Reviewerly/exHarmony huggingface dataset:
Novel few-shot learning based fuzzy feature detection algorithms

The Internet of Things (IoT) has significantly enhanced various aspects of our daily lives, including security, health, education, and energy efficiency, among others. Within the realm of IoT, image classification stands as a pivotal technique that has achieved notable success in domains such as facial recognition within security and scene recognition in transportation for traffic analysis. Nonetheless, the challenge emerges when tackling classification tasks with only limited labeled samples available for each category. Conventional machine learning techniques often struggle to attain satisfactory classification results under such circumstances. To address this issue, the concept of few-shot learning has emerged, aiming to achieve effective classification using only a small number of labeled samples. State-of-the-art few-shot learning models have introduced novel frameworks to tackle this problem. However, the inherent ambiguity and uncertainty within data often hinder the performance of classification methods. To overcome this limitation, this paper proposes the integration of fuzzy learning with few-shot learning in the context of feature extraction. The objective is to mitigate data fuzziness and enhance model performance. Leveraging a fuzzy extraction algorithm, we introduce fuzzy prototype networks and a fuzzy graph neural network with fuzzy reasoning. These frameworks are designed to analyze noisy and uncertain data, utilizing convolutional neural networks for feature extraction and applying fuzzy reasoning to capture ambiguity representations for features within each fuzzy set. The SoftMax function is then normalized to serve as a feature weight, effectively constraining the original feature vector. The effectiveness and efficiency of our proposed model are demonstrated through experimental evaluations conducted on various public datasets. The results showcase the model’s capability in addressing the challenges posed by limited labeled data and data uncertainty, thus reaffirming its potential in enhancing the performance of image classification tasks within the IoT context.
Comprehensive System for Facilitating Professional Growth by Linking Mentees and Mentors in Information Technology

This paper presents an innovative system designed to enhance connections between individuals seeking mentorship (mentees) and experienced professionals (mentors) in the field of Information Technology (IT). The system consists of two separate websites: one for mentees and mentors to connect and collaborate, and another for administrators to manage user accounts and handle payment transactions. The technical core of this system focuses on a robust face recognition mechanism for mentor verification. This mechanism involves processes such as facial detection, vector representation, and face comparison using the Euclidean distance metric. Its primary purpose is to verify the authenticity and expertise of mentors, ensuring that only genuine and high-quality mentors are retained in the system. Furthermore, the system introduces an innovative feature that enables the creation of collaborative workspaces for both mentors and mentees. These workspaces offer a flexible environment for interactive activities, including real-time chat, video calls, recording capabilities, and seamless file sharing. To demonstrate the system's effectiveness, we conducted comprehensive experimental evaluations. The results conclusively show that the system excels at facilitating successful connections between mentees and qualified mentors in the field of IT.
Visual Knowledge Discovery from Public Transit Performance Data

Public transit is an important component of the day-to-day activities of many people. It provides a cost-effective and convenient way for individuals to commute to work, school, and other destinations. Public transit bus is a vital mode of transportation for students, as it enables them to commute to and from their educational institutions. Delays in bus schedules can have severe consequences, such as missing exams, meetings, and other important engagements. Hence, in this paper, we present a visual knowledge discovery solution to mine public transit bus on-time performance data and visualize the mined results. In particular, visual representation (e.g., graphs, time plots) from our visual knowledge discovery process help reveal factors contributing to bus delays in different neighborhood areas. This helps the service providers to improve their services, and thus enhance rider experience. Evaluation on real-life data from a Canadian city shows the practicality of our solution.
Subject Experiments with a Learning Support System for Grover's Algorithm

An appropriate quantum algorithm is needed for each problem to achieve the full performance of a quantum computer. It is necessary to understand the principles of quantum computation to implement quantum algorithms. Quantum computation simulators can assist in understanding the principles and behavior of quantum computation. Combining this with explanations of quantum algorithms using visualization techniques and interactions can further aid learning. In this study, to support the understanding of quantum computation, a difficult concept for beginners, we develop an interactive system in which diagrams and graphs are added to a quantum computation simulator to support the explanation and understanding of qubits and quantum algorithms. To verify the effectiveness of the interaction in supporting learning, a subject experiment is conducted to compare the amount of knowledge understood by a group of novice quantum computer students who learned using the developed system with a group who learned using paper-based materials.
A novel policy network with a soft reward strategy for knowledge graph reasoning

