Reviewerly/exHarmony huggingface dataset:
Region Collaborative Network for Detection-Based Vision-Language Understanding

Given a query language, a Detection-based Vision-Language Understanding (DVLU) system needs to respond based on the detected regions (i.e.,bounding boxes). With the significant advancement in object detection, DVLU has witnessed great improvements in recent years, such as Visual Question Answering (VQA) and Visual Grounding (VG). However, existing DVLU methods always process each detected image region separately but ignore that they were an integral whole. Without the full consideration of each region’s context, the image’s understanding may contain more bias. In this paper, to solve the problem, a simple yet effective Region Collaborative Network (RCN) block is proposed to bridge the gap between independent regions and the integrative DVLU task. Specifically, the Intra-Region Relations (IntraRR) inside each detected region are computed by a position-wise and channel-wise joint non-local model. Then, the Inter-Region Relations (InterRR) across all the detected regions are computed by pooling and sharing parameters with IntraRR. The proposed RCN can enhance the features of each region by using information from all other regions and guarantees the dimension consistency between input and output. The RCN is evaluated on VQA and VG, and the experimental results show that our method can significantly improve the performance of existing DVLU models.
Application of Blockchain Based on Deep Learning Algorithm in Enterprise Internet of Things System

The purpose of an IoT enterprise business system is to form a global open network structure. The main task of this process is to build the Internet of Things system based on the internal enterprise, and link the upstream and downstream enterprises to interact with the Internet of Things system. However, existing blockchain systems often have limited performance and capacity, so they cannot be used in many scenarios, resulting in reduced scalability. The most common solution is to use the deep learning algorithm to find the optimal blockchain parameters. Based on deep learning and blockchain technology, this article proposes an enterprise Internet of Things system, which divides the whole Internet of Things into three layers—device layer, edge layer, and application layer, so as to build a reliable edge layer platform, so as to integrate the application network and blockchain network of the enterprise Internet of Things system and ensure the data security of the enterprise Internet of Things system. Finally, in order to verify the design proposed in this article, we build a distributed IoT business system, implement the deployment of experimental datasets on multiple hardware platforms, and design experiments to verify the security and effectiveness of the system designed in this work. By using blockchain technology based on the deep learning algorithm, the accuracy and time efficiency of the enterprise Internet of Things system are improved, and the burden of enterprise operators is greatly reduced. This article studies the deep learning algorithm and blockchain technology, and applies it to the design process of the enterprise Internet of Things system, thus promoting the development of the enterprise Internet of Things system.
Analysis of the Work of Panpipe Composition—Love on the Cloud

The panpipe is one of the wind instruments, whose birth dates back to ancient times. With the continuous development of Chinese history and culture, its specific form and social function change. On the basis of exploring the development history of the panpipe, this paper taking Love on the Cloud as an example, analyzes the modern music Love on the Cloud, covering its creation background, music theory, and aesthetic characteristics.
Grasp State Estimation

This chapter introduces the proposed method for the estimation of the grasp state of a manipulated object. It combines different sensor modalities in order to provide a robust estimate of the object pose, contact configuration and joint position errors.
Haze Elimination Strategy for Indoor Robot Vision with Dark Channel Prior and Encoded Blur Kernel Space

In an indoor environment, extracting information from an image becomes strenuous when humidity gets on the camera glass, and resulting image becomes hazy. A lot of times due to improper handling and indifference toward camera cleanliness, the camera lens screen becomes dirty which also causes haziness in image. Moreover, the shakiness of the robot vehicle which is inevitable for all mobile robot also results in shaky and blurred images. Haze and blur removal is an ill-posed problem and very difficult task. Over the years many methods have come which uses various constraints to achieve reasonable results and also fail to properly remove blur. All these methods fail to address the hazy and blurry image simultaneously in the context of indoor robot vision. In this paper, we propose a method combining dark channel prior and encoded blur kernel space which will remove this haziness and shaky blur in real time without extra overhead. This paper focuses on vision of mobile robots in hazy and shaky conditions.
Textual Inference Identification in the Malayalam Language Using Convolutional Neural Network

Natural language inference (NLI), earlier known as textual entailment, is an important task related to the semantic matching of natural language sentences. Systems and methodologies that can identify inferences are helpful for most language processing tasks like document summarization and question answering systems. There is active research in NLI for English and other foreign languages. Considering Indian languages like Malayalam, there are very few works done. Here, we focus on identifying inferences in the Malayalam language using one-dimensional convolutional neural network (CNN), multichannel CNN, and CNN architecture on matching sentences over interaction space with fastText-based embeddings. This work is an attempt to apply convolutional neural networks for NLI in Malayalam without any hand-engineered features. This approach contributes with a recall of 0.66% for binary and 0.51% for multiclass classification. This work also contributes to the language resources community.
A Smartphone-Based Six-DOF Measurement Method With Marker Detector

