Reviewerly/exHarmony huggingface dataset:
Feature Extraction Method of Piano Performance Technique Based on Recurrent Neural Network

In order to solve the problem of low efficiency in traditional feature extraction methods of piano performance techniques, a feature extraction method of piano performance techniques based on recurrent neural network is proposed. Analyze the types of piano playing techniques, and establish the hand model. On this basis, the hand action signals of piano performance are collected from the two aspects of finger key strength and hand action video image. Finally, the feature extraction of piano performance techniques is realized from the time domain and frequency domain. Through the comparison with the traditional extraction method, it is concluded that the extraction efficiency of the optimized design of piano performance technique feature extraction method has been significantly improved, and it has obvious application advantages in the identification of piano performance techniques.
A Privacy Protection Method for Deepfake Hybrid Cloud Computing

Cloud computing developed the world rapidly, so in our lifetimes, other cloud services are entered, and the security protection in our cloud services, particularly the protection against data sequestration, is gaining importance. Still, the protection separation implementation causes a huge drain. Therefore, it is difficult to implement the most appropriate product to reduce consumption power while protecting separation. The proposal proposes a full-scale sequestration scheme (PPPS) to provide usable sequestration protection that satisfies stone sequestration requirements while system performance can be maintained. First, the separation is unidentified by the drugs they carry, and they quantify the degree of security and perform the TripleDES standard and AES standard encrypts algorithms. The safety formulation is also derived from analysis results and professional data. Finally, similar results display that PPPS also meets the request sequestration from stones and maintains the performance in various cloud environments.
Deepfakes Spark Implementation for Big Data Analytics

One of the most significant challenges we have in the context of today's big data world is the fact that we are unable to process enormous amounts of data in a timely manner. In this piece, the authors will use drive HQ cloud to analyse and evaluate two different supervised multiplication systems that are built on service cluster applications. Spark, on the other hand, provides a framework for managing data that is more dependable, and also has the ability to address concerns such as the loss of nodes and the duplication of data. Although it comes at the expense of insufficient failure organization, this study issue has the ability to considerably increase pace effectiveness, which is something many research/industry companies are interested in. A soon-to-be-released study will examine the methods on bigger datasets, especially in cases where the data cannot be totally stored in memory.
Preferential Selection of Software Quality Models Based on a Multi-Criteria Decision-Making Approach

Software engineering mainly aims to produce software of good quality that is delivered on time and on budget. Software quality becomes an important concern for quantifying the performance of software attributes. The seminal objective of the work is to choose the appropriate software quality model according to the client's needs where the client can give more importance to specific criteria compared to others as per his/her application's requirements. The proposed approach will help to decide the best alternative suitable for the application. The work is based on selecting the most suitable software quality model taking all the parameters into consideration while making the decision using multi-criteria decision-making techniques.
Inven!RA Architecture for Sustainable Deployment of Immersive Learning Environments

The objective of this work was to support the sustainable deployment of immersive learning environments, which face varied obstacles, including the lack of support infrastructures for active learning pedagogies. Sustainability from the perspective of the integration of these environments in educational practice entails situational awareness, workload, and the informed assessment ability of participants, which must be supported for such activities to be employed in a widespread manner. We have approached this wicked problem using the Design Science Research paradigm and produced the Inven!RA software architecture. This novel result constitutes a solution for developing software platforms to enable the sustainable deployment of immersive learning environments. The Inven!RA architecture is presented alongside four demonstration scenarios employed in its evaluation, providing a means for the situational awareness of immersive learning activities in support of pedagogic decision making.
Clinical Screening Prediction in the Portuguese National Health Service: Data Analysis, Machine Learning Models, Explainability and Meta-Evaluation

This paper presents an analysis of the calls made to the Portuguese National Health Contact Center (SNS24) during a three years period. The final goal was to develop a system to help nurse attendants select the appropriate clinical pathway (from 59 options) for each call. It examines several aspects of the calls distribution like age and gender of the user, date and time of the call and final referral, among others and presents comparative results for alternative classification models (SVM and CNN) and different data samples (three months, one and two years data models). For the task of selecting the appropriate pathway, the models, learned on the basis of the available data, achieved F1 values that range between 0.642 (3 months CNN model) and 0.783 (2 years CNN model), with SVM having a more stable performance (between 0.743 and 0.768 for the corresponding data samples). These results are discussed regarding error analysis and possibilities for explaining the system decisions. A final meta evaluation, based on a clinical expert overview, compares the different choices: the nurse attendants (reference ground truth), the expert and the automatic decisions (2 models), revealing a higher agreement between the ML models, followed by their agreement with the clinical expert, and minor agreement with the reference.
Practical Approaches to Machine Learning for 5G and Beyond Wireless Network