Path reasoning over knowledge graph is an important method carried on knowledge graph completion. Recently, there are bright performance on reinforcement learning applications for path reasoning, which range from the initial reinforcement learning framework (Deeppath) to Bayesian reinforcement learning framework (GaussianPath) and so on. It is still being big challenges on the reduction of spurious search trajectories caused by invalid actions and the low probability of reaching positive rewards. Thus, a Gate Policy-Guided Path Reasoning (GPGPR) model framework is proposed for solving the knowledge graph completion. It mainly consists of a policy-guided graph search algorithm and a novel soft reward function. Specifically, The policy-guided graph search algorithm is implemented by a gate policy network with invalid action masking. It can filter spurious search trajectories caused by invalid actions to obtain more suitable action distributions. The novel soft reward function employs target embedding dropout mechanism to determine whether a target entity is dose to the ground truth to make higher positive rewards for obtaining the expected accumulated reward. In this experiments, we select MRR and Hits@N as metrics and conduct extensive contrast experiments between the proposed model and basdine models on open knowledge base datasets.
A GAN-based Relative Radiometric Correction Model of Remote Sensing Data

While there are many traditional methods for radiometric correction of multitemporal remote sensing images, deep learning methods are quite rare. Recently it has witnessed a rapid development of computer vision, many style transfer and domain transfer methods have given us great inspiration. However, traditional methods have problem in achieving uniform effect of radiometric correction, while style transfer methods struggle to realize control of local areas and transfer degree, especially when dealing with complicated remote sensing data. Thus, a generative adversarial network (GAN)-based relative radiometric correction method combined with deep style transfer (NormGAN) is proposed. A cycle-consistent structure is applied for bi-directional domain transfer of non-corresponding regions. VGG19 network is used to obtain feature map for the calculation of content loss and style loss. Meanwhile, rough masks of landcover enable transfer within each class and weight matrix is put forward to control transfer degree. Our results indicate that the proposed NormGAN is capable and effective, which has much superiority over other methods.
Contextual Advertising Strategy Generation via Attention and Interaction Guidance

Digital advertising has become one of the most important aspects of modern marketing, as it allows businesses to reach larger audiences with greater precision, controllable cost, and measurable feedback. However, the process of designing effective advertising strategies relies heavily on human experts and thus remains suboptimal. In this paper, we propose a novel method for Contextual Advertising Strategy Generation via Attention and InteRaction Guidance (ASGAR) that leverages transformers and a soft contrastive learning approach to optimize campaign performance. An advertising strategy is a combination of multiple targeting options, and its performance is tied strictly to the combination as a whole. This makes the exploration of the high combinatorial space infeasible and autoregressive methods inefficient. Therefore, constraints of non-combinatorial exploration and non-autoregressive generation have to be met. To the best of our knowledge, this is the first method that satisfies all constraints while also outperforming the previous methods. We compare our results with state-of-the-art methods on a public data set and with human experts in a company-deployed environment. We show that our method can effectively generate high-performance advertising strategies with better stability and controllable exploration.
AdaSub: Stochastic Optimization Using Second-Order Information in Low-Dimensional Subspaces

We introduce AdaSub, a stochastic optimization algorithm that computes a search direction based on second-order information in a low-dimensional subspace that is defined adaptively based on available current and past information. Compared to first-order methods, second-order methods exhibit better convergence characteristics, but the need to compute the Hessian matrix at each iteration results in excessive computational expenses, making them impractical. To address this issue, our approach enables the management of computational expenses and algorithm efficiency by enabling the selection of the subspace dimension for the search. Our code is freely available on GitHub, and our preliminary numerical results demonstrate that AdaSub surpasses popular stochastic optimizers in terms of time and number of iterations required to reach a given accuracy.
Exploring the Hybrid Neural Network and Attention Mechanism for Classification of Social Bias

Neural Network models are considered universal learning due to their powerful ability to solve text classification with different data types. Hybrid neural networks in text classification using bidirectional Long Short-term Memory (BiLSTM), Convolutional Neural Network (CNN) and bidirectional Gated Recurrent Unit (BiGRU) have been widely used. In this study, we conducted a preliminary study on text classification of social bias data by combining the CNN, Bidirectional LSTM, Bidirectional GRU, and attention mechanism. The output from bidirectional architectures are given a distinct focus through attention mechanisms employed in both layers. Social bias is opinionated data which is often demonstrated in stereotypical and prejudiced behavior, presenting unfair decision including hatred and emotion about people's identity such as race, religion, ethnicity and gender. Social bias is considered under low resource scenario whereby the data size is limited and subjective. The result shows the proposed hybrid model gives promising results and good potential in learning a small size of data compared to single models.
A Fitness Movement Evaluation System Using Deep Learning

