Reviewerly/exHarmony huggingface dataset:
Detection Of Tumor Affected Part From Histopathological Bone Images Using Morphological Classification And Recurrent Convoluted Neural Networks

The bone tumor (BT) remains a sickness type that is marked by unlimited cell growth and results in numerous early demises worldwide. Hence, diagnosing BT in the earlier phases and classifying them turned into a vital job for healing the sick person. Springing out of the bone, BT disseminates throughout the body swiftly impacting sick persons. By assessing histopathological images (HpIs), BT’s swift and early prognosis could be started. Centered upon this, a survey has been performed on BT identification employing diverse approaches to image processing and observed that there remain several issues, and the adversities did by BT enhance while it is unidentified at the right time. The abnormalities in the bone image are identified from the region of interest. For these problems, many research works were carried out to develop several Computer-aided diagnosis systems, having as a major challenge to define the features that better represent the images to classify. To overcome the problem this paper aims to develop Recurrent CONVoluted neural networks (Rec-CONVnet) based classification for the assessment of tumor type based on analyzing MRI images. For the smoothing process wiener filter is used to reduce the mean square error (MSE) and hence image enhancement is done by using a convoluted Gabor filter. The performance analysis is done by comparing with three traditional methods such as Dense Convolutional neural network (DCNN), Random Forest (RF), and Decision Tree (DT) in terms of accuracy, sensitivity specificity, f1 score, recall, and is found that the proposed Rec-CONVnet achieves 98.34% of accuracy, 97.96% of precision, 98.72% of recall, 98.33% of specificity and 98.32% of f1-score.
A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment

The rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendation has become an important task, which provides the best POI recommendation according to the user’s recent check-in sequences. However, all existing methods for the next POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences but ignore the significant fact that the next POI recommendation is a time-subtle recommendation task. In view of the fact that the attention mechanism does not comprehensively consider the influence of the user’s trajectory sequences, time information, social relations and geographic information of Point-of-Interest (POI) in the next POI recommendation field, a Context Geographical-Temporal-Social Awareness Hierarchical Attention Network (CGTS-HAN) model is proposed. The model extracts context information from the user’s trajectory sequences and designs a Geographical-Temporal-Social attention network and a common attention network for learning dynamic user preferences. In particular, a bidirectional LSTM model is used to capture the temporal influence between POIs in a user’s check-in trajectory. Moreover, In the context interaction layer, a feedforward neural network is introduced to capture the interaction between users and context information, which can connect multiple context factors with users. Then an embedded layer is added after the interaction layer, and three types of vectors are established for each POI to represent its sign-in trend so as to solve the heterogeneity problem between context factors. Finally reconstructs the objective function and learns model parameters through a negative sampling algorithm. The experimental results on Foursquare and Yelp real datasets show that the AUC, precision and recall of CGTS-HAN are better than the comparison models, which proves the effectiveness and superiority of CGTS-HAN.
&lt;i&gt;Verikube&lt;/i&gt;: Automatic and Efficient Verification for Container Network Policies

Recently, Linux Container has been the de-facto standard for a cloud system, enabling cloud providers to create a virtual environment in a much more scaled manner. However, configuring container networks remains immature and requires automatic verification for efficient cloud management. We propose Verikube, which utilizes a novel graph structure representing policies to reduce memory consumption and accelerate verification. Moreover, unlike existing works, Verikube is compatible with the complex semantics of Cilium Policy which a cloud adopts from its advantage of performance. Our evaluation results show that Verikube performs at least seven times better for memory efficiency, at least 1.5 times faster for data structure management, and 20K times better for verification.
DynaGAN: Dynamic Few-shot Adaptation of GANs to Multiple Domains

Few-shot domain adaptation to multiple domains aims to learn a complex image distribution across multiple domains from a few training images. A naïve solution here is to train a separate model for each domain using few-shot domain adaptation methods. Unfortunately, this approach mandates linearly-scaled computational resources both in memory and computation time and, more importantly, such separate models cannot exploit the shared knowledge between target domains. In this paper, we propose DynaGAN, a novel few-shot domain-adaptation method for multiple target domains. DynaGAN has an adaptation module, which is a hyper-network that dynamically adapts a pretrained GAN model into the multiple target domains. Hence, we can fully exploit the shared knowledge across target domains and avoid the linearly-scaled computational requirements. As it is still computationally challenging to adapt a large-size GAN model, we design our adaptation module to be lightweight using the rank-1 tensor decomposition. Lastly, we propose a contrastive-adaptation loss suitable for multi-domain few-shot adaptation. We validate the effectiveness of our method through extensive qualitative and quantitative evaluations.
Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

