Reviewerly/exHarmony huggingface dataset:
SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams

Recently, there has been an explosive generation of streaming data in various fields such as IoT and network attack detection, medical data monitoring, and financial trend analysis. These domains require precise and rapid analysis capabilities by minimizing noise from continuously generated raw data. In this paper, we propose SPinDP (Stream Purifier in Distributed Platform), an open source-based high-speed stream purification platform, to support real-time stream purification. SPinDP consists of four major components, Data Stream Processing Engine, Purification Library, Plan Manager, and Shared Storage, and operates based on open-source systems including Apache Storm and Apache Kafka. In these components, stream processing throughput and latency are critical performance metrics, and SPinDP significantly enhances distributed processing performance by utilizing the ultra-high-speed network RDMA (Remote Direct Memory Access). For the performance evaluation, we use a distributed cluster environment consisting of nine nodes, and we show that SPinDP’s stream processing throughput is more than 28 times higher than that of the existing Ethernet environment. SPinDP also significantly reduces the processing latency by more than 2473 times on average. These results indicate that the proposed SPinDP is an excellent integrated platform that can efficiently purify high-speed and large-scale streams through RDMA-based distributed processing.
Comprehensive Analysis of Mammography Images Using Multi-Branch Attention Convolutional Neural Network

Breast cancer profoundly affects women’s lives; its early diagnosis and treatment increase patient survival chances. Mammography is a common screening method for breast cancer, and many methods have been proposed for automatic diagnosis. However, most of them focus on single-label classification and do not provide a comprehensive analysis concerning density, abnormality, and severity levels. We propose a method based on the multi-label classification of two-view mammography images to comprehensively diagnose a patient’s condition. It leverages the correlation between density type, lesion type, and states of lesions, which radiologists usually perform. It simultaneously classifies mammograms into the corresponding density, abnormality type, and severity level. It takes two-view mammograms (with craniocaudal and mediolateral oblique views) as input, analyzes them using ConvNeXt and the channel attention mechanism, and integrates the information from the two views. Finally, the fused information is passed to task-specific multi-branches, which learn task-specific representations and predict the relevant state. The system was trained, validated, and tested using two public domain benchmark datasets, INBreast and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), and achieved state-of-the-art results. The proposed computer-aided diagnosis (CAD) system provides a holistic observation of a patient’s condition. It gives the radiologists a comprehensive analysis of the mammograms to prepare a full report of the patient’s condition, thereby increasing the diagnostic precision.
«The Sigma Awards» data journalism projects: features of visual content

Introduction. Data visualization plays a crucial role in creating high-quality materials in data journalism; it is precisely the successful way of presenting information that depends on its comprehensibility, accessibility, and “virality” for the audience. The worldwide award, “The Sigma Data Journalism Awards,” winners are a kind of role model and trend indicator in data journalism. Goal. That is why the study aimed to analyze the key characteristics and elements of data visualization in the noted data journalism projects, as well as to identify changes that may occur in the techniques and methods of data presentation over time. Research methods. The method of comparative content analysis, methods of synthesis and generalization, as well as logical and chronological methods, were used. Results. As a result, 21 winners of “The Sigma Data Journalism Awards” chosen over three years (2020, 2022, 2023) were analyzed exclusively from the point of view of data visualization. In developing 3D modeling technologies, editors actively use these methods to improve communication effectiveness with the audience and increase their interest. Gamification is also noted as an innovative tool that increases the interactive component of visual objects. Maps, photographs, diagrams, and audio recordings remain relevant methods in data journalism, while infographics are used less and less in the projects noted. Leading newsrooms that actively create materials in data journalism are The New York Times, ABC News, and Civio, while ProPublica, Pointer, and Texty.org.ua stand out as leading media outlets specializing in the analysis and use of data. According to the study results, the recognized journalists noted in the visualization of data journalism are R. Dottle, Y-H. Law and J. Coburn. Conclusions. The obtained results can be useful for further research, which consists in conducting a statistical content analysis of the visualization of data journalism in the leading global newsrooms identified in this study (The New York Times, ABC news and Civio).
Underwater Image Super-Resolution via Dual-aware Integrated Network

Underwater scenes are often affected by issues such as blurred details, color distortion, and low contrast, which are primarily caused by wavelength-dependent light scattering; these factors significantly impact human visual perception. Convolutional neural networks (CNNs) have recently displayed very promising performance in underwater super-resolution (SR). However, the nature of CNN-based methods is local operations, making it difficult to reconstruct rich features. To solve these problems, we present an efficient and lightweight dual-aware integrated network (DAIN) comprising a series of dual-aware enhancement modules (DAEMs) for underwater SR tasks. In particular, DAEMs primarily consist of a multi-scale color correction block (MCCB) and a swin transformer layer (STL). These components work together to incorporate both local and global features, thereby enhancing the quality of image reconstruction. MCCBs can use multiple channels to process the different colors of underwater images to restore the uneven underwater light decay-affected real color and details of the images. The STL captures long-range dependencies and global contextual information, enabling the extraction of neglected features in underwater images. Experimental results demonstrate significant enhancements with a DAIN over conventional SR methods.
Enhancing Real-time Object Detection with YOLO Algorithm