The visual measurement methods of the six-degrees-of-freedom (6-DOF) of a rigid body always require expensive and professional instruments, while the camera of smartphones is so powerful that they can perform the task of visual measurement, which will reduce the cost of measurement. Therefore, a low-cost smartphone-based 6-DOF measurement method with the marker detector is proposed, which uses two smartphones to achieve dynamic measurement for the moving rigid body. Specifically, the proposed method improves the accuracy of depth measurement based on the parallax principle. Based on the LeNet-5, the marker detector with the tracking compensation mechanism is proposed to detect and track markers. We carry out experiments to verify the feasibility of the proposed method and to evaluate its performance. In the experiment, the average absolute error of the rotation angles and the translations measurement are 2.2888&#x00B0; and 3.5763mm. The results show that the proposed method achieves dynamic measurement using two smartphones.
Nursing Students’ Perceptions on the Possibility of Using the Storytelling Platform for the Elderly in Nursing Homes: Focus Group Study

Objectives The purpose of this study was to explore the experiences and perceptions of nursing students on their potential as gerontological nursing practice educational medium that can enhance nursing students’ caring competency in using the storytelling platform for the elderly in nursing homes before developing and applying an intergenerational storytelling platform for the elderly in nursing homes. Methods A total of 6 groups were composed of two 2nd grade groups of nursing university and four 3rd grade groups, and the number of people per group was 4-5. A total of 28 participants conducted focus group interviews and the results were derived through qualitative data analysis. Qualitative data were obtained using the Zoom application through six focus group interviews consisting of 28 nursing students finishing 2-3 grade courses at a national university in Gyeongsang province. Participants completed interviews from January 16 to February 21, 2022. All interviews were recorded, transcribed, and analyzed by employing the systemic qualitative analysis method. Results The current study used the six phases of thematic analysis proposed by Braun &amp; Clarke. The analysis revealed a total of six topics, seventeen categories, and 36 subcategories. The main results of the study showed that the use of a storytelling platform could increase creativity and decrease feelings of isolation among the elderly, resolute ageism among students, and improve empathy, and the possibility of change in nursing home culture. Also, a storytelling platform with digital technology would be used as an educational medium connected and shared between nursing schools, nursing homes, and the community. Conclusions When constructing a storytelling platform for the elderly in nursing homes, based on connectivity, familiarity, common experience, and interest, with being archived, re-created, and connected, this storytelling platform could not only bridge the generation gap but also contribute to the development of students’ gerontological nursing competency.&#x0D;
Real and Pseudo Pedestrian Detection Method with CA-YOLOv5s Based on Stereo Image Fusion

With the development of convolutional neural networks, the effect of pedestrian detection has been greatly improved by deep learning models. However, the presence of pseudo pedestrians will lead to accuracy reduction in pedestrian detection. To solve the problem that the existing pedestrian detection algorithms cannot distinguish pseudo pedestrians from real pedestrians, a real and pseudo pedestrian detection method with CA-YOLOv5s based on stereo image fusion is proposed in this paper. Firstly, the two-view images of the pedestrian are captured by a binocular stereo camera. Then, a proposed CA-YOLOv5s pedestrian detection algorithm is used for the left-view and right-view images, respectively, to detect the respective pedestrian regions. Afterwards, the detected left-view and right-view pedestrian regions are matched to obtain the feature point set, and the 3D spatial coordinates of the feature point set are calculated with Zhengyou Zhang's calibration method. Finally, the RANSAC plane-fitting algorithm is adopted to extract the 3D features of the feature point set, and the real and pseudo pedestrian detection is achieved by the trained SVM. The proposed real and pseudo pedestrian detection method with CA-YOLOv5s based on stereo image fusion effectively solves the pseudo pedestrian detection problem and efficiently improves the accuracy. Experimental results also show that for the dataset with real and pseudo pedestrians, the proposed method significantly outperforms other existing pedestrian detection algorithms in terms of accuracy and precision.
Friend Recommender System for Social Networks Based on Stacking Technique and Evolutionary Algorithm

