Reviewerly/exHarmony huggingface dataset:
A Novel Approach of Complex Intuitionistic Fuzzy Linear Systems in an Electrical Circuit

The paper deals with a new type of intuitionistic fuzzy number, in the complex plane called as complex horizontal relative trapezoidal intuitionistic fuzzy number (CHRTrIFN). Arithmetic operations like addition, multiplicative inverse and division on these numbers are defined and discussed. Complex horizontal relative trapezoidal intuitionistic fuzzy number(CHRTrIFN) and geometric representation of CHRTrIFN and complex trapezoidal intuitionistic fuzzy number(CTrIFN) are presented. CHRTrIFNs are used to solve complex intuitionistic fuzzy linear system of equations and is applied in a RLC intuitionistic fuzzy circuit to find the current flow in the circuit.
Improved Method for Organizing Information Contained in Multiple Documents into a Table

Okazaki et al. (2018) have proposed a method for organizing the information contained in multiple documents into a table without limiting the information to be extracted. In this study, we propose a method for improving the accuracy of these tables. In our proposed method, information is first clustered hierarchically. Next, for the results of hierarchical clustering (with the number of clusters ranging from 1 to n), the degree of filling and the information density of the resulting table are calculated. The number of clusters when the balance between these two indicators is optimal is chosen as the optimal number of clusters. The results of the method using the chosen number of clusters are organized into a table. In the conventional method, the number of clusters estimated by the X-means method tends to be too small. As demonstrated by the results of experiments using 15 types of multiple documents, the proposed method improves this problem, with its estimated number of clusters being closer to the optimum. The average evaluation result in the tables (F-measure) when applying the conventional method was 0.43; the proposed method improves this to 0.65. We therefore confirm the effectiveness of the proposed method.
Classification of the Kanji character in Japanese using the ANFIS technique

Remembering the Kanji character in Japanese is a difficult task for foreign students. To recognize their characteristic, it is necessary to classify the kind of image containing Kanji characters. This research proposes a method to classify such tasks by using the ANFIS technique. We employ a model to learn the feature of the Kanji character image. The numerical result shows a good predictive ability. It also demonstrates a potential application in the educational environment of learning Japanese.
Conceptualizing Re-configurable Business Process: A Context-Driven Approach

Context-driven services offer new opportunities for different stakeholders of a SOA-based Enterprise Architecture Framework (EAF) to cope with the ever-changing business environment, by responding to the current environmental context and adapting its behavior accordingly. In the pre-existing works, context information is directly obtained from various sources like sensors, mobile devices, or inferred from different types of user interactions with various applications. Those processes are time consuming and troublesome task for collecting contextual data and processing it. Moreover, those approaches scarcely provide any suitable mechanism of deriving context-driven services from the given goal, decided by the group of stakeholders of an EAF. Consequently, those methodologies are unable to exhibit proper integration of context-driven services within a Business Process (BP). To resolve this crucial issue, a novel approach of deriving a context-driven re-configurable BP from a given goal has been proposed in this paper. This approach gives privilege to its end-users to re-configure a pre-existing BP by including the current context data to satisfy the newly modified goal. Thus, this method is very much user-centric, re-usable, cost-effective, and less time consuming in nature. An algorithm, namely, Context-Driven Re-configurable Business Process Achievement Algorithm (CDRBPA) has been proposed and implemented in this paper. Finally, a case study has been described to illustrate the proposed approach in detail.
Transforming Education

Cloud computing deployment and demand has increased year over year during the prior decade and has only accelerated during the COVID-19 pandemic. The rapid increase in cloud technology has also increased the need for higher education institutions to train students and re-skill or up-skill working professionals. This paper discusses the application of high impact training methodologies and their impact on student learning.
Enhancing Summarization with Text Classification via Topic Consistency

The recent success of abstractive summarization is partly due to the availability of large-volume and high-quality human-produced summaries for training, which are extremely expensive to obtain. In this paper, we aim to improve state-of-the-art summarization models by utilizing less expensive text classification data. Specifically, we use an eXtreme Multi-label Text Classification (XMTC) classifier to predict relevant category labels for each input document, and impose topic consistency in the system-produced summary or in the document encoder shared by both the classifier and the summarization model. In other words, we use the classifier to distill the training of the summarization model with respect to topical consistency between the input document and the system-generated summary. Technically, we propose two novel formulations for this objective, namely a multi-task approach, and a policy gradient approach. Our experiments show that both approaches significantly improve a state-of-the-art BART summarization model on the CNNDM and XSum datasets. In addition, we propose a new evaluation metric, CON, that measures the topic consistency between the input document and the summary. We show that CON has high correlation with human judgements and is a good complementary metric to the commonly used ROUGE scores.
A Modified Artificial Bee Colony Algorithm Using Accept–Reject Method: Theory and Application in Virtual Power Plant Planning

