms such as YouTube and Netflix constantly seek to improve their recommendation systems, to provide better quality of experience (QoE) for their users. QoE, in turn, depends on a multitude of factors, including the quality of recommendation (QoR), e.g., based on users histories and content categories, and the quality of service (QoS), e.g., measured by network delay and throughput. Even though QoS is key in a best-effort Internet, existing recommendation systems overlook it, resulting in recommendations that are suboptimal in terms of QoE. In this study, our goal is to devise a QoS-aware, QoE-friendly, content recommendation system and indicate its feasibility in the wild. For this purpose, we conducted an experiment with real users driven by the following question: When should recommenders account for low QoS? Each user is requested to evaluate pairs of videos, that vary in their contents and QoS levels. We experimentally determined category-dependent thresholds that determine the sensitivity of users with respect to QoS and QoR. Given the collected insights on QoS-aware recommendations, we considered our second research question: Can recommenders compensate for low QoS? We conducted experiments over the Internet, relying on YouTube API and network measurements tools, and report our findings on ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) the characterization of QoS and ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) the compensation for low QoS. Our measurements suggest that content far from the trends tends to be far from the user. We quantified the extent to which unpopular content tends to be served with a lower QoS and established a methodology to determine the relationship between content popularity and its physical proximity to users. Then, we verified that making requests a bit trendier can hit much closer content.
Exploring IoT-Blockchain Integration in Agriculture: An Experimental Study

The incorporation of Internet of Things (IoT) technology with agriculture has transformed several farming practices, bringing unparalleled simplicity and efficiency. This article explores the robust integration of IoT and blockchain technology(BIoT) in agricultural operations, offering insight into the resulting BIoT system’s design. This study investigates the potential benefits of merging the IoT and blockchain technologies in agriculture. A system for tracking plant growth using sensors and blockchain-integrated IoT has been developed and analyzed. Through empirical investigation, the report highlights greater efficiency, transparency, and security as significant benefits of BIoT architecture. A cost analysis is also conducted, showing that the system is cost-effective. The paper also proposes a mathematical formula for the calculation of infrastructure cost. It also looks at the problems of integrating BIoT in agricultural contexts, such as technological complications and the need for stakeholder participation. The findings indicate that the IoT can potentially revolutionize agricultural practices drastically, but its practical implementation would need careful consideration of technological, economic, and societal considerations. Empirical analysis is conducted to analyze the latency, throughput, and transaction scalability. The paper concludes with research suggestions, emphasizing the significance of interdisciplinary collaboration and the consequences of BIoT deployment in agriculture.
Image Colorization Using Color-Features and Adversarial Learning

In this study, we introduce an innovative image colorization method that not only improves color accuracy and realism but also addresses common issues found in existing methods, such as desaturation and color bleeding. Our proposed method features a novel component called the Color Encoder, which extracts intrinsic color features. Moreover, the proposed Color Encoder aligns essential color features systematically, drawn from a random normal distribution, with real colors. These aligned features are fused at the bottleneck and serve as the foundation for subsequent colorization. Complementing the Color Encoder is our Color Loss mechanism, which aims to align the extracted features from the Color Encoder with the ground-truth color features, enhancing overall color representation accuracy. We also employ a Conditional Wasserstein Generative Adversarial Network (CWGAN) architecture within the framework of a Generative Adversarial Network (GAN) to improve adversarial training and colorization accuracy. To enhance feature representation, we incorporate an attention mechanism at the bottleneck of each encoder layer, further refining our model’s ability to capture essential image details. Experimental results show that our approach significantly outperforms other state-of-the-art methods in terms of both realism and precision, striking a well-balanced performance.
Hybrid Swin Transformer-Based Classification of Gaze Target Regions

Inferring gaze targeting or gaze following is an effective method for understanding human behavior and intentions. To address errors caused by issues such as the disappearance of the eye image and head deflection occlusion in image capture, this paper adopts a non-intrusive appearance-based tracking technique using a binocular stereo vision camera to capture the face image and the head pose. It determines each gaze direction based on a single image frame. Furthermore, to handle head motion and view direction effectively and improve the classification and detection ability of the gaze target region, this paper proposes a hybrid structure for the Swin Transformer gaze target region classification method. Initially, both the ResNet50 model and Swin Transformer model are employed to extract facial image features, and subsequently, head pose features are fused to classify the gaze target area. The paper also compares the classification effects of different structural models. The analysis of the results demonstrates that the hybrid Swin Transformer model is more effective in classifying and detecting the gaze target region, achieving an accuracy rate of 90%. Lastly, the paper analyzes the gaze of flight trainees during flight missions using a heatmap, which serves as a foundation for subsequent analyses of pilot attention and operational intentions during flights.
Fine-Tuning Multimodal Transformer Models for Generating Actions in Virtual and Real Environments