Background: Refactoring is changing a software system without affecting the software functionality. The current researchers aim i to identify the appropriate method(s) or class(s) that needs to be refactored in object-oriented software. Ensemble learning helps to reduce prediction errors by amalgamating different classifiers and their respective performances over the original feature data. Other motives are added in this paper regarding several ensemble learners, errors, sampling techniques, and feature selection techniques for refactoring prediction at the class level. Objective: This work aims to develop an ensemble-based refactoring prediction model with structural identification of source code metrics using different feature selection techniques and data sampling techniques to distribute the data uniformly. Our model finds the best classifier after achieving fewer errors during refactoring prediction at the class level. Methodology: At first, our proposed model extracts a total of 125 software metrics computed from object-oriented software systems processed for a robust multi-phased feature selection method encompassing Wilcoxon significant text, Pearson correlation test, and principal component analysis (PCA). The proposed multi-phased feature selection method retains the optimal features characterizing inheritance, size, coupling, cohesion, and complexity. After obtaining the optimal set of software metrics, a novel heterogeneous ensemble classifier is developed using techniques such as ANN-Gradient Descent, ANN-Levenberg Marquardt, ANN-GDX, ANN-Radial Basis Function; support vector machine with different kernel functions such as LSSVM-Linear, LSSVM-Polynomial, LSSVM-RBF, Decision Tree algorithm, Logistic Regression algorithm and extreme learning machine (ELM) model are used as the base classifier. In our paper, we have calculated four different errors i.e., Mean Absolute Error (MAE), Mean magnitude of Relative Error (MORE), Root Mean Square Error (RMSE), and Standard Error of Mean (SEM). Result: In our proposed model, the maximum voting ensemble (MVE) achieves better accuracy, recall, precision, and F-measure values (99.76, 99.93, 98.96, 98.44) as compared to the base trained ensemble (BTE) and it experiences less errors (MAE = 0.0057, MORE = 0.0701, RMSE = 0.0068, and SEM = 0.0107) during its implementation to develop the refactoring model. Conclusions: Our experimental result recommends that MVE with upsampling can be implemented to improve the performance of the refactoring prediction model at the class level. Furthermore, the performance of our model with different data sampling techniques and feature selection techniques has been shown in the form boxplot diagram of accuracy, F-measure, precision, recall, and area under the curve (AUC) parameters.
Data Allocation in Distributed Database based on CSO

Distributed databases (DDBs) provide smart processing of large databases, the problems of fragmentation and allocation are vital design problems in addition to the centralized design. The majority of performance degradation in DDBs is due to the communication cost by query remote access and retrieval of data. This can be optimized through an efficient data allocation approach that will provide flexible retrieval of a query by low cost accessible sites. In this paper, a novel high performance data allocation approach is designed using Chicken Swarm Optimization (CSO) algorithm. Data allocation problem (DAP) is a NP-Hard problem modelled as optimization problem. The proposed data allocation approach initially characterizes the DAP into optimal problem of choosing the appropriate and minimal communication cost provoking sites for the data fragments. Then the CSO algorithm optimally chooses the sites for each of the data fragments without creating much overhead and data route diversions. This enhances the overall distributed database design and subsequently ensures quality replication. The experimental results illustrate that the proposed CSO based intelligent data fragment allocation approach has better performance than most existing approaches and thus signifies the impact of efficient data allocation in DDBs.
Development of Engine Maintenance Technology with Mixed Reality-Taking Pratt &amp; Whitney JT9D as an Example