In this paper, the modified artificial bee colony optimization algorithm, in which the accept–reject method is used as a part of the solution process in order to limit the random search space, is proposed for solving the optimization problems. The accept–reject method can find points of desirable quality and high accuracy with the help of probabilistic functions and limited the random search space, which increases the speed of the algorithm for the next steps and also reduces the number of iterations of the artificial bee algorithm. Then, the next phases of the bee algorithm will be executed in the interval determined by the accept–reject method. One of the most important issues in intelligent algorithms, especially the bee algorithm, is that in problems with several local optimal points, sometimes the algorithm gets stuck in the trap of local optimal points and does not reach the global optimal points. By using the motion step determination parameter in the employed bee phase, the algorithm can be guided to reach the global optimal points while correctly adjusting the movement of the bees for searching in random food sources, which will help improving the problem-solving process. This algorithm is tested by the basic benchmark functions as well as the benchmark functions of CEC 2005 and CEC2017 and CEC2019 and an engineering problem related to the planning of a virtual power plant. The results show that the proposed algorithm is superior in comparison to other algorithms.
Feature Purity: A Quantitative Evaluation Metric for Feature Extraction in Convolutional Neural Networks

Abstract The quantitative analysis of specific convolutional layer feature information in Convolutional Neural Networks (CNN) has received considerable critical attention in the field of deep learning. In this paper, we proposed an innovative measuring method based on the principle of information entropy to quantitatively analyze the performance of feature extraction in CNN. In the method, the feature purity was defined as the normalized entropy of activation histogram, which was calculated from all kernels in feature layer of CNN. Feature purities were evaluated respectively in different layers of CNN as the inspection for internal structure in specific classification models. At the same time, general degrees of purities among different models were compared with classification performances. The experiments were performed in models of AlexNet, VGG, ResNet, and SENet with different epochs trained by cifar10 and ImageNet1000 datasets. Additionally, a visualization evaluation was performed by grad-CAM method in the manners of the intra-model and inter-model purities. Experimental results showed a strong relationship between feature purity and the classification accuracy in each model. Additionally, the visualized feature map also demonstrated that salient region extracted in specified feature layer had good consistency with purity value. As a result, the proposed feature purity can serve as a quantitative metric representing the degree of feature extraction in CNN without the dependency of label or specific network structure; and has significant interpretability and versatility across models.
Introducing VHMason

A major impediment to the success of virtual agents is the inability of non-technical experts to easily author content. To address this barrier we present VHMason, a multimodal authoring tool designed to help creative authors build embodied conversational agents. We introduce the novel aspects of this authoring tool and explore a use case of the creation of an agent-led educational experience implemented at Children's Hospital Los Angeles (CHLA).
Towards a Framework for Interpretation of CNN Results with ANFIS

The performance of Deep Convolutional Neural Network (CNN) in the field of computer vision and image processing has shown tremendously amazing results. Nevertheless, the internal hidden and deep network architecture of CNN offers a Black Box computational model. Thus, it is infeasible to interpret and explain the computed results directly from such a model, whilst semantic interpretation and explanation are often required for applications such as fault monitoring in a physical plant and medical diagnosis in a human body. To address this challenging problem, various approaches have been proposed in the emerging area of eXplainable AI. Adaptive Network-based Fuzzy Inference System (ANFIS) provides a possible modelling technique that returns transparent and interpretable reasoning results, but it tends to work with data of a dimensionality considerably lower than that workable with CNN. Yet, an integration of these two different approaches may present a meaningful solution to the problem. This paper introduces such a novel framework which enables an interpretable explanation of the CNN outcomes with an ANFIS. This is facilitated by the use of Fuzzy Rough Feature Selection that converts high dimensional data into low dimensional data while minimising information loss. Initial experimental results illustrate the working of this framework.
Design and Implementation of Online Browsing and Automatic Explanation System of Tea Science and Technology History

This paper mainly analyzes the development process of self-service E-Tour guide system, and puts forward the overall design idea, technical route and data design idea of the open self-service e-guide platform, to provide accurate and timely explanation and guidance service for tourists through online platform. The hardware design of the system includes RFID module and multimedia player module. The embedded operating system is used as the development platform. The software includes the transplantation of WinCE operating system, the development of bootloader, the development of related drivers and the design of application program. Finally, illustrated by the online tour guide of tea science and technology history website, the data application system of scenic spot is downloaded to the platform for testing. The test results show that the system has good practicability and the establishment of open self-service electronic tour guide platform is feasible.
Calibration Method of the Monocular Omnidirectional Stereo Camera

