Reviewerly/exHarmony huggingface dataset:
Student Pose Detection Algorithm for the Classroom Environment Based on Improved YOLOv7

The field of smart education is experiencing rapid development in artificial intelligence and deep learning technologies. Researchers are increasingly focusing on classroom intelligence and informatization. The use of classroom monitoring has become popular, making it valuable to automatically gather information about students' learning status through cameras. While pose estimation techniques have been extensively studied in recent years, applying these methods in different school and classroom scenarios with diverse camera positions remains challenging. Unlike pose estimation methods, we consider pose detection of students in classrooms as a distinct problem of object detection. However, the monitoring data in the classroom scene presents challenges, such as similar categories, small object sizes, and severe occlusion. Achieving a balance between accuracy and speed in pose detection is often difficult. To address these problems, we propose an attitude detection algorithm that combines feature fusion with a one-stage object detection framework. Firstly, we utilize the YOLOv7 algorithm framework to preprocess the image and improve the data before feeding it into the network. Secondly, we introduce a feature extraction module that combines self-attention and convolution into the network, allowing the algorithm to quickly extract and prioritize features. Finally, we incorporate a feature fusion enhancement module multiple times into the network structure to obtain multi-scale features and enhance the model’s learning ability. Experimental results demonstrate that our proposed method exhibits a slight advantage over other excellent object detection methods when applied to real classroom pose datasets.
Label Hierarchy Alignment for Improved Hierarchical Text Classification

Effective text and label representations are crucial for accurate predictions in Hierarchical Text Classification (HTC). However, existing methods face challenges in capturing relevant label representations due to the discrete nature of labels and the complexities of their hierarchical relationships. To address these challenges, we propose Label Hierarchy Alignment (LHA), which ensures that learned label representations conform to the hierarchical label space. In this paper, we present two LHA approaches. The first is adversarial label alignment, which utilizes adversarial learning to enforce a prior distribution on label embeddings. This guides the model to adjust label embeddings in accordance with the hierarchical structure. The second approach is contrastive label alignment, which uses contrastive learning to identify semantic similarities and differences between label nodes across the hierarchy. This enriches the label embeddings with semantic depth, thereby enhancing their alignment with the hierarchical structure. By explicitly addressing various hierarchical aspects, both approaches generate refined label representations that align more effectively with the label hierarchy. We implemented LHA on the current state-of-the-art model and evaluated its performance on two benchmark datasets. The experiment results show that performing LHA improves HTC performance.
System for Urban Construction and Model Optimization Based on Machine Learning Algorithms

Urban construction plays a vital role in the development of modern society, has a profound impact on the quality of life of residents, and promotes sustained economic growth. However, in the face of many complex and changeable factors in urban development, traditional planning methods often seem inadequate. This paper discusses the application of machine learning (ML) algorithm in urban construction and model optimization. The urban construction model optimization system based on ML algorithm makes full use of historical and real-time data as well as external factors and expert knowledge to automatically adjust model parameters and design schemes, thus improving the efficiency and quality of urban construction. This system shows significant advantages in population forecasting, land use optimization and traffic flow forecasting. Compared with traditional methods, it can provide more accurate population prediction results, more optimized land use planning and more accurate traffic flow prediction. These advantages help to enhance the scientific and accurate urban planning, and ultimately promote the sustainable development of cities. In terms of traffic flow prediction, the performance of ML algorithm is also very good. The mean square error of the algorithm is only 1200.5, which indicates that the average error between the predicted results and the actual traffic flow is very small. At the same time, the Mean absolute error is 65.4, which means that the error between the predicted value and the actual value of the ML algorithm is small. An accuracy of 81.5% further demonstrates the system's superior accuracy in traffic flow prediction. These data and results together demonstrate the important role of ML algorithm in urban construction and planning, and provide strong support and guidance for the sustainable development of cities.
Augmented Reality Enabled Internet of Things for Smart Visualisation in Applications

