Reviewerly/exHarmony huggingface dataset:
Visualizing and Characterizing the Parameter Configuration Landscape of Differential Evolution using Physical Landform Classification

It is well known that appropriately configuring the control parameters for computational intelligence algorithms is a challenging problem. Thus, analysis of the configuration space can provide critical insights towards designing more effective parameter tuning strategies. Recently, the concept of parameter configuration landscapes was proposed by drawing parallels between the parameter configuration space and traditional fitness landscapes, thereby facilitating the use of fitness landscape analysis in the parameter configuration domain. This paper extends the idea of the parameter configuration landscape and proposes the use of geomorphons, a physical landform classification scheme, to visualize and characterize the parameter configuration landscape. Additionally, a measure of ruggedness is proposed to quantity the difficulty associated with tuning the control parameters for an algorithm. The proposed methodology is applied to the parameter configuration landscape of the differential evolution (DE) algorithm on 20 benchmark problems in 10, 30, and 50 dimensions.
Suitability of VVC and HEVC for Video Telehealth Systems

Video compression in medical video streaming is one of the key technologies associated with mobile healthcare.Seamless delivery of medical video streams over a resource constrained network emphasizes the need of a video codec that requires minimum bitrates and maintains high perceptual quality.This paper presents a comparative study between High Ef ciency Video Coding (HEVC) and its potential successor Versatile Video Coding (VVC) in the context of healthcare.A large-scale subjective experiment comprising of twenty-four non-expert participants is presented for eight different test conditions in Full High De nition (FHD) videos.The presented analysis highlights the impact of compression artefacts on the perceptual quality of HEVC and VVC processed videos.Our results and ndings show that VVC clearly outperforms HEVC in terms of achieving higher compression, while maintaining high quality in FHD videos.VVC requires upto 40% less bitrate for encoding an FHD video at excellent perceptual quality.We have provided rate-quality curves for both encoders and a degree of overlap across both codecs in terms of perceptual quality.Overall, there is a 71% degree of overlap in terms of quality between VVC and HEVC compressed videos for eight different test conditions.
Estimation of Anxiety Level Using Physiological Measures for Virtual Reality Exposure Therapy System

Exposure therapy has problems of recurrence and patient resistance. Treatment generally begins with weak anxiety which may cause recurrence of the symptom. On the other hand, starting treatment from strong anxiety increases patient resistance. Therefore, we proposed Virtual Reality (VR) exposure therapy system that control VR contents and maintain the patient’s anxiety at an appropriate level. In the previous study, we showed the possibility to estimate patients’ anxiety using physiological indices. Nine healthy males participated in the experiments who were interviewed in a VR space with head mounted display (HMD). The subjective anxiety levels were rated in 11-point scoring system and physiological measures such as electrocardiogram, respiration, skin conductance (SC) were measured. Principal Component Analysis was conducted for various combinations of physiological indices. The combination of averaged heart rate (HR), low and high frequency variability of heart rate (HRVLF, HRVHF), respiratory gravity frequency (RESPGF), level and responses of SC (SCL, SCR) was chosen because of the highest explanation rate. First principal component (PC1) consists of increase in HR, HRVLF, SCL and SCR, which is interpreted as sympathetic activation and was suggested to be utilized for the estimation of anxiety level. In this study, we propose an anxiety level estimation method using the coefficients of PC1. Some degree of correlations between the estimated anxiety level and the subjective ratings were obtained for the participants who showed large changes in subjective ratings. The evaluation of this method using a large size of participants and the examination of the usefulness of the anxiety level optimization system are future issues.
Mongolian Questions Classification in the Law Domain

Question classification is an important part of the question answering system. Through question classification, we can understand the purpose of users' questions and determine the conditions that the answers need to meet. This paper introduces our work on Mongolian questions classification in the law domain. Mongolian is a kind of low-resource language, and there is a lack of public legal-oriented corpus. The complicated morphological structure in Mongolian vocabulary causes the data-sparse problem. In this paper, we constructed a legaloriented corpus for the Mongolian questions classification. And we proposed a method of Mongolian questions classification based the combination of the BERT and BiLSTM model. The BERT model is proposed to extract the semantic features representations of the text. And then the words semantic features acquired are input into the BiLSTM model for questions classification. Our results on the experiments confirm that the method proposed in this paper works effectively with the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> value of 86.98%.
On the Consistency of Maximum Likelihood Estimators for Causal Network Identification