Compact and low-cost devices are needed for autonomous driving to image and measure distances to objects 360-degree around. We have been developing an omnidirectional stereo camera exploiting two hyperbolic mirrors and a single set of a lens and sensor, which makes this camera compact and cost efficient. We establish a new calibration method for this camera considering higher-order radial distortion, detailed tangential distortion, an image sensor tilt, and a lens-mirror offset. Our method reduces the calibration error by 6.0 and 4.3 times for the upper- and lower-view images, respectively. The random error of the distance measurement is 4.9% and the systematic error is 5.7% up to objects 14 meters apart, which is improved almost nine times compared to the conventional method. The remaining distance errors is due to a degraded optical resolution of the prototype, which we plan to make further improvements as future work.
Modeling Research of Solar Illumination for Automotive Head-Up Display Testing Based on Chinese Population’s Driving Postures

The displayed image of Head-up display of cars would make a significant effect on driving safety, and a precise solar luminance simulation is the key for Head-up Display image testing. It is the local luminous environment and driving postures that influence the simulation accuracy. This paper proposed a method to calculate solar luminance and illuminance in China, which was programmed with PYTHON, and validated it by a comparison with the actual solar data collected using luminance meter, which shows that this method is qualified to simulate the solar luminance and illuminance with a certain extent. Finally, the maximum solar luminance and illuminance in China in different scenarios were obtained using the method, which could be the input to automotive Head-up Display test.
Multi-access Edge Computing for Adaptive Bitrate Video Streaming

Video streaming is the most used service in mobile networks and its usage will continue growing in the upcoming years. Due to this increase, content delivery should be improved as a key aspect of video streaming service, supporting higher bandwidth demand while assuring high quality of experience (QoE) for all the users. Multi-access edge computing (MEC) is an emerging paradigm that brings computational power and storage closer to the user. It is seen in the industry as a key technology for 5G mobile networks, with the goals of reducing latency, ensuring highly efficient network operation, improving service delivery and offering an improved user experience, among others. In this doctoral study, we aim to leverage the possibilities of MEC to improve the content delivery of video streaming services. We present four main research questions to target the different challenges in content delivery for HTTP Adaptive Streaming.
Preparing Philippine higher education institutions for flexible learning during the period of COVID-19 pandemic: Curricular and instructional adjustments, challenges, and issues: Preparing Philippine higher education institutions for flexible learning

The current situation, which is considered the new normal, calls for an immediate response and action from all higher education institutions (HEIs). This is a qualitative study that shares the experiences and preparations of universities and colleges in the Philippines in shifting to flexible learning as an alternative way to deliver curriculum and instruction at the time of the COVID-19 pandemic. Through analysis of public documents like memoranda, advisories, reports, and updates from leading organizations and HEIs, this study identifies curricular and instructional adjustments done and discusses several challenges and issues experienced by higher education institutions in shifting to flexible learning. The study also recommends a model that universities and colleges could use in planning for an effective and efficient implementation of flexible learning.
Hint Pyramid Learning Via Salient Semantic Mining

Feature pyramid network (FPN) has dominated the task of object detection for many years with its congenital capability of semantic information interaction between different feature levels. Satisfactory performance can be easily achieved via the heavy backbones or tricky designs of anchors. However, the heavy structure brings about a huge increase of parameters and calculation. To address this challenge, this paper proposes a novel Hint Pyramid Learning(HPL) strategy to optimize a lightweight FPN-based detector such as one based on ResNet18. In HPL, the hint feature representation is learnt via salient semantic mining based on the teacher-student learning mechanism and self learning mechanism. Besides minimizing of the discrepancy of features belonging to the same feature pyramid layer, HPL also focuses on the relationships between feature pyramid layers. Unlike the popular mimicking algorithms such as hint learning and knowledge distillation which achieve comparable performance on classification tasks but failed to generalize to the detection tasks with FPN, HPL can be easily plugged into different FPN-based pipelines such as one-stage RetinaNet or two-stage Faster R-CNN and improves the performance straightforwardly. Without any meticulous designing, when the backbone of students is ResNet18 and the backbone of teachers is ResNet50, HPL effectively improves the performance by 2.1 AP for RetinaNet and 1.0 AP for Faster R-CNN on COCO benchmark.
Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists

