Reviewerly/exHarmony huggingface dataset:
Analysis of Euclidean Distance and Manhattan Distance in the K-Means Algorithm for Variations Number of Centroid K

Abstract K-Means is a clustering algorithm based on a partition where the data only entered into one K cluster, the algorithm determines the number group in the beginning and defines the K centroid. The initial determination of the cluster center is very influential on the results of the clustering process in determining the quality of grouping. Better clustering results are often obtained after several attempts. The manhattan distance matrix method has better performance than the euclidean distance method. The author making the result of conducted testing with variations in the number of centroids (K) with a value of 2,3,4,5,6,7,8,9 and the authors having conclusions where the number of centroids 3 and 4 have a better iteration of values than the number of centroids that increasingly high and low based on the iris dataset.
Entangling the Roles of Maker and Interpreter in Interpersonal Data Narratives

To explore how materials, data, and humans collaborate to produce physical data representations, we created a series of artefacts from personal data we collected (about commuting, forgetting, and busy-ness) in different media---yarn and sound. We exchanged these artefacts without providing guidelines for how to interpret them in order to study where the boundary between maker and interpreter emerges. Through creating hand-crafted physicalizations and sonifications, we present three themes on making personal data narratives: matching data to the materials (and vice versa), accepting the materials' will to co-author, and negotiating between the experience of the data and data of the experience. In exchanging the artefacts, we explored the role of the interpreter as a re-maker and how multiple narratives can productively co-exist. We conclude with a discussion about how reimagining the roles of maker and interpreter might lead to new interactions with personal data narratives.
Privacy-Preserving Association Rule Mining Using Homomorphic Encryption in a Multikey Environment

With the development of big data and cloud computing, data analysis technologies play an important role to produce huge market values. Customers with limited computing resources may resort to the cloud to perform some association rule mining tasks. Data owners may have a risk of personal sensitive information leakage in this process. To preserve privacy in outsourced data, data owners may encrypt raw data before uploading. Data analysis of encrypted data is a challenge that has attracted the attention of many researchers in recent years. Homomorphic encryption is a cryptographic tool, which is one of the ways to solve this challenge. It allows data processing of encrypted data without decryption. Researching homomorphic encryption schemes that support privacy-preserving data mining in a multikey environment has become a significant direction. In this article, we propose a novel homomorphic cryptosystem, which supports multiple cloud users to have different public keys. Besides, we propose a privacy-preserving association rule mining scheme on outsourced data uploaded from multiple parties in a twin-cloud architecture. Our scheme uses a transaction record representation method in databases for large shopping malls based on real-world situations, and our experiments on a real transaction database show that our technology is reasonably feasible.
HASVRec: A modularized Hierarchical Attention-based Scholarly Venue Recommender system

Manually selecting appropriate scholarly venues is becoming a tedious and time-consuming task for researchers due to many reasons that include relevance, scientific impact, and research visibility. Sometimes, high-quality papers get rejected due to mismatch between the area of the paper and the scope of the journal. Recommending appropriate academic venues can, therefore, enable researchers to identify and take part in relevant conferences and publish in journals that matter the most. A researcher may certainly know of a few leading venues for her specific field of interest. However, a venue recommendation system becomes particularly helpful when exploring a new domain or when more options are needed. Due to high dimensionality and sparsity of text data, and complex semantics of the natural language, journal identification presents difficult challenges. We propose a novel and unified architecture that contains a Bi-directional LSTM (Bi-LSTM) and a Hierarchical Attention Network (HAN) to address the above problems. We call the proposed architecture modularized Hierarchical Attention-based Scholarly Venue Recommender system (HASVRec), which only requires the abstract, title, keywords, field of study, and author of a new paper along with its past publication record to recommend scholarly venues. Experiments on the DBLP-Citation-Network V11 dataset exhibit that our proposed approach outperforms several state-of-the-art methods in terms of accuracy, F1, nDCG, MRR, average venue quality, and stability.
Stabilization of Interval Type-2 Fuzzy-Based Reliable Sampled-Data Control Systems