This paper introduces YOLO, the best approach to object detection. Real-time detection plays a significant role in various domains like video surveillance, computer vision, autonomous driving and the operation of robots. YOLO algorithm has emerged as a well-liked and structured solution for real-time object detection due to its ability to detect items in one operation through the neural network. This research article seeks to lay out an extensive understanding of the defined Yolo algorithm, its architecture, and its impact on real-time object detection. This detection will be identified as a regression problem by frame object detection to spatially separated bounding boxes. Tasks like recognition, detection, localization, or finding widespread applicability in the best real-world scenarios, make object detection a crucial subdivision of computer vision. This algorithm detects objects in real-time using convolutional neural networks (CNN). Overall this research paper serves as a comprehensive guide to understanding the detection of objects in real-time using the You Only Look Once (YOLO) algorithm. By examining architecture, variations, and implementation details the reader can gain an understanding of YOLO’s capability.
Indian Budget 2022: A Make-or-Break Moment for Cryptocurrency

People are liable to the tax rate if they transfer digital assets during a specific fiscal year. There is no distinction between income from businesses and investments or between short-term and long-term gains because the 30% tax rate is applicable regardless of the sort of income. By clearly stating how it would be charged, the Indian budget 2022 has provided some direction. Losses were consequently experienced by both new and old cryptocurrency buyers. Under Section 115 BBH, it is illegal to offset cryptocurrency losses with cryptocurrency gains—or any other gains or revenue, for that matter. The implementation of the 30% tax rule on digital assets has caused the collapse of the cryptocurrency market, and there is a possibility that investors will continue to suffer losses in the future.
A Fast Fully Parallel Ant Colony Optimization Algorithm Based on CUDA for Solving TSP

In view of the known problems of parameter sensitivity, local optimum, and slow convergence in the ant colony optimization (ACO), we aim to improve the performance of the ACO. To solve the traveling salesman problem (TSP) quickly with accurate results, we propose a fully parallel ACO (FP-ACO). Based on the max–min ant system (MMAS), we initiate a compensation mechanism for pheromone to constrain its value, guarantee the correctness of results and avoid a local optimum, and further enhance the convergence ability of ACO. Moreover, based on the compute unified device architecture (CUDA), the ACO is implemented as a kernel function on a graphics processing unit (GPU), which shortens the running time of massive iterations. Combined with the roulette wheel selection mechanism, FP-ACO has powerful search capabilities and is committed to obtaining better solutions. The experimental results show that, compared with the effective strategies ACO (ESACO) that runs on CPU, the speed-up ratio of the proposed algorithm reaches 35, and the running time is less than that of the max–min ant system-roulette wheel method-bitmask tabu (MMAS-RWM-BT) that runs on GPU. Furthermore, our algorithm outperforms the other two algorithms in the speed-up ratio and less runtime, proving that the proposed FP-ACO is more suitable for solving TSP.
Assessing the utility of text-to-SQL approaches for satisfying software developer information needs

Abstract Software analytics integrated with complex databases can deliver project intelligence into the hands of software engineering (SE) experts for satisfying their information needs. A new and promising machine learning technique known as text-to-SQL automatically extracts information for users of complex databases without the need to fully understand the database structure nor the accompanying query language. Users pose their request as so-called natural language utterance, i.e., question. Our goal was evaluating the performance and applicability of text-to-SQL approaches on data derived from tools typically used in the workflow of software engineers for satisfying their information needs. We carefully selected and discussed five seminal as well as state-of-the-art text-to-SQL approaches and conducted a comparative assessment using the large-scale, cross-domain Spider dataset and the SE domain-specific SEOSS-Queries dataset. Furthermore, we study via a survey how SE professionals perform in satisfying their information needs and how they perceive text-to-SQL approaches. For the best performing approach, we observe a high accuracy of 94% in query prediction when training specifically on SE data. This accuracy is almost independent of the query’s complexity. At the same time, we observe that SE professionals have substantial deficits in satisfying their information needs directly via SQL queries. Furthermore, SE professionals are open for utilizing text-to-SQL approaches in their daily work, considering them less time-consuming and helpful. We conclude that state-of-the-art text-to-SQL approaches are applicable in SE practice for day-to-day information needs.
Self-Adaptive Representation Learning Model for Multi-Modal Sentiment and Sarcasm Joint Analysis