Checklists are used to aid the fulfillment of safety critical activities in a variety of different applications, such as aviation, health care or labour inspections. However, optimizing a checklist for a specific purpose can be challenging. Checklists also need to be trustworthy and user friendly to promote user compliance. With labour inspections as a starting point, we introduce the Checklist Construction Problem. To address the problem, we seek to optimize the content of labour inspection checklists in order to improve the working conditions in every organisation targeted for inspections. To do so, we introduce a hybrid framework called BCBR to construct trustworthy checklists. BCBR is based on case-based reasoning (CBR) and Bayesian inference (BI) and constructs new checklists based on past cases. A key novelty of BCBR is the use of BI for constructing new features in past cases. The augmented past cases are retrieved via CBR to construct new checklists, which ensures justification for the content of the checklists and promotes trust. Experiments suggest that BCBR is more effective than any other baseline we tested, in terms of constructing trustworthy checklists.
Symmetric Triangle Network for Object Detection Within X-ray Baggage Security Imagery

X-ray baggage security image inspection is crucial for maintaining safety and is difficult due to the 2D projection nature of the images and variable sizes of objects. We proposed an anchor-free symmetric triangle network (ST-Net) for object detection within X-ray baggage security imagery. The ST-Net has three main components: bottom-up structure, Symmetric triangle feature pyramid module (STFPM) and detector head. Specifically, the STFPM module has multiple paths with different directions and lengths, which can fuse the rich multi-scale feature representation. Symmetry structure can effectively supplement the global feature information. In addition, we built a new dual-energy (DE) dataset generated from the real dual-energy images to preserve the original information. Experiments on DE dataset and public SIXray dataset demonstrate the proposed ST-Net could surpass most existing methods.
Machine Learning Approaches for Image Quality Improvement

The most widely used tool for learning and interpreting medical diagnoses is magnetic resonance imaging. To analyze and learn the features, the image quality must be improved by preprocessing step. The most challenging task is to perform denoising without altering the contents of image. Performing denoising can dramatically speed up the diagnostic process by addressing the various ranges of noise in magnetic resonance images to enhance the quality of images. Many widespread studies have been carried out for noise control but lags into complication. To overcome these things, the paper provides various types of noise reduction approaches in-detail. The study also includes brief elaboration of magnetic resonance imaging. Machine learning that is foremost used field for complex problems is also discussed with pros and cons of existing techniques with performance parameters to measure the noise in MR images.
Model-Agnostic Explanations using Minimal Forcing Subsets

How can we find a subset of training samples that are most responsible for a specific prediction made by a complex black-box machine learning model? More generally, how can we explain the model's decisions to end-users in a transparent way? We propose a new model-agnostic algorithm to identify a minimal set of training samples that are indispensable for a given model's decision at a particular test point, i.e., the model's decision would have changed upon the removal of this subset from the training dataset. Our algorithm identifies such a set of “indispensable” samples iteratively by solving a constrained optimization problem. Further, we speed up the algorithm through efficient approximations and provide theoretical justification for its performance. To demonstrate the applicability and effectiveness of our approach, we apply it to a variety of tasks including data poisoning detection, training set debugging and understanding loan decisions. The results show that our algorithm is an effective and easy-to-comprehend tool that helps to better understand local model behavior, and therefore facilitates the adoption of machine learning in domains where such understanding is a requisite.
End-to-End Fine-Grained Neural Entity Recognition of Patients, Interventions, Outcomes

PICO recognition is an information extraction task for detecting parts of text describing Participant (P), Intervention (I), Comparator (C), and Outcome (O) (PICO elements) in clinical trial literature. Each PICO description is further decomposed into finer semantic units. For example, in the sentence ‘The study involved 242 adult men with back pain.’, the phrase ‘242 adult men with back pain’ describes the participant, but this coarse-grained description is further divided into finer semantic units. The term ‘242’ shows “sample size” of the participants, ‘adult’ shows “age”, ‘men’ shows “sex”, and ‘back pain’ show the participant “condition”. Recognizing these fine-grained PICO entities in health literature is a challenging named-entity recognition (NER) task but it can help to fully automate systematic reviews (SR). Previous approaches concentrated on coarse-grained PICO recognition but focus on the fine-grained recognition still lacks. We revisit the previously unfruitful neural approaches to improve recognition performance for the fine-grained entities. In this paper, we test the feasibility and quality of multitask learning (MTL) to improve fine-grained PICO recognition using a related auxiliary task and compare it with single-task learning (STL). As a consequence, our end-to-end neural approach improves the state-of-the-art (SOTA) F1 score from 0.45 to 0.54 for the “participant” entity and from 0.48 to 0.57 for the “outcome” entity without any handcrafted features. We inspect the models to identify where they fail and how some of these failures are linked to the current benchmark data.