Reviewerly/exHarmony huggingface dataset:
A Unified Multi-view Clustering Algorithm Using Multi-objective Optimization Coupled with Generative Model

There is a large body of works on multi-view clustering that exploit multiple representations (or views) of the same input data for better convergence. These multiple views can come from multiple modalities (image, audio, text) or different feature subsets. Obtaining one consensus partitioning after considering different views is usually a non-trivial task. Recently, multi-objective based multi-view clustering methods have suppressed the performance of single objective based multi-view clustering techniques. One key problem is that it is difficult to select a single solution from a set of alternative partitionings generated by multi-objective techniques on the final Pareto optimal front. In this article, we propose a novel multi-objective based multi-view clustering framework that overcomes the problem of selecting a single solution in multi-objective based techniques. In particular, our proposed framework has three major components as follows: (i) multi-view based multi-objective algorithm, Multiview-AMOSA, for initial clustering of data points; (ii) a generative model for generating a combined solution having probabilistic labels; and (iii) K -means algorithm for obtaining the final labels. As the first component, we have adopted a recently developed multi-view based multi-objective clustering algorithm to generate different possible consensus partitionings of a given dataset taking into account different views. A generative model is coupled with the first component to generate a single consensus partitioning after considering multiple solutions. It exploits the latent subsets of the non-dominated solutions obtained from the multi-objective clustering algorithm and combines them to produce a single probabilistic labeled solution. Finally, a simple clustering algorithm, namely K -means, is applied on the generated probabilistic labels to obtain the final cluster labels. Experimental validation of our proposed framework is carried out over several benchmark datasets belonging to three different domains; UCI datasets, multi-view datasets, search result clustering datasets, and patient stratification datasets. Experimental results show that our proposed framework achieves an improvement of around 2%--4% over different evaluation metrics in all the four domains in comparison to state-of-the art methods.
Improved PSO Algorithm Based on Exponential Center Symmetric Inertia Weight Function and Its Application in Infrared Image Enhancement

In this paper, an improved PSO (Particle Swarm Optimization) algorithm is proposed and applied to the infrared image enhancement. The contrast of infrared image is enhanced while the image details are preserved. A new exponential center symmetry inertia weight function is constructed and the local optimal solution jumping mechanism is introduced to make the algorithm consider both global search and local search. A new image enhancement method is proposed based on the advantages of bi-histogram equalization algorithm and dual-domain image decomposition algorithm. The fitness function is constructed by using five kinds of image quality evaluation factors, and the parameters are optimized by the proposed PSO algorithm, so that the parameters are determined to enhance the image. Experiments showed that the proposed PSO algorithm has good performance, and the proposed image enhancement method can not only improve the contrast of the image, but also preserve the details of the image, which has a good visual effect.
GustaCine

In this pictorial, we describe the design and making of GustaCine, an engaging crossmodal cinematic experience that allows the viewer to experience and savor cinematic moments through the gustatory pleasures of the different flavored popcorn. Our aim behind creating GustaCine is to explore possibilities in designing food experiences that 'move' in parallel with the moving picture. We articulate our making process enumerating the challenges in creating a gustatory experience and undertaken strategies to resolve them. Ultimately, we aim to inspire and guide future research on gustatory cinematic experiences.
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection Using Enhanced Neural Networks

Outlier detection has received special attention in various fields, mainly for those dealing with machine learning and artificial intelligence. As strong outliers, anomalies are divided into point, contextual and collective outliers. The most important challenges in outlier detection include the thin boundary between the remote points and natural area, the tendency of new data and noise to mimic the real data, unlabeled datasets and different definitions for outliers in different applications. Considering the stated challenges, we defined new types of anomalies called Collective Normal Anomaly and Collective Point Anomaly in order to improve a much better detection of the thin boundary between different types of anomalies. Basic domain-independent methods are introduced to detect these defined anomalies in both unsupervised and supervised datasets. The Multi-Layer Perceptron Neural Network is enhanced using the Genetic Algorithm to detect new defined anomalies with a higher precision so as to ensure a test error less than that be calculated for the conventional Multi-Layer Perceptron Neural Network. Experimental results on benchmark datasets indicated reduced error of anomaly detection process in comparison to baselines.
Testing Bayesian Networks