In recent years, social networks have made significant progress and the number of people who use them to communicate is increasing day by day. The vast amount of information available on social networks has led to the importance of using friend recommender systems to discover knowledge about future communications. It is challenging to choose the best machine learning approach to address the recommender system issue since there are several strategies with various benefits and drawbacks. In light of this, a solution based on the stacking approach was put out in this study to provide a buddy recommendation system in social networks. Additionally, a decrease in system performance was caused by the large amount of information that was accessible and the inefficiency of some functions. To solve this problem, a particle swarm optimization (PSO) algorithm to select the most efficient features was used in our proposed method. To learn the model in the objective function of the particle swarm algorithm, a hybrid system based on stacking is proposed. In this method, two random forests and Extreme Gradient Boosting (XGBoost) had been used as the base classifiers. The results obtained from these base classifiers were used in the logistic regression algorithm, which has been applied sequentially. The suggested approach was able to effectively address this issue by combining the advantages of the applied strategies. The results of implementation and evaluation of the proposed system show the appropriate efficiency of this method compared with other studied techniques.
The Performance of Topic Evolution Based on a Feature Maximization Measurement for the Linguistics Domain

Understanding the performance of the data mining approach and topic evolution in a certain scientific domain is imperative to capturing key domain developments and facilitating knowledge transfer within and across domains. Our research selects linguistics as an exploratory domain and exploits the feature maximization (FM) measurement for feature selection, combined with the contrast ratio to conduct the diachronic analysis for the linguistics domain’s topics. To accurately mine the linguistics domain’s topics and obtain the optimal clustering model selection, we exploit an integrated method associated with the deep embedding for clustering (DEC) algorithm based on the keywords-based Text Representation Matrix (KTRM) and Lamirel’s EC index and test the performance of this method. The results show that the FM measurement is applicable in the linguistics domain for topic mining, and the combinatory method has the advantage of an unbiased clustering optimization model and applies to the design of non-parameter clustering and algorithms from the low dimension to the high dimension of datasets. The findings suggest that this approach could be suitable for a diachronic analysis of topic evolution and facilitate the performance of topic detection. In addition, these findings of text detection can rise to knowledge fusion cognition with the factor of language as an available research objective in interdisciplinary research.
Application Design of Sports Music Mobile App Based on Rhythm Following

With the rise of various running apps, running has become a national sport. Relevant studies have found that music can provide energy, psychological and psychophysiological benefits in physical activities, especially when the action and music are carried out simultaneously. However, most running apps on the market simply integrate the function of music playing, and do not focus on the synchronization of action and music. Therefore, it is a very innovative and meaningful thing to study the synchronous follow of music for running. This paper introduces the research process and implementation steps of rhythm following in detail. First, based on the sensor data (acceleration sensor and gyroscope) and logistic regression analysis, the accuracy rate of 92.4% and recall rate of 98.2% are finally achieved. After that is the synchronization algorithm of music rhythm, this paper introduces in detail how to adjust the music of musical instrument digital interface (MIDI) in real time based on the landing point, so as to achieve the synchronization effect of music rhythm and running rhythm. Finally, the Android program is developed, the above research results are integrated, and the rhythm following mobile app is produced.
Design of Blowing Instrument Controller Based on Air Pressure Sensor

This paper presents a design of wind instrument controller for Chinese folk music. Since the birth of MIDI, the electronic musical instrument industry has become increasingly mature through constant iteration. This paper aims to restore the air pressure amplitude mapping of real musical instruments, and tries to design a wind instrument controller based on the prototype of Sheng, making a preliminary attempt to fill the gap in the market of national wind instrument controllers.
Designing and Assessing Authentic Software Development Projects in Undergraduate Computing Education

Team software development projects are a crucial component of undergraduate computing education. Studies of software developers in industry underscore the need for undergraduates to acquire authentic software development experiences in which they contribute to legacy code bases within collaborative software development contexts. Despite their clear educational value, team software development projects are notoriously challenging to implement and assess. Drawing on experience and relevant research results, this Research in Practice Project Activity (RIPPA) brings together computing educators and researchers to discuss, research, and innovate approaches to designing and assessing authentic software development projects. Participants are invited to run empirical studies in their own courses in which they (a) collect subjective and objective measures of team and individual progress in software projects, and (b) explore alternative pedagogical and assessment approaches, including those supported by the use of custom learning management and data analytics tools. By the end of the RIPPA, we aim to publish one or more research papers that advance the state-of-the-art in team software project pedagogy, supporting technology, and assessment based on the results of multi-institutional research studies.
YARN Schedulers for Hadoop MapReduce Jobs: Design Goals, Issues and Taxonomy