This paper proposes a fitness movement evaluation system using deep learning. The system uses a deep convolutional neural network (CNN) to extract features from pictures of fitness movements. The features are then used to classify the movements into different categories. The system is evaluated on a dataset of pictures of fitness movements. The results show that the system can accurately classify the movements into different categories. The system is designed to provide feedback to users on their fitness movements. The proposed system is a valuable tool for fitness enthusiasts. The main contribution of this paper is to propose a way to give users a score for their fitness movement. It can help users improve their fitness and track their progress over time. The system is also a valuable tool for fitness professionals. It can help professionals develop new fitness programs and provide feedback to their clients.
End-to-End Speech Keyword Spotting System

The purpose of speech keyword spotting is to detect a set of predefined keywords from a continuous speech signal stream. Based on the research on end-to-end technologies in the field of deep learning, this paper designs and implements an end-to-end speech keyword spotting algorithm, which has a wide range of applications in various fields, such as smartphones and automobiles. The algorithm first trains an acoustic model based on a deep neural network, which receives the acoustic features and outputs the posterior probability of the wake-up word. Then, the posterior probability is smoothed to obtain the confidence score of the wake-up word. Through the above process, the traditional decoding process can be avoided effectively. In addition, this paper compares various neural network structures of acoustic model, such as the time-delay neural network (TDNN) and the factorized time-delay neural network (TDNN-F). Through comparative experiments by controlling variables, it is verified that the proposed end-to-end speech keyword spotting algorithm has competitive performance compared with the other popular technologies.
Prediction of IDC Breast Cancer by the Application of Transfer Learning with an Ensemble Method

Cancer mainly is the result of uncontrollable changes in genes. Hence breast cancer is a result of the unstoppable growth of breast cells. A common type of breast cancer is Invasive Ductal Carcinoma (IDC) also popular as Infiltrating Carcinoma. Detection of IDC is necessary and precise identification is expected. If the diagnosis is accurate then the patient will be able to get the necessary treatment needed. For the detection of IDC positive or negative, our proposed methodology employs transfer learning by using ResNet-50, Inception V2, and EfficientNet deep learning models with an ensemble method. The dataset used here consisted of 162 whole-mount slide images of Breast Cancer specimens. From that, 277,524 patches of size 50 × 50 were extracted (198,738 IDC negative and 78,786 IDC positive). This dataset contains a huge number of histopathology images and is passed through the models for training, testing, and validation. In the training section, 10-fold cross-validation was used. This method gives better performance than any single method as it uses an ensemble method with all three mentioned models. The accuracy of separate models is compared with the ensemble methods result. Having made the ensemble approach, we got an accuracy of 94.047% which is higher than the models performing separately. Moreover, the other performance parameters also showed improvement in the ensemble approach. With this satisfactory result, this approach can be of great use in detecting IDC breast cancer.
Receiver-Agnostic Radio Frequency Fingerprint Identification via Feature Disentanglement

The Radio Frequency Fingerprint Identification (RFFI) technique utilizes the subtle and unintentional modulation present in the transmitted RF waveform, caused by the non-ideal characteristics of the device, to uniquely identify an emitter. RFFI has recently gained significant attention, and deep learning (DL) approach has achieved superior performance. In this paper, we study the DL-based RFFI with an emphasis on the fingerprint contamination by receivers. Specifically, during reception, the receivers’ fingerprints are inevitably introduced, and different receivers affect emitters’ fingerprints differently, thereby degrading recognition performance when the RFFI recognition model is deployed at different receivers. To mitigate the receivers’ effect, we propose a receiver-independent recognition model based on the idea of feature disentanglement. With the aid of some carefully designed model structure, loss functions and training strategy, the proposed model learns a feature space, in which the emitters’ features are disentangled from the receivers’, through multi-receiver’s training data. When deploying the model on a new receiver, the emitter-dependent features are exploited to achieve receiver-independent identification. Experiments on two real-world datasets demonstrate that our method significantly outperforms the baseline method without considering fingerprint contamination.
ReciPic: A Tool for Generating Infographic from Recipe Procedure Text