Wireless communication is now the market segment that is expanding the fastest, and this is because it can offer ubiquitous access to a wide range of applications and services at very low costs. This issue makes it difficult to analyze energy consumption and maximize that energy. Additionally, it might raise certain financial and environmental issues. Modern energy service companies are working to develop and implement energy solutions using cutting-edge technologies. Machine learning is overemphasized by all data scientists while being a widely used technology in the fields of advanced sciences. Automated decision-making is the foundation for advanced machine learning features. It has been noted that every industry is attempting to adopt and utilize machine learning and artificial intelligence in order to reduce reliance on humans. As the field of information technology continues to advance quickly, developers are working to incorporate machine learning for energy management in wireless systems.
A Day/Night Leader-Following Method Based on Adaptive Federated Filter for Quadruped Robots

The quadruped robots have superior adaptability to complex terrains, compared with tracked and wheeled robots. Therefore, leader-following can help quadruped robots accomplish long-distance transportation tasks. However, long-term following has to face the change of day and night as well as the presence of interference. To solve this problem, we present a day/night leader-following method for quadruped robots toward robustness and fault-tolerant person following in complex environments. In this approach, we construct an Adaptive Federated Filter algorithm framework, which fuses the visual leader-following method and the LiDAR detection algorithm based on reflective intensity. Moreover, the framework uses the Kalman filter and adaptively adjusts the information sharing factor according to the light condition. In particular, the framework uses fault detection and multisensors information to stably achieve day/night leader-following. The approach is experimentally verified on the quadruped robot SDU-150 (Shandong University, Shandong, China). Extensive experiments reveal that robots can identify leaders stably and effectively indoors and outdoors with illumination variations and unknown interference day and night.
Anomaly detection in images with shared autoencoders

Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased toward one class (normal) due to the insufficient sample size of the other class (abnormal). We introduce a novel model that utilizes two decoders to share two encoders, respectively, forming two sets of network structures of encoder-decoder-encoder called EDE, which are used to map image distributions to predefined latent distributions and vice versa. In addition, we propose an innovative two-stage training mode. The first stage is roughly the same as the traditional autoencoder (AE) training, using the reconstruction loss of images and latent vectors for training. The second stage uses the idea of generative confrontation to send one of the two groups of reconstructed vectors into another EDE structure to generate fake images and latent vectors. This EDE structure needs to achieve two goals to distinguish the source of the data: the first is to maximize the difference between the fake image and the real image; the second is to maximize the difference between the fake latent vector and the reconstructed vector. Another EDE structure has the opposite goal. This network structure combined with special training methods not only well avoids the shortcomings of generative adversarial networks (GANs) and AEs, but also achieves state-of-the-art performance evaluated on several publicly available image datasets.
Scientometrics-Based Research Status and Hot Topics Analysis of Chinese Private Colleges under Policy Guidance

Private colleges play an important role in the cause of education, explore the research landscape and trends in development, and provide theoretical support and management practices for high-quality, healthy, and sustainable development. Based on a bibliometric analysis, and employing techniques such as scientometrics, social network analysis, a two-dimensional matrix, and textual analysis, this study analyzed the research literature on private colleges in the CSSCI database from 1998 to 2021, along with relevant policies from the legal literature database. This analysis addressed the dimensions of spatial and temporal distribution, author cooperation, primary journals, and research topics. The results showed a significant increase in the number of research papers published by private colleges, although with an uneven regional distribution of research development and differing influence and attention. The primary research forces of private colleges were mainly concentrated in the Jiangsu and Zhejiang regions. For scientific research cooperation, several core research groups were formed. According to the content analysis of relevant policies, the research of private universities has gone through three evolutionary stages: the lead period, the development period, and the adjustment period. Scholars in the lead period focused on learning about the advanced systems and concepts of foreign private colleges, while conducting research from a macro perspective. During the development period, the focus was on researching the management modes of Chinese private colleges from the medium point of view, while exploring different operating modes. During the adjustment period, private colleges research has returned to the macro level, exploring the classified management and governance of private colleges and re-examining China’s private colleges.
Cognitive assistance for action selection: Challenges and approaches