This work initiates a systematic investigation of testing high-dimensional structured distributions by focusing on testing Bayesian networks - the prototypical family of directed graphical models. A Bayesian network is defined by a directed acyclic graph, where we associate a random variable with each node. The value at any particular node is conditionally independent of all the other non-descendant nodes once its parents are fixed. Specifically, we study the properties of identity testing and closeness testing of Bayesian networks. Our main contribution is the first non-trivial efficient testing algorithms for these problems and corresponding information-theoretic lower bounds. For a wide range of parameter settings, our testing algorithms have sample complexity sublinear in the dimension and are sample-optimal, up to constant factors.
Data-Driven Analysis of Radiologists’ Behavior for Diagnosing Thyroid Nodules

Thyroid nodule has been a common and serious threaten to human health. With the identification and diagnosis of thyroid nodules in the general population, large volumes of examination reports in clinical practice have been accumulated. They provide data basics of analyzing radiologists' behavior of diagnosing thyroid nodules. To conduct data-driven analysis of radiologists' behavior, an experimental framework is designed based on belief rule base, which is essentially a white box for knowledge representation and uncertain reasoning. Under the framework, with 2744 examination reports of thyroid nodules in the period from January 2012 to February 2019 that have been collected from a tertiary hospital located in Hefei, Anhui, China, experimental results are obtained from conducting missing validation, self-validation, and mutual validation. Three principles are then concluded from the results and corresponding analysis. The first is that missing features on some criteria are considered as benign ones by default, the second is that there is generally inconsistency between the recorded features on criteria and the overall diagnosis, and the third is that different radiologists have different diagnostic preferences. These three principles reflect three diagnostic behavioral characteristics of radiologists, namely reliability, inconsistency, and independence. Based on the three principles and radiologists' behavioral characteristics, managerial insights in a general case are concluded to make the findings in this study available in other situations.
Sentiment Analysis of Application User Feedback in Bahasa Indonesia Using Multinomial Naive Bayes

User feedback can be used as a tool for application developers to find out and understand users' needs, preferences, and complaints. Developers need to identify problems that arise from the user-given feedbacks, which is very difficult to do, considering the amount of feedback received every day. Reading and classifying every feedback takes a long time, and it is very ineffective. In order to overcome this problem, a sentiment analysis system based on the Multinomial Naïve Bayes classification algorithm was built to determine whether a user-feedback has a positive or negative sentiment. Naïve Bayes algorithm is generally used for classification because it is straightforward and effective. Based on previous research, the Multinomial Naïve Bayes algorithm gives off the best performance compared to other traditional machine learning algorithms. This study aims to implement the Multinomial Naïve Bayes classification algorithm on a web application and calculate the accuracy of class predictions made by the system. Based on the results of several test, the accuracy of class predictions which were evaluated using confusion matrix, shows that the model with training-testing comparison of 70:30, balanced datasets, and oversampling each dataset by 30% produces the best performance, with 71.6% for accuracy, 76.92% for precision, 61.73% for recall and 68.49% for F1 score.
Conversational Recommender System Chatbot Based on Functional Requirement

To make a decision in choosing a product with insufficient information or knowledge, we can use a recommendation. Recommender system is a system that gives recommendation to user. In recommendation system that recommends product, user requirements can be given as technical requirement or functional requirement. User that not familiar with technical specification of a product will be easier to specify the needs via functional requirement. With the nature of interacting with user via conversation, chatbot can be used as a conversational recommender system. In this work we will be developing chatbot as a conversational recommender system based on user functional requirement. The developed system will be evaluated using usability testing method.
Meta Decision Trees for Explainable Recommendation Systems

We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes. The regression functions receive as input the embedding of the user's training set, as well as the embedding of the samples that arrive at the current node. The embedding and the regressors are learned end-to-end with a loss that encourages the decision rules to be sparse. By applying our method, we obtain a collaborative filtering solution that provides a direct explanation to every rating it provides. With regards to accuracy, it is competitive with other algorithms. However, as expected, explainability comes at a cost and the accuracy is typically slightly lower than the state of the art result reported in the literature. Our code is available at \urlhttps://github.com/shulmaneyal/metatrees.
An Efficient Spatial-Temporal Polyp Detection Framework for Colonoscopy Video