Objective: Big Data processing is a demanding task, and several big data processing frameworks have emerged during recent decades. The performance of these frameworks greatly dependent on resource management models. Methods: YARN is one of such models which acts as a resource management layer and provides computational resources for execution engines (Spark, MapReduce, storm, etc.) through its schedulers. The most important aspect of resource management is job scheduling. Results: In this paper, we first present the design goal of YARN real-life schedulers (FIFO, Capacity, and Fair) for the MapReduce engine. Later, we discuss the scheduling issues of the Hadoop MapReduce cluster. Conclusion: Many efforts have been carried out in the literature to address issues of data locality, heterogeneity, straggling, skew mitigation, stragglers and fairness in Hadoop MapReduce scheduling. Lastly, we present the taxonomy of different scheduling algorithms available in the literature based on some factors like environment, scope, approach, objective and addressed issues.
Continuing Qualitative Data Collection During the COVID 19 Pandemic: Lessons Learnt from A Study Among Tribes in Manipur, India

COVID-19 pandemic has caused disruptions in carrying out research activities in the field. The established methods of data collection for both qualitative and quantitative research could not be implemented. Researchers worldwide adopted contactless data collection methods, including the use of mobile phones for research purposes. This paper presents the experiences of conducting interviews among tribal population using mobile phones in three villages of Manipur during the pandemic. The interviews proved to be successful and effective. Minor technical glitches were a challenge but were not significant to affect the quality of the data. During unprecedented times such as the current pandemic, conducting interviews via mobile phones could be a viable alternative to face-to-face interviews for collecting qualitative data from the communities.
Automatic Labeling of Clusters for a Low-Resource Urdu Language

Document clustering techniques often produce clusters that require human intervention to interpret the meaning of such clusters. Automatic cluster labeling refers to the process of assigning a meaningful phrase to a cluster as a label. This article proposes an unsupervised method for cluster labeling that is based on noun phrase chunking. The proposed method is compared with four other statistical-based methods, including Z-Order, M-Order, T-Order, and YAKE. In addition to the statistical measures based labeling schemes, the approach is also compared with two graph-based techniques: TextRank and PositionRank. The experiments were performed on the low-resource Urdu language corpus of News Headlines. The proposed approach's effectiveness was evaluated using cosine similarity, the Jaccard index, and feedback received from human evaluators. The results show that the proposed method outperforms other methods. It was found that the labels produced were more relevant and semantically rich in contrast to other approaches.
Carrier Aircraft Scheduling Optimization Based on A* Algorithm and Genetic Algorithm

In the whole aircraft carrier combat system, it has always been an important issue to realize the rapid take-off of carrier aircraft. Previous articles tend to pay more attention to the problems of path planning and deck processing, rarely consider the integration of the whole process, and do not consider the situation that the carrier aircraft are on the deck and hangar at the same time. In this paper, the double-layer space of the aircraft carrier is considered, which allows the aircraft to realize the operation of the deck arriving from the hangar through the elevator. On this basis, the process of sortie on aircraft carrier is studied from the perspective of the whole scheduling. The process of sortie is divided into five steps: task allocation, ascending to the deck, taxiing to the preparation position, completing takeoff inspection, takeoff and baffle recovery. Firstly, the plane model of the aircraft carrier is established, the obstacles and key positions are marked, the A* algorithm is used to search the path for each carrier aircraft, and the delayed start strategy is used to avoid obstacles in the process of traveling. The idea of job shop scheduling is used to arrange the dispatch sequence for carrier aircraft, and the best sortie scheme is selected by genetic algorithm. Finally, through the simulation of the algorithm, the feasibility of the model and the effectiveness of the algorithm are verified.
A 2D UAV Path Planning Method Based on Reinforcement Learning in the Presence of Dense Obstacles and Kinematic Constraints

The complex kinematic constraints and dense obstacles are always the huge challenge in the UAV path planning. To effectively deal with dense obstacles and kinematic constraints, a novel two-level optimization algorithm for unmanned aerial vehicles (UAVs) in 2D maps, called as Spherical Expansion-Proximal Policy Optimization (SE-PPO), is proposed in this paper. This method is a combination of SE and PPO algorithms. In the first level, SE algorithm is used to generate the initial path, and sub-goals are selected from this path in the first level. These sub-goals are optimized by the local path optimizer based on PPO algorithm to obtain the final path. The effectiveness of this method to deal with the kinematic constraints and dense obstacles is demonstrated by the results of the simulation experiments.