Cognitive assistance systems aim at compensating shortcomings of natural cognition concerning specific activities. Notable progress has been made regarding data acquisition, analysis, and the exploration of technical means for supporting human action selection and execution. The related challenges and potential solutions can be associated to four largely independent questions: What actions should be executed, when this must or should be done, whether assistance is needed for a specific action, and if so, how the action should be supported. A broad range of technological and methodical approaches can be taken for tackling each of these issues, including recent advances and new challenges in the automatized analysis of task-related mental representation structures.
An Intelligent Platform for Software Component Mining and Retrieval

The development of robotic applications necessitates the availability of useful, adaptable, and accessible programming frameworks. Robotic, IoT, and sensor-based systems open up new possibilities for the development of innovative applications, taking advantage of existing and new technologies. Despite much progress, the development of these applications remains a complex, time-consuming, and demanding activity. Development of these applications requires wide utilization of software components. In this paper, we propose a platform that efficiently searches and recommends code components for reuse. To locate and rank the source code snippets, our approach uses a machine learning approach to train the schema. Our platform uses trained schema to rank code snippets in the top k results. This platform facilitates the process of reuse by recommending suitable components for a given query. The platform provides a user-friendly interface where developers can enter queries (specifications) for code search. The evaluation shows that our platform effectively ranks the source code snippets and outperforms existing baselines. A survey is also conducted to affirm the viability of the proposed methodology.
HetSev: Exploiting Heterogeneity-Aware Autoscaling and Resource-Efficient Scheduling for Cost-Effective Machine-Learning Model Serving

To accelerate the inference of machine-learning (ML) model serving, clusters of machines require the use of expensive hardware accelerators (e.g., GPUs) to reduce execution time. Advanced inference serving systems are needed to satisfy latency service-level objectives (SLOs) in a cost-effective manner. Novel autoscaling mechanisms that greedily minimize the number of service instances while ensuring SLO compliance are helpful. However, we find that it is not adequate to guarantee cost effectiveness across heterogeneous GPU hardware, and this does not maximize resource utilization. In this paper, we propose HetSev to address these challenges by incorporating heterogeneity-aware autoscaling and resource-efficient scheduling to achieve cost effectiveness. We develop an autoscaling mechanism which accounts for SLO compliance and GPU heterogeneity, thus provisioning the appropriate type and number of instances to guarantee cost effectiveness. We leverage multi-tenant inference to improve GPU resource utilization, while alleviating inter-tenant interference by avoiding the co-location of identical ML instances on the same GPU during placement decisions. HetSev is integrated into Kubernetes and deployed onto a heterogeneous GPU cluster. We evaluated the performance of HetSev using several representative ML models. Compared with default Kubernetes, HetSev reduces resource cost by up to 2.15× while meeting SLO requirements.
Deep Image Clustering Based on Label Similarity and Maximizing Mutual Information across Views

Most existing deep image clustering methods use only class-level representations for clustering. However, the class-level representation alone is not sufficient to describe the differences between images belonging to the same cluster. This may lead to high intra-class representation differences, which will harm the clustering performance. To address this problem, this paper proposes a clustering model named Deep Image Clustering based on Label Similarity and Maximizing Mutual Information Across Views (DCSM). DCSM consists of a backbone network, class-level and instance-level mapping block. The class-level mapping block learns discriminative class-level features by selecting similar (dissimilar) pairs of samples. The proposed extended mutual information is to maximize the mutual information between features extracted from views that were obtained by using data augmentation on the same image and as a constraint on the instance-level mapping block. This forces the instance-level mapping block to capture high-level features that affect multiple views of the same image, thus reducing intra-class differences. Four representative datasets are selected for our experiments, and the results show that the proposed model is superior to the current advanced image clustering models.
Fast Segmentation Method of Sonar Images for Jacket Installation Environment

It has remained a hard nut for years to segment sonar images of jacket installation environment, most of which are noisy images with inevitable blur after noise reduction.For the purpose of solutions to this problem, a fast segmentation algorithm is proposed on the basis of the gray value characteristics of sonar images.This algorithm is endowed with the advantage in no need of segmentation thresholds.To realize this goal, we follow the undermentioned steps: first, calculate the gray matrix of the fuzzy image background.After adjusting the gray value, the image is divided into three regions: background region, buffer region and target regions.After filtering, we reset the pixels with gray value lower than 255 to binarize images and eliminate most artifacts.Finally, the remaining noise is removed by morphological processing.The simulation results of several sonar images show that the algorithm can segment the fuzzy sonar images quickly and effectively.Thus, the stable and feasible method is testified.
Pengaruh Gender terhadap Prestasi Belajar Mahasiswa dengan Pendekatan SEM-PLS