Augmented reality(AR) is a vast field which enlarges the capabilities of various technological areas. Even though AR has proven to be advantageous in various areas, mainly by eliminating cognitive overload and providing a visual experience, it has a major drawback of requiring internet connectivity in most applications. Internet of Things(IoT) is associated with AR to upgrade existing IoT technology to another level. Considering home automation(which incorporates IoT), when coerced with AR provides remote access to the network, contactless switching(simply, by hovering over virtual buttons) and includes additional features like temperature, humidity detection, etc. AR is an emerging technology which is rapidly developing. When AR is used with novel cloud platforms like Sinric Pro, Blynk, ThinkSpeak, etc., exploration of a lot of new features takes place. This paper describes the concept of AR integrated with home automation which uses IoT and Sinric Pro. Along with this, it gives the idea of using augmented reality in real life. Visual representation of cards, books, distance between two objects, heart rate, moisture content in soil and real-time weather is given by augmented reality. Using these technologies in the existing system enhances human capabilities, creates unique customer experiences and provides real time analysis. It discusses an approach to inculcate AR in IoT to increase the applications. The analysis of AR framework incorporating IoT has been done in order to use it for real life applications.
Blockchain in Healthcare Systems

Advancements in technology are reshaping the future of healthcare data management and systems. The imperative for secure, efficient, and scalable Electronic Health Record (EHR) solutions has spurred innovation. Traditional healthcare data management, relying on cloud-based and client-server systems, grapples with issues like single points of failure, data privacy concerns, centralization, and vulnerabilities. Blockchain technology presents a promising solution, offering replication, security, and privacy features that can address these challenges. However, the healthcare sector requires a blockchain that is even more secure, fault-tolerant, traceable, adaptable, and privacy-focused. This paper's core objective is to examine how blockchain technology can be integrated into healthcare systems, with a focus on addressing its inherent challenges. It delves into issues faced by blockchain in healthcare and examines potential solutions. These solutions encompass techniques such as clustering, sharding, improved consensus protocols, and the use of smart contracts to enhance the scalability, security, and privacy of healthcare data management systems.
Towards Sustainable Video Streaming: Evaluation of AI Approaches for Content Aware Encoding

With the increasing demand for video content and the associated rise in energy consumption, there is a critical need to develop sustainable solutions for digital media delivery. This paper introduces an AI-based video encoding solution designed to reduce bitrates and minimize energy consumption in online video streaming. Traditional encoding methods often result in high bitrates and increased environmental impact. Our approach leverages machine learning algorithms and AI-based approaches to analyze video content and dynamically adjust encoding parameters based on visual characteristics. Based on previous research we determined that reducing streamed video bitrates and storage needs leads to reduced energy consumption overall. Expanding on existing work, this paper evaluates the potential of different AI-approaches and ML-models and initial results demonstrate significant bitrate savings without compromising perceptual quality, leading to lower bandwidth requirements and greener streaming practices.
CGP: Centroid-guided Graph Poisoning for Link Inference Attacks in Graph Neural Networks

Graph Neural Network (GNN) is the state-of-the-art machine learning model on graph data, which many modern big data applications rely on. However, GNN’s potential leakage of sensitive graph node relationships (i.e., links) could cause severe user privacy infringements. An attacker might infer the sensitive graph links from the posteriors of a GNN. Such attacks are named graph link inference attacks. While most existing research considers attack settings without malicious users, this work considers the setting where some malicious nodes are established by the attacker. This setting enables link inference without relying on the estimation of the number of links in the target graph, which significantly enhances the practicality of link inference attacks. This work further proposes centroid-guided graph poisoning (CGP). Without participating in the training process of the target model, CGP operates on links between malicious nodes to make the target model more vulnerable to graph link inference attacks. Experiment results in this work demonstrate that using less than 5% of malicious nodes, i.e. modifying approximately 0.25% of all links, CGP can increase the F-1 of graph link inference attacks by up to 4%.
Exponentially Distributed Nonlinear Proof-of-Work: Saving Energy while Preserving Decentralisation in Bitcoin Mining

