Reviewerly/exHarmony huggingface dataset:
Research on Teaching Effect Evaluation Model of Single Chip Microcomputer Principle Based on Fruit Fly Optimization Algorithm

Taking the Outcome Based Education (OBE) as the general guiding ideology and taking the single-chip principle course as the carrier, the teaching effect evaluation model of the single-chip principle are studied based on the support vector machine (SVM) and fruit fly optimization algorithm (FOA). By this model, the accuracy and efficiency of the single chip principle teaching effect evaluation can be improved. According to the relevant theory, a multi-index teaching effect evaluation system is constructed, in which the main body is the teachers and students. The FOA is used to solve the optimal solution of the kernel function parameters and regularization parameters of the least squares SVM, and the evaluation process of the least squares SVM is optimized. From the experimental results, it can be seen that the method has the best evaluation effect on the evaluation of the teaching effect of the principle of single-chip microcomputer, and the training time is the shortest.
A Novel Layered GSP Incentive Mechanism for Federated Learning Combined with Blockchain

Federated Learning (FL) has shown great potential as a solution to the problem of data islands. It enables collaborative modeling while adhering to data privacy and security. But how to ensure participants remain active in FL and make rewards reasonable are issues. Although various game theory models, such as Stackelberg, Cournot, and Leader price, give a pricing method for participants, these models don’t consider the roles in FL and the security. We presented the FL incentive mechanism, B-LSP, based on the Generalized Second Price Auction (GSP). This mechanism can overcome the issue of unmanageable incentives while calculating the reward values. Furthermore, a magnitude stratification is introduced to ensure the participants remain active and the basic need for data volume in FL. The requester sets the basic volume requirement for participants, Initial, and volume comparison standard, Interval, to ensure the basic effectiveness of FL and categorize participants into different layers according to their data volume. In B-LSP, requester can control its costs and keep the federation’s stability. And the blockchain is a crucial part to audit everyone’s contribution and guarantee our mechanism safe. The analysis results show that the B-LSP is more reasonable and scientific, and it satisfies both security and traceability when compared to other game theory models.
Spatial Relationship-Driven Computer Vision Image Data Set Annotation

Modern machine learning (ML) is based to a great extent on supervised deep learning models that require large amounts of labeled training data. While image data sets with annotations exist, the annotations are produced manually and possess relatively simple descriptions. To date, none of the freely available labeled image data sets incorporate spatial reasoning, one of Gardner's nine human intelligences. This article presents a new process with open source tools provided to label imagery based on spatial interactions between image objects and auto-mated reasoning under uncertainty. The resulting annotated data can be used to train new ML/AI algorithms and/or help us better understand existing methodologies.
CNN Model for Segmenting Whole Slide Images of the Kidney

In recent years, the classification and detection of glomeruli images has now become the most important study field in renal disease therapy. With the advancement of computer-aided systems, a greater emphasis has been placed on deep learning for glomerular detection. In this paper, a segmentation-based model using deep learning has been developed to detect glomeruli in images of human renal tissues. The performance of the proposed deep learning model has been evaluated in terms of different performance metrics to validate the results and ensure the system's efficiency. With the proposed model, the achieved accuracy, precision, recall, and F1-Score comes out to be 94.7%, 95.4%, 93.1%, and 94.2%, respectively, which demonstrate that the proposed model does a better job than the current state of methods.
Value-Based Preference Aggregation Argument Framework and Its Application

In this paper, a value-based preference graph G of a decision-making problem is presented by using a method similar to Borda-counting to quantify the preferences of agents holding different values for the alternatives in the decision problem, and then a Value-based Preference Aggregation Argument Framework (VPAAF, an extended VAF theory) generated fromelimiate G is introduced. In VPAAF, a defeating relation between arguments is redefined by an aggregate function, which is used to aggregate individual preferences into group preferences. The defeating relation helps us almost eliminate the odd-cycles (or even-cycles) in the VPAAF, allowing obtain an extension containing only a single set of arguments. The argument framework not only achieves the goal of exploring some decision-making problems, which include resolving voting paradox to a certain extent and expanding the application field of VAF, but also explicitly reveals the value factors implied in the decision results, which makes it possible to do retrospective reasoning of the results by virtue of arguments.
Parallel Classification of Cells in Thinprep Cytology Test Image for Cervical Cancer Screening