We consider the problem of identifying parameters from data for systems with dynamics evolving according to a particular class of Markov chain processes, called Bernoulli Autoregressive (BAR) processes. The structure of any BAR model is encoded by a directed graph with p nodes. The edges of the graph indicate causal influences, or equivalently dynamic dependencies. More explicitly, the incoming edges to a node in the graph indicate that the state of the node at a particular time instant, which corresponds to a Bernoulli random variable, is influenced by the states of the corresponding parental nodes in the previous time instant. The associated edge weights determine the corresponding level of influence from each parental node. In the simplest setup, the Bernoulli parameter of a particular node's state variable is a convex combination of the parental node states in the previous time instant and an additional Bernoulli noise variable; this convex combination corresponds to the associated parental edge weights and the contribution of the local noise variable. In this paper, we focus on the problem of structure and edge weight identification by relying on well-established statistical principles. We present two consistent estimators of the edge weights, a Maximum Likelihood (ML) estimator and a closed-form estimator, and numerically demonstrate that the derived estimators outperform existing algorithms in the literature in terms of sample complexity.
Active Learning With Multiple Kernels

Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature (RF) approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently alleviated. These advantages enable RF-based OMKL to be considered in practice. In this article, we introduce a new research problem, named stream-based active MKL (AMKL), in which a learner is allowed to label some selected data from an oracle according to a selection criterion. This is necessary for many real-world applications as acquiring a true label is costly or time consuming. We theoretically prove that the proposed AMKL achieves an optimal sublinear regret O(√T) as in OMKL with little labeled data, implying that the proposed selection criterion indeed avoids unnecessary label requests. Furthermore, we present AMKL with an adaptive kernel selection (named AMKL-AKS) in which irrelevant kernels can be excluded from a kernel dictionary "on the fly." This approach improves the efficiency of active learning and the accuracy of function learning. Via numerical tests with real data sets, we verify the superiority of AMKL-AKS, yielding a similar accuracy performance with OMKL counterpart using a fewer number of labeled data.
A Turkish Topic Modeling Dataset For Multi-label Classiﬁcation of Movie Genre

Statistical topic modeling aims to assign topics to documents in an unsupervised way. Latent Dirichlet Allocation (LDA) is the standard model for topic modeling. It shows good performance on document collections, documents being relatively long texts but it has poor performance on short texts. Topic modeling on short texts is on the rise due to the potential of social media. Thus, approaches that are able to find topics on short texts as well as long texts are sought. However, there is a lack of datasets that include both long and short texts which have the same ground-truth categories. In this work, we release a Turkish movie dataset which contain both short film descriptions and long subscripts where film genre can be considered as topic. Furthermore, we provide multi-label movie genre classification results using a Feed Forward Neural Network (FFNN) taking LDA document-topic or Doc2Vec dense representations.
Statistical Comparative Analysis and Evaluation of Validation Indices for Clustering Optimization

Clustering is a relevant exploratory tool for a broad range of machine learning applications as it aids identification of meaningful subgroups. For a given clustering algorithm, multiple partitions can be obtained on the same data set by varying algorithmic parameters. Internal validation indices provide a means to objectively evaluate how well groupings obtained from a clustering configuration partitions the data, since there is no prior labeled data. This work presents a rigorous statistical evaluation framework that analyzes performance of internal validation indices based on correlation with external indices. A synthetic data generator that captures a wide range of complexity is proposed. Evaluation is conducted on a varied set of synthetic data types and real data sets to investigate performance of the indices.
A Training Scheme of Deep Neural Networks on Encrypted Data

Machine learning based on deep neural network has shown great potential in many application fields. Compared with traditional machine learning algorithms, training a reliable neural network model requires a huge amount of data. However, the data required to train a deep neural network model is often privacy sensitive. In order to preserve privacy data, users need to encrypt the data before uploading them to the cloud server. Since back-propagation algorithm works on plaintext, it is difficult to train a neural network model on ciphertext. To address this issue, we propose a new scheme to support training neural network over encrypted data. We use homomorphic cryptosystem to protect privacy feature data of users. We also improve the back-propagation algorithm in privacy-preserving environment to protect classification label of user. We implement the model based on LeNet-5 and present performance evaluation. The experimental results show that our scheme has similar accuracy to training the MNIST dataset in plaintext.
Shape Control of Elastic Objects Based on Implicit Sensorimotor Models and Data-Driven Geometric Features