Bitcoin is one of the most prominent blockchain systems but is infamous for its massive energy consumption. The proof-of-work (PoW) consensus algorithm used for appending transactions to the Bitcoin ledger (also known as Bitcoin mining) incurs substantial energy expenditure due to the energy-intensive nature of PoW. The root of this inefficiency lies in the current implementation of the PoW algorithm. PoW establishes a linear relationship between a miner's computational power and their probability of successfully mining a block by assigning an identical cryptographic puzzle to all miners. This paper investigates the energy inefficiency inherent in PoW mining by exploring the potential benefits of introducing a nonlinear probability of success based on a miner's computational power. This nonlinear proof-of-work (nlPoW) algorithm reduces energy consumption without compromising the decentralised nature of Bitcoin. This study formulates four distinct nlPoW algorithms through a meticulous design science approach by deducing requisite algorithmic specifications and structures. Rigorous statistical simulations are employed to assess the performance of nlPoW against conventional PoW within the Bitcoin mining process. Preliminary outcomes obtained from simulating a sizable network of miners, each possessing equivalent computational power, demonstrate that nlPoW effectively curtails the hash computations required during Bitcoin mining. nlPoW achieves energy efficiency enhancements without compromising the decentralised consensus model or substituting energy consumption with alternate resources, a trade-off often observed in prior attempts to mitigate the energy challenge associated with PoW.
An Identification of Software Defect Using Cost Based Support Vector Machine and Robust Similarity Aware Clustering Model

Software fault prediction is the prominent research area that aims at the successful delivery of the software to the clients. The success of the software relies on the successful development of the software without any of the faulty modules. The presence of the large number of the software faults results in ineffective product and the faults may be due to any of the hominoid mistake. The software faults are detected in the stress, computational faults, faults in syntax, capacity, load balance, and so on. It is the need of the hour to detect the faults effectively and precisely, but predicting the software faults remained a challenge. This research proposed a software fault prediction and classification approach based on the combination of the spiral model, Cost based Support Vector Machine (CSVM) and Robust Similarity Aware Clustering (RSC) process. The proposed machine learning algorithm efficiently identify the fault occurred on the software modules. RSC algorithm helps to cluster the resultant classes based on the similarity measure. This proposed technique helps to achieve less computing time and also reduces the failure ratio for testing the software modules. The experimental analysis of the proposed CSVM method is done on publicly available dataset such as NASA-CM1 data. The analysis demonstrated that the proposed algorithm is better than various existing approaches with respect to Accuracy (0.991), Precision (0.944), Recall (0.962), and F-measure (0.945) and such proved that it is capable of predicting faults within limited resource budget.
Multi-Instance Bias Suppression for Enhanced Generalization in Breast Cancer Diagnosis : Harnessing Histopathological Big Data Insights

The automated diagnosis of breast cancer through Whole Slide Images (WSI) is a critical endeavour to combat the threat it poses to women’s health. However, traditional deep learning algorithms strongly rely on Independent and Identically Distributed (I.I.D) and then encounter challenges related to multi-instance bias when analyzing multiple tissue sections from the same patient, limiting their generalization capability. To address this, this study introduces Multi-Instance Bias Suppression (MIBS), a novel approach leveraging adversarial training to mitigate patient-specific overfitting. MIBS employs an instance-level discriminator to guide feature generation, disentangling instance-specific cues from broader diagnostic patterns. Through competitive adversarial training, MIBS enhances feature generalization, effectively addressing overfitting and boosting cross-patient accuracy. Validated on the BreakHis dataset, MIBS effectively tackles multi-instance bias-induced overfitting. By bridging the gap between cutting-edge deep learning techniques and the challenges posed by large-scale medical image data, MIBS advances the accuracy and applicability of breast cancer diagnosis. Our approach addresses the multi-instance bias challenge and integrates seamlessly with big data, propelling medical image analysis to new heights of efficiency and precision.
Balancing the Scales: HyperSMOTE for Enhanced Hypergraph Classification

With the proliferation of bots on social media platforms, especially X, the need for effective and efficient bot detection mechanisms has never been more paramount. However, the inherent imbalance between the number of genuine users and bots presents a significant challenge, often leading to biased classifiers. In this paper, we introduce HyperSMOTE, a novel approach for imbalanced node classification leveraging the rich structure of hypergraphs. By representing X users as nodes and their interactions as hyperedges, we construct a hypergraph that captures the intricate relationships and interactions among users. This hypergraph-based representation allows for a more nuanced understanding of user behavior and interactions, providing a robust foundation for bot detection. HyperSMOTE addresses the class imbalance by generating synthetic bot accounts in the hypergraph, ensuring a balanced training dataset while preserving the hypergraph’s semantics. Our method significantly outperforms existing baselines across various metrics, demonstrating its efficacy. We further delve into the impact of different upsampling scales on classification performance, providing insights into the optimal configurations for HyperSMOTE.
SmRNet: Scalable Multiresolution Feature Extraction Network