Deep learning has proven to be an effective approach to read whole-slide cytologic images for computer-assisted cervical cancer screening. To construct such a pipeline, it is often necessary to train a classifier, which decides if a patch (for example, sized $$224\times 224$$ ) provides a positive cell reading or not. Following the clinical guidance, pathologists must label many such patches of both negative (N) and positive (P) cells. Then, a deep network can be trained for the binary N-P classification problem. In this paper, we take advantage of the complexity and illegibility of the cells that are negative according to current clinical guidance, to further improve the classification performance. We claim that the negative patches can be divided into two types: easy-negative (EN) and hard-negative (HN). The initial N-P binary classification can then be converted to an EN-HN-P triple-class problem. We also align the EN-HN and HN-P decision planes in parallel in the latent feature space where all input patches are encoded. The dual planes perform parallel classification then, following a well-planned curriculum learning scheme. Our results show that the proposed method can greatly enhance the performance of classifying positive patches and negative patches by using the better learned latent space and the related encoding of each patch.
Outlier Identification for Symbolic Data with the Application of the DBSCAN Algorithm

Outliers have a significant negative impact on the data quality, data analysis results. If a large dataset contains only few outliers it is essential to identify them and then remove them (e.g., for credit card transactions only some of them will be fraudulent) or not. The paper presents an application of ensemble learning for symbolic data as a tool for outlier detection, where the DBSCAN (density-based SCAN) algorithm is applied. In the empirical part ensemble learning and single DBSCAN algorithm is used to detect outliers in an unbalanced data sets. The results show that ensemble approach can be efficiently used to detect outliers in symbolic data sets.
SOCIAL COMMENTARY IN A FAIRY SONG POEM BY WILLIAM SHAKESPEARE: A STRUCTURALISM STUDY

Social commentary is one aspect related to society in seeing a phenomenon that occurs. The researcher see a phenomenon that occurs in a community environment and is represented by poem. Where poem used figurative language to beautify writing. The object in this research is a poem by William Shakespeare entitled A Fairy Song. This research aims to explain the figurative language contained in A Fairy Song and also to analyze the social comments are found in A Fairy Song. This research used the structuralism theory proposed by Strauss, Wellek and Warren. The sociological literary criticism proposed by Marx and Lukacs. The methodology that used in this research is qualitative method approach with the phenomenological approach by Husserl. There are five figurative language contained in A Fairy Song poem such as alliteration, hyperbole, simile, metaphor and personification. The figurative language here is also used as a social commentary which generates criticism of this poem. Social comments are found because of the words that use figurative language to interpret a meaning. The researcher found the social comments from the used of figurative language such as injustice, discrimination and exploitation.&#x0D;
Cascaded Video Generation for Videos In-the-Wild

Videos can be created by first outlining a global view of the scene and then adding local details. Inspired by this idea we propose a cascaded model for video generation which follows a coarse to fine approach. First our model generates a low resolution video, establishing the global scene structure, which is then refined by subsequent cascade levels operating at larger resolutions. We train each cascade level sequentially on partial views of the videos, which reduces the computational complexity of our model and makes it scalable to high-resolution videos with many frames. We empirically validate our approach on UCF101 and Kinetics-600, for which our model is competitive with the state-of-the-art. We further demonstrate the scaling capabilities of our model and train a three-level model on the BDD100K dataset which generates 256x256 pixels videos with 48 frames.
Light-weight Frequency Information Aware Neural Network Architecture for Voice Spoofing Detection

The voice assistant market is overgrowing, and mainstream services like Bixby (Samsung), Alexa (Amazon), and Siri (Apple) are quickly being upgraded to support advanced commands. Such capabilities make them lucrative targets for attackers to exploit. Voice spoofing attacks involve recording voice commands of a target victim and simply replaying them through a loudspeaker. The "2019 Automatic Speaker Verification Spoofing And Countermeasures Challenge" (ASVspoof) competition aims to facilitate the design of highly accurate voice spoofing attack detection systems. However, most of the presented models do not take frequency-level modeling into account in their modeling architecture and do not consider model complexity. To design a light-weight system with frequency-level modeling, we propose two systems: 1) Double Depthwise Separable (DDWS) convolution and 2) BC-ResNet with max feature map (MFM) activation (BC-ResMax). We evaluate the accuracy by equal error rate (EER) using the ASVspoof 2019 dataset. Our single models of parallel DDWS, sequential DDWS, and BC-ResMax model achieved spoofing attack detection EER of 2.63%, 2.08% and 2.59% in the LA dataset, and 0.47%, 0.63% and 0.49% in the PA dataset, achieving comparable performance with other top ensemble systems from the competition. Furthermore, parallel DDWS, sequential DDWS, and BC-ResMax used only 45K, 28K and 29K numbers of parameters which are far fewer than existing models.
Web Service Selection Based on QoS Prediction for Clustering and Ranking Services Using Auto-Encoder and K-Means