This paper proposes a general approach to design automatic controls to manipulate elastic objects into desired shapes. The object’s geometric model is defined as the shape feature based on the specific task to globally describe the deformation. Raw visual feedback data is processed using classic regression methods to identify parameters of data-driven geometric models in real-time. Our proposed method is able to analytically compute a pose-shape Jacobian matrix based on implicit functions. This model is then used to derive a shape servoing controller. To validate the proposed method, we report a detailed experimental study with robotic manipulators deforming an elastic rod.
Panel: Bodily Expressed Emotion Understanding Research: A Multidisciplinary Perspective

Developing computational methods for bodily expressed emotion understanding can benefit from knowledge and approaches of multiple fields, including computer vision, robotics, psychology/psychiatry, graphics, data mining, machine learning, and movement analysis. The panel, consisting of active researchers in some closely-related fields, attempts to open a discussion on the future of this new and exciting research area. This paper documents the opinions expressed by the individual panelists.
Autonomous MAV Landing on a Moving Platform with Estimation of Unknown Turbulent Wind Conditions

This paper presents an autonomous landing of a micro aerial vehicle (MAV) on a moving platform immersed in turbulent wind conditions. We estimate the 3D wind vector acting on the vehicle using a model-based and a deep learning-based approach. A disturbance-aware boundary layer sliding controller then uses this estimation to generate a control input that provides trajectory tracking guarantees in the presence of unknown, but bounded disturbances. The approach presented integrates our previous works on control and estimation, and we show its performance in a challenging setting. The experiments show that our methods enable fast landing on a moving platform in turbulent, unknown wind conditions.
Improve CAM with Auto-adapted Segmentation and Co-supervised Augmentation

Weakly Supervised Object Localization (WSOL) methods generate both classification and localization results by learning from only image category labels. Previous methods usually utilize class activation map (CAM) to obtain target object regions. However, most of them only focus on improving foreground object parts in CAM, but ignore the important effect of its background contents. In this paper, we propose a confidence segmentation (ConfSeg) module that builds confidence score for each pixel in CAM without introducing additional hyper-parameters. The generated sample-specific confidence mask is able to indicate the extent of determination for each pixel in CAM, and further supervises additional CAM extended from internal feature maps. Besides, we introduce Co-supervised Augmentation (CoAug) module to capture feature-level representation for foreground and background parts in CAM separately. Then a metric loss is applied at batch sample level to augment distinguish ability of our model, which helps a lot to localize more related object parts. Our final model, CSoA, combines the two modules and achieves superior performance, e.g. 37.69% and 48.81% Top-1 localization error on CUB-200 and ILSVRC datasets, respectively, which outperforms all previous methods and becomes the new state-of-the-art.
Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment.

Computational models of argument quality (AQ) have focused primarily on assessing the overall quality or just one specific characteristic of an argument, such as its convincingness or its clarity. However, previous work has claimed that assessment based on theoretical dimensions of argumentation could benefit writers, but developing such models has been limited by the lack of annotated data. In this work, we describe GAQCorpus, the first large, domain-diverse annotated corpus of theory-based AQ. We discuss how we designed the annotation task to reliably collect a large number of judgments with crowdsourcing, formulating theory-based guidelines that helped make subjective judgments of AQ more objective. We demonstrate how to identify arguments and adapt the annotation task for three diverse domains. Our work will inform research on theory-based argumentation annotation and enable the creation of more diverse corpora to support computational AQ assessment.
Non-Functional Requirements Classification for Aligning Business with Information Systems

Non-Functional Requirements (NFR) are defined as the desired quality requirements, such as availability, that restrict software product being developed where some external restrictions may apply. Since information systems have been introduced, organizations in the business world align their functional activities with systems without paying attention to quality-based alignment. Few research works have been conducted in order to classify and integrate the NFR with business or system models. But these classifications and integrations are only confined to either the business side or the system side, which in turn have caused in having a gap in mapping the classifications between the two sides. Because business models and system models mutually affect each other in many ways, their NFR integration and classification should be aligned with each other. Having a NFR alignment-based classification between business and information systems contributes to assist the stakeholders in reflecting the quality requirements at the business side for a particular task on the related tasks integrated with NFRs at the systems side. Also having an alignment-oriented classification contributes to trace quality/NFR-based changes from the business organization to its systems and vice versa.
Bot Detection in Social Networks Using Stacked Generalization Ensemble