This work addresses the challenge of designing a lightweight convolutional neural network (CNN) with high accuracy. To overcome information loss due to pooling operation, the proposed approach leverages the discrete wavelet transform (DWT) and inverse wavelet transform (IWT) as downsampling and upsampling operators instead of pooling and stride operations. The lost details during downsampling can be effectively restored by exploiting the lossless property of wavelets. As opposed to using a single filter, as in traditional convolution, this method employs both low- and high-frequency filters. The low-frequency filter captures information similar to the spatial domain representation, while the high-frequency filter extracts additional feature information. By incorporating both components, we enhance the classification and detection performance, as the combined information provides a richer representation of the input data. The depth-wise separable convolution is also employed to optimize the model further by reducing the number of parameters. Additionally, the proposed network offers flexibility with three options: small, medium, and large, allowing users to choose the appropriate size based on their specific requirements. Furthermore, the network can be easily expanded by adding more layers for those who desire further customization. The experimental evaluations on the CIFAR-10 and CIFAR-100 datasets for classification and MS COCO and Pascal VOC datasets for object detection demonstrate that the proposed feature extraction network outperforms ResNet-50, VGG16, and other networks in terms of accuracy while using fewer parameters. Notably, our approach shows significant promise for small object classification. The proposed network can be a general backbone for image classification, object detection, segmentation, and related tasks. The code has been released.
Toward Open and Sustainable Data Platform for Accessible Pedestrian Network

To provide mobility assistance for people with disabilities, pedestrian network data that includes accessibility attributes within public spaces is required. The freshness of data is necessary as public space conditions change frequently; however, its regular maintenance incurs substantial costs. In this paper, we propose a data platform for an accessible pedestrian network with a workflow similar to continuous software development. With this approach, the platform aims to allow local governments and citizens to maintain data in a collaborative and sustainable manner. This paper reports the initial phase of our project aiming at realizing such a platform; we designed and prototyped a platform based on this idea and conducted an actual field experiment in a specific area of Tokyo. Through this experiment, we demonstrated the feasibility of this idea and gained valuable insights for practical implementation.
Action Tracer: An Extendable, Low-Cost and Open-Source Motion Tracking Kit Based on 6-Axes Inertial Measurement Units

This paper presents the development and evaluation of an extendable motion capture device, Action Tracer, designed to assist with training repetitive motions in sports and activities. The system has feedback capabilities and an API for device connectivity, and can be configured for different sports and operated from an Android device while remaining low cost relative to devices on the market. The paper covers the use of marker-based motion capture systems, inertial measurement units, and biofeedback, and outlines the design specifications for the essential subsystems of the system. The evaluation results show that the developed system can keep, on average, within 5% of a markerless camera-based motion capture system with a precision of 10%. Overall, this paper contributes to the field of motion capture technology by presenting a low-cost, versatile system that can assist with training and rehabilitation for various sports and activities. The system's technical details and evaluation are presented, with a comparison to a markerless camera-based motion capture system. The paper provides a concise overview of the system's design, evaluation, and potential applications.
Recent Artificial Intelligence Advances in Detection and Diagnosis of Sickle Cell Disease: A review

Sickle cell anemia is a genetic disease characterized by a genuine alteration of hemoglobin that leads to the emergence of sickle-shaped red blood cells. The clinical diagnosis of sickle cell disease is based on blood analysis tests such as electrophoresis, chromatography and isoelectric focusing, blood count scanning or peripheral blood smears. These analyses aim to identify the presence of the hemoglobin S (HbS) in the sickle cell patient's blood. Nonetheless, these approaches face limitations linked to processing delays, substantial expenses, the need for specialized expertise, and inaccessibility in developing countries, where the prevalence of sickle cell disease is substantial. Thus, artificial intelligence techniques, including medical image segmentation and machine learning algorithms, offer innovative solutions to address these challenges. This paper gives an overview of recent advances related to image segmentation, feature extraction as well as classification approaches used in the detection and diagnosis of sickle cell disease. We introduced both computer vision techniques, machine learning and convolutional neural networks for sickle cell detection in microscopic images of blood smear. We discuss specific challenges related to segmentation methods while processing microscopic images containing overlapping cells. In addition, we examined challenges related to the robustness of convolutional neural network models used for feature extraction and then classification of sickle cells images. To conclude this paper, research prospects that can be explored in the future related to sickle cell detection in medical concerns are given.