When selecting web services, users look for those that meet their requirements, primarily the overall functionality and non-functionality quality of service (QoS). In general, various service providers offer a large number of functionally similar services. That makes it very hard for users to find the best ones that satisfy their needs. Thus, service selection based on QoS has emerged as a challenging problem in service computing. So, the authors propose in this paper a web service selection method based on QoS prediction for clustering and ranking services using auto-encoder and k-means. Experiment results show that the proposed method efficiently improves the services' selection accuracy.
Understanding the Impact of the Ontology of Semantic Web in Knowledge Representation: A Systematic Review

This paper is a systematic review of 19 resources showing the relationship between semantic web and ontology and answers several queries on the implications of this relationship on knowledge representation and real-life solutions in various industries. PRISMA guidelines were used to select the papers and the authors focused on the most important 10 resources papers to investigate the research questions. It is concluded that semantic-web ontologies are one of the solid foundations of knowledge representation and suggested further research on focused industry to further elaborate on a specific implications of semantic web ontology on knowledge representation and associated proposed solutions in a specific industry.
A Framework of Maximum Feature Exploration Oriented Remote Sensing Object Detection

Object detection from remote sensing images is an interesting research topic full of theoretical and application values. Compared with generic images, those of remote sensing contain much less distinguishing representation information due to far-away capturing position and limited camera lens resolution, the perpendicular to ground capturing view, almost unacquirable 3-D object structure and serious roof occlusion, and the inherent unimportance of top roof view. Focusing on this intrinsic disadvantage, in this letter, we propose a general framework of accurate remote sensing object localization constructed with the core idea of maximum image feature exploration. This integrated structure is mainly comprised of cascaded tiny patch correlation (CTPC) based feature digging, averaging local patch regression (ALPR) centered coarse location acquisition, and instance segmentation oriented further refinement (ISOFR). All those components are designed to maximize feature digging and usage. We believe this is the first to implement the idea of maximum feature exploration to each corner of object detection architecture. Extensive experiments on challenging aerial image datasets DOTA and NWPU VHR-10 show state-of-the-art performance.
Extraction of Complementary Topics Based on Phrase Importance and Co-occurrence in Technical Blogs

When acquiring knowledge in a certain field, it is important to obtain comprehensive information. However, it is difficult for users to extract missing information in unknown fields by themselves. Therefore, this study proposes a subtopic extraction method that can supplement missing information step-wise by presenting the user with important unknown phrases related to the topic the user is currently learning about by performing Web searches; the phrases are such that they are highly relevant to the user’s browsing history. We then conducted small-scale experiments and found that topic extraction using phrase co-occurrence as a reference for the next search keyword to be entered is effective compared to conventional topic extraction using LexRank.
A Deep Learning based hybrid model for improving accuracy of sentiment analysis

Text Sentiment analysis has been of great importance over the last few years. It is being widely used to determine a person’s feelings, opinions, and emotions on any topic or for someone. In recent years CNN and LSTM have been widely used to develop such models. CNN has shown that it can effectively extract local information between consecutive words, but it lacks in extracting contextual semantic information between words. Although LSTM is able to extract some contextual information, it lacks CNN in extracting local information. To counter such problems, we used the attention mechanism in our multichannel CNN with a Bidirectional LSTM model to give attention to those parts of a sentence which have a major influence in determining the sentiment of that sentence. Experimental results have shown that our MultiChannel CNN model with Bidirectional LSTM and Attention mechanism has achieved an accuracy of 93.61% which has outperformed the traditional LSTM-CNN, CNN models, and many machine learning algorithms.
Classification of Healthcare Data Using T-BMSVM in Big Data

With the fast pace in collating big data healthcare framework, a streamlining of machine learning algorithms together with apache spark has been designed for effective classification of images and stages of lung cancer to the greatest extent. We experiment on a combination of binary classification (SVM-non linear SVM with Radial Basis Function RBF) and Multi-class classification (WTA-SVM winner-takes-all with support vector machine) with threshold technique (T-BMSVM) to classify nodules into malignant or benign nodules and also their malignancy levels respectively. We have argued for handling and processing large sizes of data sets as sputum cell images in the field of classification using the map-reduce framework in Pyspark, which works better with Apache spark. The outsourced outcomes reveal that classification works better with Multi-class SVM, therefore, may be considered as a promising tool to diagnose the stages of nodules. Also, Scalability and convergence analysis embed to prove the improving results of multi-class classification than SVM.
Fee-Based Service as A Human Resource Development (Librarian) in Library of Gadjah Mada University