In recent times, the reach and influence of social media have grown tremendously across the entire globe. The ease of access, simplicity, publicity and reach offered by giant social networking sites have come to hold immense value nowadays. However, this has led to the widespread use of fake accounts or programmed bots in order to inflate one’s social media popularity and further spread favourable content. Many recent studies have highlighted the impact of such bots in fields like advertising, commercial promotion and even elections. In this paper, we propose a method to detect bots on social networking sites and distinguish them from genuine user accounts by using a stacked learning approach whereby a convolutional neural network model is trained to feed forward to a machine learning model. This is achieved by using a supervised learning approach to build a layered classifier that makes predictions based on a user’s profile information, tweets and activity information from a dataset of Twitter users. Our paper also analyses the comparative performance of many machine learning models applied to this problem.
Constacyclic codes of length $ 8p^s $ over $ \mathbb F_{p^m} + u\mathbb F_{p^m} $

&lt;p style='text-indent:20px;'&gt;For any odd prime &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ p $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, the structures and duals of &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ \lambda $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-constacyclic codes of length &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ 8p^s $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; over &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ \mathcal R = \mathbb F_{p^m}+u\mathbb F_{p^m} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; are completely determined for all unit &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ \lambda $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; of the form &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ \lambda = \xi^l\in \mathbb F_{p^m} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, where &lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ l $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is even. In addition, the algebraic structures of all cyclic and negacyclic codes of length &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ 8p^s $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; over &lt;inline-formula&gt;&lt;tex-math id="M11"&gt;\begin{document}$ \mathcal R $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; are established in term of their generator polynomials. Dual codes of all cyclic and negacyclic codes of length &lt;inline-formula&gt;&lt;tex-math id="M12"&gt;\begin{document}$ 8p^s $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; over &lt;inline-formula&gt;&lt;tex-math id="M13"&gt;\begin{document}$ \mathcal R $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; are also investigated. Furthermore, we give the number of codewords in each of those cyclic and negacyclic codes. We also obtain the number of cyclic and negacyclic codes of length &lt;inline-formula&gt;&lt;tex-math id="M14"&gt;\begin{document}$ 8p^s $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; over &lt;inline-formula&gt;&lt;tex-math id="M15"&gt;\begin{document}$ \mathcal R $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
Trend-guided Small Hydropower System Power Prediction Based on Extreme Learning Machine

A trend-guided extreme learning machine prediction model (TG-ELM) is proposed, which enriches the physical mechanism of the input and output of the traditional extreme learning machine (ELM), and solves the problems that a large amount of raw data in the traditional hydropower prediction model depends on a single data normalization process, which causes the prediction accuracy of the entire prediction model to decrease. The model first smoothed and repaired abnormal output data of the small hydropower group, and then extracted the trend of power change from the processed power curve, and used this trend as a new feature input for the extreme learning machine model to provide a correct and unique prediction trend for the prediction of the extreme learning machine. The model is applied to the power generation forecast of small hydropower groups in Guangxi, which greatly improves the accuracy of the power generation forecast and shortens the calculation time of the forecast. Based on the historical data of the power generation of the small hydropower group in Guangxi in August 2019 for simulation analysis, the root mean square error and average absolute error of the prediction are 4.16 WM and 6.11%, respectively. Compared with prediction methods such as backpropagation neural network and support vector machine, the proposed prediction model has distinct advantages and is more suitable for industrial applications.
Deep Learning-Based Paperless Attendance Monitoring System

This paper concerns about the paperless attendance monitoring system. Major focus of this system is on the concept of face recognition. The goal is to mark the attendance without disturbing the class by using face recognition technique. Marked attendance using this monitoring system can be directly sent to the mentor/coordinator through email. Face recognition uses biometric features, and it extracts person’s facial features and stores data. Faces will be recognized based on deep learning algorithm to train the system and to compare or test to identify the person. Time is an important factor in recognition. This factor has been brought up to get the attendance in hand of the respected person instantly. To implement this efficiently, we have gone for deep learning model based on CNN. One important aspect is to deal with the results of the live detection and the immediate prediction of the person. The proposed system can identify an individual from various angles and positions. One of the important parameters that decides the accuracy is the quality of image, better the quality better the accuracy. Quality of the captured image defines the accuracy of the system. After implementing CNN on created database, we achieved an accuracy of 80.5%. Accuracy value can change depending on the testing database.