Sentiment and sarcasm are intimate and complex, as sarcasm often deliberately elicits an emotional response in order to achieve its specific purpose. Current challenges in multi-modal sentiment and sarcasm joint detection mainly include multi-modal representation fusion and the modeling of the intrinsic relationship between sentiment and sarcasm. To address these challenges, we propose a single-input stream self-adaptive representation learning model ( SRLM ) for sentiment and sarcasm joint recognition. Specifically, we divide the image into blocks to learn its serialized features and fuse textual feature as input to the target model. Then, we introduce an adaptive representation learning network using a gated network approach for sarcasm and sentiment classification. In this framework, each task is equipped with its dedicated expert network responsible for learning task-specific information, while the shared expert knowledge is acquired and weighted through the gating network. Finally, comprehensive experiments conducted on two publicly available datasets, namely Memotion and MUStARD, demonstrate the effectiveness of the proposed model when compared to state-of-the-art baselines. The results reveal a notable improvement on the performance of sentiment and sarcasm tasks.
PSYCHOLOGICAL AND PEDAGOGICAL FUNDAMENTALS OF THE CREATIVE ACTIVITY DEVELOPMENT OF THE FUTURE TEACHER

The article substantiates the essence of creative activity as a property of the individual. Psychological and pedagogical influences on its formation and development are presented. The basics of the formation of the creative activity of the future primary
OVERVIEW OF RECOMMENDATION ALGORITHMS FOR USE IN CROWDFUNDING PLATFORMS

This report provides an in-depth overview of recommendation algorithms and their critical role in the dynamic landscape of crowdfunding systems. Public financing systems have become an outstanding and transformative method of financing a number of creativ
STUDY OF THE PROBLEM OF TRAINING THE FUTURE MUSIC TEACHER FOR THE ORGANIZATION OF GROUP EDUCATION ACTIVITIES OF STUDENTS

The search for optimal forms of organization of students' educational activities, the use of the most effective and promising interactive teaching methods is of great importance in the preparation of the future music teacher for the organization of group
Premortems in Game Development Teams: Impact and Potential

Software design teams need methods to evaluate plans as part of agile development processes. The premortem is a cognitive, structured, analytic technique that supports team plan evaluation and re-planning. Few empirical or longitudinal studies involving premortems exist. Ten game development teams (n=68 members) conducted premortems early on during a year-long game development project. Teams provided initial ratings of their game design plan, then conducted a premortem, and revised their plans. In the premortems, teams identified 17.8 unique reasons on average for project failure and 16.7 mitigations to those failures. Reasons for project failure focused mainly on game design execution, team communication, and game complexity (e.g., too many levels, branching). While most teams identified solutions for these challenges, surprisingly few teams revised their plans to scale back the game design complexity.
Skin Cancer Detection and Classification using Deep learning methods

Skin cancer is a very dangerous disease that needs to be found early, so that it can be treated effectively. In the past few years, classifiers built on convolutional neural networks (CNNs) have become the best way to find melanoma. According to the review, the CNN-based classifier is as accurate as dermatologist in classifying skin cancer images, allowing for faster and more accurate detection. This article examines the most recent studies on Machine learning and deep learning-based melanoma categorization in depth. We provide a comprehensive description of the machine learning and deep learning classifier, including details on the accuracy of these classifiers. The primary objective of this research is to analyze and collect current research trends, issues, and opportunities for melanoma diagnosis, as well as to investigate the current approach for using deep learning to detect and recognize melanoma. The main finding of this review is that the neural network provides high accuracy as comparison to machine learning methods.
Scene-Aware Activity Program Generation with Language Guidance

We address the problem of scene-aware activity program generation, which requires decomposing a given activity task into instructions that can be sequentially performed within a target scene to complete the activity. While existing methods have shown the ability to generate rational or executable programs, generating programs with both high rationality and executability still remains a challenge. Hence, we propose a novel method where the key idea is to explicitly combine the language rationality of a powerful language model with dynamic perception of the target scene where instructions are executed, to generate programs with high rationality and executability. Our method iteratively generates instructions for the activity program. Specifically, a two-branch feature encoder operates on a language-based and graph-based representation of the current generation progress to extract language features and scene graph features, respectively. These features are then used by a predictor to generate the next instruction in the program. Subsequently, another module performs the predicted action and updates the scene for perception in the next iteration. Extensive evaluations are conducted on the VirtualHome-Env dataset, showing the advantages of our method over previous work. Key algorithmic designs are validated through ablation studies, and results on other types of inputs are also presented to show the generalizability of our method.
Time-Energy Uncertainty Relation for Noisy Quantum Metrology