Fee-based services have many impacts on the performance of optimizing services in library. In addition to optimal services provided to users, the impact is also felt by librarians as human resources who provide fee-based . Researchers want to identify the effect of fee-based in the application of intellectual abilities and professionalism of human resources, namely librarians at Library of Gadjah Mada University. Librarians as human resources have an important role in optimizing the services in the library. This study uses a case study research method with data collection techniques using interviews as primary data and literature studies as secondary data. The results of this study indicate that the application of fee-based can test the intellectual abilities and professionalism of librarians in providing services to users. Librarians are required to apply library science in understanding the needs of users in more detail due to the fees charged for the services provided. A balanced view between the application of fee-based with the characteristics of libraries as institutions that provide benefits to the public supports the optimization of services provided to both special and general users at Library of Gadjah Mada University.
Reinforcement Learning Applied to Agent Path Planning

This paper provides an enhanced Q-learning algorithm for agent path planning in traditional grid maps, which solves the problem of agent path planning in grid maps. In an unknown environment, the classic Q-learning algorithm addresses the agent's path planning problem. However, there are several limits to this strategy in terms of path planning: the agent could only move nearby grids, and the step size is only one frame. Improved Q-learning algorithm, including changes the agent's direction and step size. The action direction of agents is increased from four to eight. The movement step of agents is raised from one to three frames. The new method makes the convergence faster and proxy path smoother. Finally, a set of simulation tests are presented to validate the modified Q-learning algorithm.
Study on the yaw axis mechanism of reproduction robot suits

We have proposed a new robot suit system. The robot suit system consists of two robot suits. One is the robot suit to trace the motion by the encoders, and another is the robot suit to execute the motion by the motors. We have developed the new yaw axis mechanism, which is the arc-shaped mechanism using a rack and a pinion. We experimented in the basic arm motion. We confirmed the joint angles measured by the new yaw mechanism and the wrist positions obtained by the forward kinematics. We are going to develop the new yaw mechanism of the robot suit to execute the motion.
Adaptive Quantum Tomography in Weak Measurement with Superconducting Circuits

Adaptive tomography has been widely investigated to obtain faster state tomography process for quantum computation. Infidelity [1 − F(ρ,σ)] on nearly pure states in quantum information process scales generally as $O\left( {1/\sqrt N } \right)$, which requires a large number of statistical ensembles in comparison to the infidelity scaling on mixed states of O(1/N). Recently, optimization of a measurement basis in a photonic qubit system, whose state tomography uses projective measurements, reported improved infidelity scaled as O(1/N). However, this dramatic improvement cannot be applied to a weak-value based measurement system, which can be attributed to the fact that one cannot distinguish two quantum states with perfect measurement reliability. We introduce new optimal measurement basis to achieve a fast adaptive quantum state tomography and minimum magnitude of infidelity in this weak measurement system. This novel protocol allows us to accomplish approximately 80% error reduction without changing the scaling of $O\left( {1/\sqrt N } \right)$ in numerical simulations and realize approximately 90% error reduction in superconducting circuit QED experiments.
FRISPEE: FRI-Based Single Image Super-Resolution with Deep Recursive Residual Network

In this paper, we propose a novel single image super-resolution algorithm that integrates a model-based approach with self-learning deep networks. The proposed method can be adapted to low-resolution (LR) images obtained with real acquisition devices where the point spread function is Gaussian-like. By modelling natural image lines as piece-wise smooth functions and approximating the blurring kernel with B-splines, an intermediate high-resolution (HR) image can be first obtained based on Finite Rate of Innovation theory. A self-supervised deep recursive residual network is then applied to further enhance the reconstruction quality. From the simulation results, our algorithm outperforms other self-learning algorithms and achieves state-of-the-art performance.
Triplet Embedding Convolutional Recurrent Neural Network for Long Text Semantic Analysis

Deep Recurrent Neural Network has an excellent performance in sentence semantic analysis. However, due to the curse of the computational dimensionality, the application in the long text is minimal. Therefore, we propose a Triplet Embedding Convolutional Recurrent Neural Network for long text analysis. Firstly, a triplet from each sentence of the long text. Then the most crucial head entity into the CRNN network, composed of CNN and Bi-GRU networks. Both relation and tail entities are input to a CNN network through three splicing layers. Finally, the output results into the global pooling layer to get the final results. Entity fusion and entity replacement are also used to retain the text’s structural and semantic information before triplet extraction in sentences. We have conducted experiments on a large-scale criminal case dataset. The results show our model significantly improves the judgment prediction task.