The engine is the heart of the aircraft and deeply affects performance, flight safety, and airline management, which makes it a critical component requiring maintenance. With the advancement of technology, augmented reality (AR), virtual reality (VR), and mixed reality (MR) technologies have been introduced one after another. To increase maintenance skill ability, it is important to train for aviation maintenance. Therefore, the engine maintenance mixed reality (EMMR) system was developed through mixed reality technology in this study. There are three different scenarios have been developed in the EMMR system for making engine maintenance easy. In the first scenario, an exploded diagram of the disassembled engine with the main components has been made and an animation of the fuel flow has been demonstrated to introduce the basic structure of the engine. In the second scenario, interactive buttons and animations have been used to simulate the removal of the engine from the wing of an airplane so that the users can understand the process of disassembling the engine. In the third scenario, hybrid reality has been implemented to interact with the virtual objects, so that users could simulate the disassembly and replacement of the engine fan blades and get familiar with the whole process. Students have been introduced to and have been familiarized with the Microsoft HoloLens 2 and a survey has been conducted before and after using the developed technology to understand if the currently developed EMMR could help students to understand the process of each engine maintenance in the general classroom and make the learning more efficient at the same time.
Dual-weight local linear embedding algorithm based on adaptive neighborhood

Local linear embedding (LLE) algorithm mainly depends on local structures to extract significant features; however, the local structures are sensitive to the selection of neighborhood parameters. To solve this problem, the dual-weight local linear embedding algorithm based on adaptive neighborhood (AN-DWLLE) is proposed. First, the neighborhood parameter is adaptively estimated by computing the distances between the inquiry point and the centers of the inquiry point’s candidate neighborhoods. Then, the neighbor sequence and the local linear structures are integrated to construct the robust local structure that can be used to extract the significant features. A large number of experiments are performed on two bearing data sets. The experimental results illustrate that, compared with other related methods, the AN-DWLLE algorithm can extract more salient features and has greater dimensionality reduction effect.
Model of formation of digital competences in implementing higher education programs

Objectives. The paper presents and analyzes a model for the formation and evaluation of digital competencies in students. The model is aimed at the implementation of higher education programs for training specialists not directly working in IT, but whose activities are directly related to using ready-made digital products. Digital competences imply an ability to confidently, effectively, and safely select and apply information and communication technologies in various life practices including researching and critically analyzing information, using digital devices and accessing social network functionality, conducting financial and trading transactions, as well as creating digital content. The formation of such digital competences is one of the results of completing higher education programs. Methods. The study is based on a model of digital competence formation having the following four interconnected stages: basic digital competences; personal competences (soft skills); professional digital competences; digital culture. Results. The presented general model for the formation and assessment of student digital competences in higher education programs consists of four interrelated steps, each integral to the process of formation and assessment of the student digital competences, none of which can be excluded without the risk of failing to achieving the specified goals. Conclusions. The model developed in the paper is based on the existing extensive regulatory framework, as well as existing domestic and foreign practices. Relying on expert community opinion (employers, primarily), it accounts for sector- and region-specific features of universities along with specifics of training areas, as well as comprising a list of optimal organizational and methodological conditions for formation of digital competency.
Triadicities of Indonesian Phatic Functions

This study aims to describe the triadicities meaning of Indonesian phatic functions using a culture-specific pragmatic perspective. The data prepared for this study is the triadic meaning of Indonesian phatic functions in a specific culture, i.e., the Javanese. The data were collected and presented using the observation method and involved both conversation and uninvolved conversation techniques. Data analysis was carried out using the identity method. It involved the process of comparing and contrasting data with their external contexts. There were fifteen types of triadic of the pragmatic meaning in Indonesian phatic functions: (1) small talk, (2) courtesy, (3) disappointment, (4) thanking, (5) joking, (6) complimenting, (7) apologizing, (8) avoiding, (9) disagreeing, (10) negating, (11) greeting, (12) offering, (13) emphasizing, and (14) reminding. These triadicities illustrate that Indonesian culture, especially Java, has several phatic functions in pragmatic meaning that give some color to the regional language.
Health data privacy through homomorphic encryption and distributed ledger computing: an ethical-legal qualitative expert assessment study