In this work, we propose and investigate an original approach to using a pre-trained multimodal transformer of a specialized architecture for controlling a robotic agent in an object manipulation task based on language instruction, which we refer to as RozumFormer. Our model is based on a bimodal (text-image) transformer architecture originally trained for solving tasks that use one or both modalities, such as language modeling, visual question answering, image captioning, text recognition, text-to-image generation, etc. The discussed model was adapted for robotic manipulation tasks by organizing the input sequence of tokens in a particular way, consisting of tokens for text, images, and actions. We demonstrated that such a model adapts well to new tasks and shows better results with fine-tuning than complete training in simulation and real environments. To transfer the model from the simulator to a real robot, new datasets were collected and annotated. In addition, experiments controlling the agent in a visual environment using reinforcement learning have shown that fine-tuning the model with a mixed dataset that includes examples from the initial visual-linguistic tasks only slightly decreases performance on these tasks, simplifying the addition of tasks from another domain.
Unveiling Cryptocurrency Conversations: Insights From Data Mining and Unsupervised Learning Across Multiple Platforms

The rapid growth of the cryptocurrency market has led to an increasing interest in the subject. Cryptocurrency is now recognized as an asset, and laws and financial regulations have begun to emerge for supporting its practical use. As a result, it has become essential to perform data mining and attain knowledge from text data related to cryptocurrency. Previous studies have focused on analyzing data from a single source such as Twitter. However, there are unique insights to be gained from data across multiple platforms. In the present study, we utilized data mining techniques to extract insights from LexisNexis, Web of Science, and Reddit, representing the media, academia, and general public, respectively. Among unsupervised learning technologies, topic modeling was employed for the analysis. Topic modeling is a methodology that uncovers hidden meanings within the collected data. Among the diverse topic modeling techniques available, bidirectional encoder representations from transformers topic was chosen for the analysis. BERTopic considered to be state-of-the-art in the field of topic modeling. Dynamic topic modeling was employed to track changes in themes over time. Our experimental results reveal a tendency in the news to cover major events related to cryptocurrencies, such as regulatory developments and market trends. Academic papers, on the other hand, tend to focus on the technology behind cryptocurrencies and related research. Finally, social media conversations center more around information delivery from an investor’s psychological perspective, such as market sentiment and investment strategies.
Identification of Victims Wearing Vibrant Clothing using MATLAB

This paper presents an image-based target detection and integration algorithm for victim wearing vibrant clothing. The algorithm utilizes image processing and analysis techniques to automatically identify and locate victims, and combines the detected bounding boxes into a larger box. Experimental results validate the effectiveness and accuracy of the algorithm in various environments. The proposed algorithm can be applied in emergency response, disaster management, and surveillance systems, enhancing the efficiency of victim search and rescue operations
A Non-Isomorphic-Discriminated Inductive Graph-based Matrix Completion Model for High-Dimensional and Incomplete Data

High-dimensional and incomplete (HDI) data are generally used to analyze the potential links between real entities in the big data era. To perform this key task, an inductive graph-based matrix completion (IGMC) model is proposed to predict the link ratings w/o using the side information and pre-training. However, the isomorphic representation in the IGMC model only promotes the representation accuracy of the links with the adjacent ratings while does not consider the link representation accuracy of non-adjacent ratings. To address this issue, this paper proposes a local Non-Isomorphic-Discriminated IGMC (NID-IGMC) model to represent the links with distinct rating values more exactly. The main idea of the NID-IGMC model is two-fold. First, the NID-IGMC model proposes the definition of the non-isomorphic representation, constructs its representative functions and divides into three sub-categories. Second, the NID-IGMC model incorporates the non-isomorphic representation into the isomorphic representation and re-construct the model. Two kinds of experiments on industrial HDI matrices are performed, which indicate the high accuracy of the proposed NID-IGMC model.
MoMo Strategy: Learn More from More Mistakes