Recent computer-aided polyp detection systems showed its effectiveness to decrease the polyp miss rate in colonoscopy operations, which is helpful to reduce colorectal cancer mortality. However, traditional polyp detection approaches suffer from the following drawbacks: low precision and sensitivity caused by the variance of polyp's appearance, and the system may not be able to detect polyps in real time due to the high computation complexity of the detection algorithms. To alleviate those problems, we introduce a real-time detection framework that incorporates spatial and temporal information extracted from colonoscopy videos. Our framework consists of the following three components: 1) we adopt Single Shot MultiBox Detector (SSD) to generate the proposal bounding boxes in each video frame. 2) Simultaneously, we compute optical flow from neighboring frames to extract temporal information and generate another group of polyp proposals with the temporal detection network. 3) At last, the final result is generated by a fusion module that connects the end of both streams. Experimental results on ETIS-LARIB dataset demonstrate that our proposed approach reaches the state-of-the-art performance on polyp localization with real-time performance.
Building Machine Learning Models for Long-Period and Volcano-Tectonic Event Classification

The proper identification of several types of volcanic seismic events can be related to the intrinsic behavior of a volcano, and it could be useful to provide an early alarm in the case of an impending eruption. Among different recorded seismic events, the long-period and volcano-tectonic stand as the most important events to track since their occurrence increase may help to forecast possible eruptions. Thus, the correct classification of both types of seismic events could increase the safety of peoples living around the volcano. In this sense, this paper proposed a comprehensive algorithm for building classification models to classify long-period and volcano-tectonic seismic events based on a suitable combination of five well-known machine learning classifiers that provide the highest performance over the area under receiver operating characteristic curve. The method explores different machine learning model combinations to find the most adequate in the context of long-period and volcano-tectonic seismic event classification. The proposed method was validated on an experimental dataset, containing 587 long-period and 81 volcano-tectonic seismic events recorded from the Cotopaxi Volcano in Ecuador. The random forest classifier with 100 tree based predictors demonstrated to be the best classification model. According to the Wilcoxon Statistical test, the proposed method was effective in providing competitive models for the classification of volcano seismic events.
On the Convergence Speed of AMSGRAD and Beyond

In ICLR's (2018) best paper "On the Convergence of Adam and Beyond", the author points out the shortcomings in Adam's convergence proof, proposes an AMSGRAD algorithm that can guarantee convergence as the number of iterations increases. However, through some comparative experiments, this paper finds that there are two problems in the convergence process of AMSGRAD algorithm. Firstly, the AMSGRAD algorithm is easy to oscillate; Secondly, the AMSGRAD algorithm converges slowly. After analysis, the above two problems can be solved by the following ways. When g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t-1</sub> g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> > 0, this paper adds the momentum term in Momentum algorithm to the AMSGRAD algorithm to accelerate convergence. When g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t-1</sub> g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> ≤ 0, this paper use SGD algorithm instead of AMSGRAD algorithm to update the model weights. In order to eliminate some negative effects of the previous parameter gradient on the current parameter gradient and reduce the oscillation amplitude of the objective function, the first-order and second-order moment estimations of the parameter gradient are recalculated when g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t-1</sub> g <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> ≤ 0. Therefore, this paper proposes the ACADG algorithm, which not only can improve the convergence speed, suppress the oscillation amplitude of the objective function, but also can improve the accuracy of training and test data sets.
Knowledge management system design method with joint application design (JAD) adoption

Purpose This study proposes to adopt joint application design (JAD) in the requirement gathering process on developing a knowledge management system (KMS) using Fernandez methodology. It is proposed that Fernandez methodology produces only a list of KMS features and the requirement of the feature is still unclear. This study aims to produce KMS that meets the needs and expectations of users based on features of Fernandez's methodology. Design/methodology/approach This study was conducted using a combination methodology that combines Fernandez methodology to identify the feature needs of the organization and JAD to identify user requirements of KMS. The system prototype is evaluated by measuring user satisfaction using the Post Study System Usability Questionnaire. Findings The results of this study indicate that with the adoption of JAD it is possible to get the requirement in accordance with the expectations of the user. In addition, this study also shows that the level of user satisfaction with the prototype developed with the requirement is high at 84% of the maximum score. Research limitations/implications This study was used only in the education context, especially teaching assistant problems at the Faculty of Computer Science, University of Indonesia. For this reason, further research is needed for different contexts so that the implementation of this combination of methods can be generalized more broadly. Practical implications This paper provides practical implications for the development of KMS by identifying user requirements using JAD. Originality/value This paper provides a combination method to generate KMS that meets user requirements.
Obstacle Avoidance Algorithm for Multi-UAV Flocking Based on Artificial Potential Field and Dubins Path Planning