This study aims to determine the effect of gender on student achievement at the Medica Farma Husada Mataram Polytechnic in terms of activities, student learning styles and lecturer performance. In this study using SEM-PLS analysis with SmartPLS software. The sampling technique used is a simple random sample with a total sample of 92 students of the Polytechnic Medica Farma Husada Mataram with details of 49 male students and 43 female students. The results showed that the percentage of the influence of activity, learning style and lecturer performance on learning achievement for the female gender was 53% while the remaining 47% was influenced by other factors. As for the male gender, the magnitude of the influence of activity, learning style and lecturer performance on learning achievement is 55% while the remaining 45% is influenced by other factors. In conclusion, the influence of learning styles, both female and male gender, is significant on learning achievement.&#x0D; &#x0D; Keywords: SEM-PLS, Gender, learning achievement
Preservice teachers’ technological pedagogical content knowledge development: A bibliometric review

Technological pedagogical content knowledge (TPACK) has become an emerging topic in teacher education research. This review aims to analyze the global trends of the research on preservice teachers’ technological pedagogical content knowledge (TPACK) development. The bibliometric approach examines preservice teachers’ TPACK by identifying the most cited publications, journals, authors, funding agencies, and keywords used in the eligible studies (N = 114). The results show that preservice teachers’ TPACK research began in 2007 and the scientific community’s interest in this subject has been irregular. This study presents a global perspective on preservice teachers’ TPACK and provides researchers with future directions.
Spectrum Sensing Using Optimized Deep Learning Techniques in Reconfigurable Embedded Systems

The exponential growth of Internet of Things (IoT) and 5G networks has resulted in maximum users, and the role of cognitive radio has become pivotal in handling the crowded users.In this scenario, cognitive radio techniques such as spectrum sensing, spectrum sharing and dynamic spectrum access will become essential components in Wireless IoT communication.IoT devices must learn adaptively to the environment and extract the spectrum knowledge and inferred spectrum knowledge by appropriately changing communication parameters such as modulation index, frequency bands, coding rate etc., to accommodate the above characteristics.Implementing the above learning methods on the embedded chip leads to high latency, high power consumption and more chip area utilisation.To overcome the problems mentioned above, we present DEEP HOLE Radio systems, the intelligent system enabling the spectrum knowledge extraction from the unprocessed samples by the optimized deep learning models directly from the Radio Frequency (RF) environment.DEEP HOLE Radio provides (i) an optimized deep learning framework with a good trade-off between latency, power and utilization.(ii) Complete Hardware-Software architecture where the SoC's coupled with radio transceivers for maximum performance.The experimentation has been carried out using GNURADIO software interfaced with Zynq-7000 devices mounting on ESP8266 radio transceivers with inbuilt Omni directional antennas.The whole spectrum of knowledge has been extracted using GNU radio.These extracted features are used to train the proposed optimized deep learning models, which run parallel on Zynq-SoC 7000, consuming less area, power, latency and less utilization area.The proposed framework has been evaluated and compared with the existing frameworks such as RFLearn, Long Term Short Memory (LSTM), Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN).The outcome shows that the proposed framework has outperformed the existing framework regarding the area, power and time.Moreover, the experimental results show that the proposed framework decreases the delay, power and area by 15%, 20% 25% concerning the existing RFlearn and other hardware constraint frameworks.
Blockchain and Data Integrity Authentication Technique for Secure Cloud Environment

Nowadays, numerous applications are associated with cloud and user data gets collected globally and stored in cloud units.In addition to shared data storage, cloud computing technique offers multiple advantages for the user through different distribution designs like hybrid cloud, public cloud, community cloud and private cloud.Though cloud-based computing solutions are highly convenient to the users, it also brings a challenge i.e., security of the data shared.Hence, in current research paper, blockchain with data integrity authentication technique is developed for an efficient and secure operation with user authentication process.Blockchain technology is utilized in this study to enable efficient and secure operation which not only empowers cloud security but also avoids threats and attacks.Additionally, the data integrity authentication technique is also utilized to limit the unwanted access of data in cloud storage unit.The major objective of the projected technique is to empower data security and user authentication in cloud computing environment.To improve the proposed authentication process, cuckoo filter and Merkle Hash Tree (MHT) are utilized.The proposed methodology was validated using few performance metrics such as processing time, uploading time, downloading time, authentication time, consensus time, waiting time, initialization time, in addition to storage overhead.The proposed method was compared with conventional cloud security techniques and the outcomes establish the supremacy of the proposed method.