Food is one of the basic human needs and an important part of human culture. With the development of Internet, it has become increasingly common for people to share recipes on recipe websites. However, in most cases, these recipes contain only text information about the cooking procedures. It is difficult for users to have a big picture about the food materials and cooking methods at a glance. In this paper, we propose a tool for generating infographic from recipe procedure text, to supplement the pure text information on the recipe websites. This tool accepts one step text of the cooking procedures, and then segments the text stream into appropriate units. After that, it extracts the food material, cooking tool, cooking action segmentation and label them with F, T, Ac (F: Food material, T: Cooking tool, Ac: Cooking action) respectively. Then, it analyzes the grammatical relationship between the extracted words. Finally, it matches the words with prepared pictures and place them with appropriate positions in an infographic and represents it to users.
Course relevance analysis based on Word Mover’s Distance

With the progress of the times and the development of technology, the creation of artificial intelligence technology has brought a huge impact on the field of education. For the field of education, exploring curriculum relevance is one of its core elements. However, most of the traditional course relevance analysis is conducted from the perspective of course performance, ignoring the external factors that affect the performance such as test difficulty and student level. Since course text descriptions can describe a course more objectively than grades, it is necessary to apply text similarity analysis to course relevance research. The prevalent text similarity analysis is only achieved by word frequency statistics, ignoring the connection between textual contexts. In this context, this paper utilizes the Word Mover’s Distance (WMD) approach to analyze the relevance of course texts. The experimental results show the heat map of course relevance and demonstrate the superiority and effectiveness of the WMD method through the experimental comparison with the traditional text distance metric algorithm.
A Blockchain-ML Driven Freelancing Telemedicine Platform: Bangladesh Perspective

Telemedicine revolutionizes healthcare delivery via remote consultations, diagnosis, and treatment, leveraging tech-nologies like video conferencing and mobile apps. In Bangladesh, inadequate infrastructure and resources pose challenges. To over-come these, we propose an innovative freelancing telemedicine platform, integrating blockchain and machine learning. This platform incorporates gig functionality, informed by patient, doctor, and medical student surveys, plus an analysis of existing telemedicine tools. Using the Test and Evaluation Master Plan (TEMP) method, our platform modernizes telemedicine by introducing gig functionality, simplifying freelancing for BM&DC ver-ified doctors who can manage private online clinics. Blockchain technology ensures the security of user data. Moreover, machine learning enhances the doctor-patient experience by suggesting suitable doctors. The Freelancing Telemedicine platform can potentially revolutionize healthcare, improving telemedicine accessibility, ensuring secure data sharing, and facilitating seamless doctor-patient communication.
Research on UAV Image Detection Method in Urban Low-altitude Complex Background

With the rapid development of urban air traffic, more and more Unmanned Aerial Vehicles are appearing in cities. The government focuses on the need for safety-oriented unmanned aviation to strengthen the regulatory capability, and to form cooperative air-ground object active surveillance, as well as passive object surveillance supplemented by visible light detection. The Unmanned Aerial Vehicle detection method for urban low-altitude complex backgrounds based on YOLOv7x is proposed and to ensure the safety of flight activities. We construct an experimental dataset which is called UAVULCB (Unmanned Aerial Vehicle dataset used in Urban Low-altitude Complex Background). Extensive experiments on the UAV-ULCB validate that this method achieves 95.8% mAP50 with an inference speed of 30 frames/second. Finally, the heat map by the Grad-CAM is used to show the most concerned parts and increase the interpretability of the model. This method provides a strong technical support for urban air traffic supervisory authorities, which can be safeguarded the safety of urban Unmanned Aerial Vehicle flight activities together with other supervisory methods.
Algorithm and Processor Architecture for the Implementation of the Minimum Filtering Technique in Complex-Valued CNNs

In this paper, we present a resource-efficient algorithmic solution regarding the fully parallel hardware implementation of the basic filtering operation performed in the complex-valued convolutional neural networks (CNNs). In fact, this basic operation computes the inner products of adjacent complex-valued vectors formed by a sliding time window from the current data stream with the impulse response vector of a 3-tap finite impulse response (FIR) filter. We adapt Winograd's minimal filtering algorithm for a case of the complex-valued data and applied it to develop a fully parallel hardware-oriented algorithm for implementing basic filtering operations in complex-valued CNNs. We also additionally apply the well-known Gauss's trick for multiplying complex numbers, which replaces 4 multiplications and 2 real additions with real 3 multiplications and 3 real additions. A general view of the architecture of the processor unit for the implementation of this operation is also given. A fully parallel hardware implementation of the proposed processor unit provides 50% savings in the number of embedded multipliers compared to a fully parallel hardware implementation of the naive calculation method.