Increasingly, hospitals and research institutes are developing technical solutions for sharing patient data in a privacy preserving manner. Two of these technical solutions are homomorphic encryption and distributed ledger technology. Homomorphic encryption allows computations to be performed on data without this data ever being decrypted. Therefore, homomorphic encryption represents a potential solution for conducting feasibility studies on cohorts of sensitive patient data stored in distributed locations. Distributed ledger technology provides a permanent record on all transfers and processing of patient data, allowing data custodians to audit access. A significant portion of the current literature has examined how these technologies might comply with data protection and research ethics frameworks. In the Swiss context, these instruments include the Federal Act on Data Protection and the Human Research Act. There are also institutional frameworks that govern the processing of health related and genetic data at different universities and hospitals. Given Switzerland's geographical proximity to European Union (EU) member states, the General Data Protection Regulation (GDPR) may impose additional obligations.To conduct this assessment, we carried out a series of qualitative interviews with key stakeholders at Swiss hospitals and research institutions. These included legal and clinical data management staff, as well as clinical and research ethics experts. These interviews were carried out with two series of vignettes that focused on data discovery using homomorphic encryption and data erasure from a distributed ledger platform.For our first set of vignettes, interviewees were prepared to allow data discovery requests if patients had provided general consent or ethics committee approval, depending on the types of data made available. Our interviewees highlighted the importance of protecting against the risk of reidentification given different types of data. For our second set, there was disagreement amongst interviewees on whether they would delete patient data locally, or delete data linked to a ledger with cryptographic hashes. Our interviewees were also willing to delete data locally or on the ledger, subject to local legislation.Our findings can help guide the deployment of these technologies, as well as determine ethics and legal requirements for such technologies.
Peran Orang Tua dalam Memotivasi Belajar Anak pada Pembelajaran Daring di Desa Gemiring Lor

The purpose of this study is to describe the role of parents in motivating children's learning in online learning in Gemiring Lor Village.The research method used is qualitative with a descriptive approach. Data collection is done by observation, in-depth interviews, documentation, recording. The research was conducted in Gemmiring Lor Village. The object of research is the role of parents in motivating children's learning in online learning for children who are in grade IV and the informants are grade IV students, totaling 3 and their parents. The data analysis used is that there are three lines of qualitative data analysis, namely data reduction, data presentation, and drawing conclusions.The results showed that the role of people in increasing children's learning motivation during online learning during the pandemic by providing encouragement, mentoring for learning, and giving time to play first after that if it was time to study children had to learn to complete the tasks given by the teacher. In addition, giving rewards or prizes so that children have a desire to learn.
Analisis Gerakan Literasi Sekolah Di SDN 2 Bangsri Selama Pertemuan Tatap Muka (PTM) Terbatas

The purpose of this study is to analyze the implementation of School Literacy (GLS) at SDN 2 Bangsri during limited PTM. This study uses a qualitative approach to the type of phenomenological research. The subjects of this study were school principals, fourth grade teachers, and fourth grade students. Data obtained from observation, interviews and documentation. The validity of the data using triangulation of data sources. Data analysis used interactive analysis techniques with stages, namely data collection, data reduction, data presentation, and drawing conclusions. The results showed that SDN 2 Bangsri during the limited offline learning (PTM) continued to carry out the GLS but the implementation was less than optimal as in the habituation stage it was only done for 10 minutes and used theme books continuously, at the development stage students never wrote their reading results in journals, at the learning stage the teacher was less maximum in providing reference books to students.
Joint Cross-Consistency Learning and Multi-Feature Fusion for Person Re-Identification

To solve the problem of inadequate feature extraction by the model due to factors such as occlusion and illumination in person re-identification tasks, this paper proposed a model with a joint cross-consistency learning and multi-feature fusion person re-identification. The attention mechanism and the mixed pooling module were first embedded in the residual network so that the model adaptively focuses on the more valid information in the person images. Secondly, the dataset was randomly divided into two categories according to the camera perspective, and a feature classifier was trained for the two types of datasets respectively. Then, two classifiers with specific knowledge were used to guide the model to extract features unrelated to the camera perspective for the two types of datasets so that the obtained image features were endowed with domain invariance by the model, and the differences in the perspective, attitude, background, and other related information of different images were alleviated. Then, the multi-level features were fused through the feature pyramid to concern the more critical information of the image. Finally, a combination of Cosine Softmax loss, triplet loss, and cluster center loss was proposed to train the model to address the differences of multiple losses in the optimization space. The first accuracy of the proposed model reached 95.9% and 89.7% on the datasets Market-1501 and DukeMTMC-reID, respectively. The results indicated that the proposed model has good feature extraction capability.
Retracted: Research on Classroom Online Teaching Model of “Learning” Wisdom Music on Wireless Network under the Background of Artificial Intelligence

[This retracts the article DOI: 10.1155/2021/3141661.].
Generative models for visualizing idiosyncratic impressions

In their comprehensive review of research on impressions from faces, Sutherland and Young (this issue) highlight both the remarkable progress and the many challenges facing the field. We focus on two of the challenges: the need for generative, powerful models of impressions and the idiosyncratic nature of complex impressions.