In this paper, a novel obstacle avoidance algorithm for the multi-UAV flocking was proposed by combining artificial potential field and Dubins path planning method. For the multi-UAV flocking, a virtual-leader was considered, and its flight path was designed by Dubins path planning method, which can take into account the overload constraint of the UAV. In this way, the flight path of the whole multi-UAV flocking was roughly planned, so as to save obstacle avoidance time, and the flight path of the follower-UAVs was designed according to the artificial potential field method, which has a better performance in real-time obstacle avoidance, and the algorithm is relatively simple. The simulation results show that the obstacle avoidance algorithm designed by combining the two methods has a better obstacle avoidance ability.
Boundary Sensitive and Category Sensitive Network for Temporal Action Proposal Generation

The latest research on temporal motion detection research content is an important research topic in the field of computer vision. The TAP (temporal action proposal) generation task is an essential part for quickly and accurately drawing semantically important action proposals from untrimmed video. As a separate and important research field, the proposal generated by the temporal action proposal generation network should have three attributes: (1) flexible temporal length, (2) accurate temporal boundaries and (3) reliable confidence scores. Therefore, a new type of effective and efficient universal action proposal generation network for temporally uncropped videos is provided by us, named Boundary Sensitive and Category Sensitive (BSCS) network. Firstly, We need a combination of a frame-level appearance features and a clip-level optical flow features, so a two-stream network is adopted. Secondly, a sub-network in our network is designed. This sub-network needs to outputs 203 probability sequences. These probability sequences represent the temporal distribution of the start and end frames and categories, respectively. The ending frame and starting frame belong to the same kind of action are screened through the probability sequence of 200 action categories. Finally, according to certain rules that combining the ending frames and the starting frames, our network generates proposals. We further compare our method with more experiments experimentally with the existing networks in ActivityNet-1.3 [1]. Comparing the network results with experiments proves that our method is more effective.
AFaaS: Authorization framework as a service for Internet of Things based on interoperable OAuth

Internet of Things has become a fundamental paradigm in our everyday lives. However, standards and technologies are often designed without considering interoperability, which is a critical issue for Internet of Things. Internet of Things environment requires interoperability to share resources (e.g. data and services) between heterogeneous Internet of Things domains. The open authorization (OAuth) 2.0 framework that is actively used in Internet of Things (as well as in conventional web environments) also did not focus on interoperability. In other words, the systems that implement the same OAuth 2.0 standard cannot interoperate without additional support. For this reason, we propose an authorization framework as a service. Authorization framework as a service provides an additional authorization layer to support standard authorization capabilities as an interoperable secure wrapper between different domains. Besides, authorization framework as a service supports the four extended authorization grant flow types to issue an interoperable access token, which has a global access scope across multiple heterogeneous domains. With the authorization framework as a service, interoperability can be supported for heterogeneous domains, and token management can also be simple because an interoperable access token can represent several existing access tokens that have local access scopes. Furthermore, this article presents a feasible interoperability scenario, implementation, and security considerations for authorization framework as a service, focusing on Internet of Things platforms.
Research on transfer learning algorithm based on support vector machine

Transfer learning is a new machine learning algorithm. It solves problems in different but related target domains by utilizing the knowledge in existing data. Based on the classical SVM algorithm and transfer learning, a selective transfer learning support vector machine (STL-SVM) algorithm is pro posed in this paper. First, STL-SVM uses the maximum mean discrepancy to measure the weight vector of the source domain samples relative to the target domain, and selects samples from the source domain according to each weight to avoid negative transfer. Then, the knowledge in the source domain is learned by the approximate extreme point support vector at the minimum training data cost. Finally, the object function is constructed by the obtained knowledge and the soft-margin SVM. In the constraint conditions of the objective function, the learned knowledge that is highly correlated with the target domain is selected, and further, the phenomenon of negative transfer is avoided in principle. STL-SVM solves the problem of negative transfer, and has considerable advantages in training time efficiency compared with the existing algorithms. The experimental results on artificial and real datasets show the effectiveness of the proposed algorithm.