Training accurate convolutional neural networks (CNNs) is essential for achieving high-performance machine learning models. However, limited training data pose a challenge, reducing model accuracy. This research investigates the selection and utilization of misclassified training samples to enhance the accuracy of CNNs where the dataset is long-tail distributed. Unlike classical resampling methods involving oversampling of tail classes and undersampling of head classes, we propose an approach that allocates more misclassified training samples into the training process to learn more (namely, MoMo strategy), with ratios of 50:50 and 70:30 for the wrongly predicted and correctly predicted samples, respectively. Additionally, we propose incorporating a balanced sample selection method, whereby the maximum training sample per class in an epoch is assigned to address the long-tail dataset problem. Our experimental results on a subset of the current largest plant dataset, PlantCLEF 2023, demonstrate an increase of 1%-2% in overall validation accuracy and a 2%-5% increase in tail class identification. By selectively focusing on more misclassified samples in training, at the same time, integrating a balanced sample selection achieves a significant boost in accuracy compared to traditional training methods. These findings emphasize the significance of adding more misclassified samples into training, encouraging researchers to rethink the sampling strategies before implementing more complex and robust network architectures and modules.
MAEDefense: An Effective Masked AutoEncoder Defense against Adversarial Attacks

Recent studies have demonstrated that deep neural networks (DNNs) are vulnerable to attacks when adversarial perturbations are added to the clean samples. Reconstructing clean samples under the premise of inputting adversarial perturbations is a challenging task. To address this issue, this paper proposes a Mask AutoEncoder Defense (MAEDefense) framework to counter adversarial attacks. Firstly, the adversarial sample is divided into two complementary masked images. Secondly, the two masked images with carefully crafted adversarial noise locations are reassigned to non-adversarial noise locations. Finally, the two reconstructed images are pixel-wise fused (weighted average) to obtain a "clean image". The proposed method requires no external training and is easy to implement. Experimental results show that the proposed method significantly defends against white-box attacks and black-box transferable attacks compared with state-of-the-art methods.
Improved Data2vec with Soft Supervised Hidden Unit for Mandarin Speech Recognition

Speech pre-training method has shown great success in learning useful and general latent representations from large-scale unlabeled data. In order to further improve the performance of self-supervised learning method for specific downstream tasks, an improved approach based on data2vec framework with soft supervised hidden unit is proposed. To take full advantage of the labeled data from downstream task, a supervised model is firstly trained to extract supervised hidden unit. And then based on data2vec, an extra Bert-like prediction task with soft cluster distance is introduced to match the downstream task and avoid unnecessary information loss. The proposed method can form a virtuous circulating utilization pattern for the downstream labeld data. Experiments on the small open-source Mandarin speech corpus AISHELL-2 and large private-source Mandarin speech corpus TRANS-L tasks show that our method can achieve relative character error rate reductions of 13.2% and 5.2% respectively when pre-trained on AISHELL-2 and TRANS-L corpus compared with data2vec framework.
ASF-LLRDA: Locality-regularized Linear Regression Discriminant Analysis with Approximately Symmetrical Face Preprocessing for Face Recognition

Face recognition is a crucial task in numerous applications, but it has difficulties because of the high-dimensional nature of facial photos, limited sample sizes, variations in illumination, and facial expressions. This paper presents a novel face recognition approach to overcome these challenges by combining the advantages of the Approximately Symmetrical Face (ASF) preprocessing strategy and the Locality-regulated Linear Regression Discriminant Analysis (LLRDA) method. The proposed method, called ASF-LLRDA, makes use of ASF to generate axis-symmetric face images for reducing the impact of illumination and variations in facial expressions, followed by LLRDA to extract discriminative features and project the data into a lower dimensional space. Locality-regulated Linear Regression (LLRC) is further utilized as the classifier. Experimental results on the Yale-B face dataset demonstrated the superiority of the proposed method compared to the baselines.
Badminton Self-Training System Based on Virtual Reality

In sports games, quick sensory processing and attention are crucial for athletes to win. Therefore, players need to strengthen their perceptual response and movement coordination through iterative training to increase their chances of winning. However, conventional badminton training is limited by the availability of training venues and coaches. Additionally, training materials are imprecise and unstable due to human factors, and players cannot effectively analyze their strengths and weaknesses from training results.This study proposes a virtual reality badminton self-training system to solve these training problems, including the lack of training venues and coaches, imprecise training materials, and ineffective analysis of strengths and weaknesses. As a result, the proposed system provides players with an effective evaluation of their training performance under stable and precise training processes. Players can identify their shortcomings and receive constructive suggestions for improvement.