The impact of noise on the accuracy of a quantum clock is investigated, unveiling a necessary and sufficient requirement for accuracy that extends standard error-correcting conditions to quantum metrology.
Reconstructing Close Human Interactions from Multiple Views

This paper addresses the challenging task of reconstructing the poses of multiple individuals engaged in close interactions, captured by multiple calibrated cameras. The difficulty arises from the noisy or false 2D keypoint detections due to inter-person occlusion, the heavy ambiguity in associating keypoints to individuals due to the close interactions, and the scarcity of training data as collecting and annotating motion data in crowded scenes is resource-intensive. We introduce a novel system to address these challenges. Our system integrates a learning-based pose estimation component and its corresponding training and inference strategies. The pose estimation component takes multi-view 2D keypoint heatmaps as input and reconstructs the pose of each individual using a 3D conditional volumetric network. As the network doesn't need images as input, we can leverage known camera parameters from test scenes and a large quantity of existing motion capture data to synthesize massive training data that mimics the real data distribution in test scenes. Extensive experiments demonstrate that our approach significantly surpasses previous approaches in terms of pose accuracy and is generalizable across various camera setups and population sizes. The code is available on our project page: https://github.com/zju3dv/CloseMoCap.
Object Motion Guided Human Motion Synthesis

Modeling human behaviors in contextual environments has a wide range of applications in character animation, embodied AI, VR/AR, and robotics. In real-world scenarios, humans frequently interact with the environment and manipulate various objects to complete daily tasks. In this work, we study the problem of full-body human motion synthesis for the manipulation of large-sized objects. We propose Object MOtion guided human MOtion synthesis (OMOMO), a conditional diffusion framework that can generate full-body manipulation behaviors from only the object motion. Since naively applying diffusion models fails to precisely enforce contact constraints between the hands and the object, OMOMO learns two separate denoising processes to first predict hand positions from object motion and subsequently synthesize full-body poses based on the predicted hand positions. By employing the hand positions as an intermediate representation between the two denoising processes, we can explicitly enforce contact constraints, resulting in more physically plausible manipulation motions. With the learned model, we develop a novel system that captures full-body human manipulation motions by simply attaching a smartphone to the object being manipulated. Through extensive experiments, we demonstrate the effectiveness of our proposed pipeline and its ability to generalize to unseen objects. Additionally, as high-quality human-object interaction datasets are scarce, we collect a large-scale dataset consisting of 3D object geometry, object motion, and human motion. Our dataset contains human-object interaction motion for 15 objects, with a total duration of approximately 10 hours.
Towards tailored cognitive support in augmented reality assembly work instructions

Abstract Background Augmented reality (AR) is receiving increasing interest as a tool to create an interactive and motivating learning environment. Yet, it is unclear how instructional support affects performance in AR. Objectives This study sought to explore how varying the instructional support in AR can affect performance‐related behaviours of students with low cognitive abilities during assembly work. Methods A total of 90 Belgian secondary school students repeatedly executed four different realistic assembly tasks. Three levels of instructional support (low, medium, and high) in AR as well as a control condition with paper instructions with a high level of detail were systematically varied across tasks and participants. Results and Conclusions Multilevel regression analyses showed that AR instructions yielded lower assembly times and a lower perceived physical effort than paper instructions. Additionally, participants perceived tasks as less complex when given AR instructions with a high or a medium level of detail than when given a low level of detail. No effects of instructional support were established for other performance‐related behaviours, namely necessary assistance, error‐making, cognitive load, competence frustration, and stress. Effect sizes were small, at least among the instructional support conditions studied, yielding a limited base for adaptivity. Presumably, tailoring the instructional support in AR is only beneficial for highly complex tasks. The results might be useful for the design and implementation of AR in educational settings.
Improving the Accuracy of Robot Collecting Organisms in Marine Environment Based on Yolov5 Improvement

Abstract In this paper, an improved YOLOv5 multiscale marine organism target detection algorithm (YOLOv5‐Mult) is proposed to address the insufficient feature extraction ability of small targets, low detection accuracy, and high catching error of existing models in complex environments. First, real frame clustering is performed using the Kmeans++ method. Second, the BiFPN network module is adopted in lieu of the PANet network module to enhance the feature fusion ability. Next, the multilayer semantic fusion module RBC (RepBlock CSP) replaces the C3 module before the SPP layer of the Backbone network and the C3 module in the Neck layer to enrich the image semantic information. Finally, the multiscale feature fusion module MC (Mult Conv) replaces the last C3 module in the Backbone network to mitigate the semantic gap between different feature channel layers. Experimental results demonstrate that the improved algorithm attains a mAP value of 71.18%, which is 5.22% higher than that of the original YOLOv5 algorithm, providing accurate identification and fishing for underwater robots.