This article investigates the stabilization problem of the Takagi-Sugeno (T-S) fuzzy-based interval type-2 (IT-2) reliable sampled-data control system. Different from the existing studies, the information for the entire sampling interval with a relation of the augmented state vectors is considered for designing the reliable sampled-data control of IT-2 T-S fuzzy systems. In this regard, we construct the suitable looped Lyapunov-Krasovskii functional (LKF) with the information of the fuzzy membership function that feeds back the time derivative of the membership function to derive sufficient conditions. These sufficient conditions are established in the form of linear matrix inequalities (LMIs), which ensure the global asymptotic stability of T-S fuzzy systems under the designed control technique. Finally, the applicability and superiority of developed reliable sampled-data control techniques were validated by three practical systems.
Real-Time Continuous Human Rehabilitation Action Recognition using OpenPose and FCN

The action recognition of human rehabilitation movement in the home scene plays a positive role in promoting the rehabilitation process of patients, we present an efficient approach for real-time continuous human rehabilitation action recognition using OpenPose and FCN. The proposed method first fuses OpenPose with Kalman filter to track human targets and generate the 2D poses action sequences from the RGB videos stream. Then we extract the segmented action sequence by sliding the window and convert the rectangular coordinates to relative coordinates from each frame of the human skeleton. We design a 1D fully convolutional network to extract spatial-temporal characteristics and classify actions. The experimental results show that the method has strong adaptability to the interference of activity background, human body wearing and irrelevant personnel, and it can identify continuous rehabilitation actions online with an accuracy rate of 85.6%. At the same time, the model running speed on GTX1060 is up to 18.14 FPS which has certain application value in the home rehabilitation training scene.
Conversational AI: An Overview of Methodologies, Applications &amp; Future Scope

Conversational AI is a sub-domain of Artificial Intelligence that deals with speech-based or text-based AI agents that have the capability to simulate and automate conversations and verbal interactions. Conversational AI Agents like chatbots and voice assistants have proliferated due to two main developments. On one hand the methods required to develop highly accurate AI models i.e. Machine Learning, Deep Learning have seen a tremendous amount of advancement due to the increasing research interest in these fields accompanied by the progress in achieving higher computing power with the help of complex hardware architectures like GPUs and TPUs. Secondly, due to the Natural Language interface and the nature of their design, conversational agents have been seen as a natural fit in a wide array of applications like healthcare, customer care, ecommerce and education. This rise in the practical implementation and their demand has in turn made Conversational AI a ripe area for innovation and novel research. Newer and more complex models for the individual core components of a Conversational AI architecture are being introduced at a never before seen rate. This study is intended to shed light on such latest research in Conversational AI architecture development and also to highlight the improvements that these novel innovations have achieved over their traditional counterparts. This paper also provides a comprehensive account of some of the research opportunities in the Conversational AI domain and thus setting up the stage for future research and innovation in this field.
Sentiment Analysis on Students’ Evaluation of Higher Educational Institutions

Sentiment analysis is the method of identifying and classifying the views of users from text documents into different sentiments, such as positive, negative or neutral. Sentiment analysis can be employed to extract structured and informative knowledge from unstructured text pieces. This knowledge can serve as an important source of information for decision support systems and individual decision makers. Sentiment analysis plays an important role in many fields like education, where student input is crucial for assessing the effectiveness of educational institutions. In this paper, we present a machine learning based approach to sentiment analysis on students’ evaluation of higher educational institutions. We analyze a corpus containing approximately 700 student reviews written in Turkish, with the use of conventional text representation schemes and machine learning classifiers. In the experimental analysis, three conventional text representation schemes (i.e., term-presence, term-frequency, TF-IDF scheme) and three N-gram models (1-gram, 2-gram and 3-gram) have been considered in conjunction with four classifiers (i.e., support vector machines, Naïve Bayes, logistic regression, and random forest algorithm). The predictive performance of four ensemble learners (i.e., AdaBoost, Bagging and Random Subspace and voting algorithm) have been also evaluated. The empirical results indicate that machine learning based approach yields promising results on students’ evaluation of higher educational institutions.
Color image watermark decoder by modeling quaternion polar harmonic transform with BKF distribution

It still remains a challenging problem to improve both of the imperceptibility and robustness for the digital image watermarking task. In this study, a color image watermarking scheme is proposed based on quaternion polar harmonic transform (QPHT) with maximum likelihood decoder, which better balances imperceptibility and robustness in a novel fashion. Thanks to the strong robustness of QPHT, the digital watermark is embedded into the QPHT magnitudes using a multiplicative approach. At the receiver, the watermark decoder can be formulated as a problem of weak signal detection, as the QPHT magnitudes are highly non-Gaussian. In view of this, selection of proper models is of great importance to construct a suitable watermark decoder. Further, we validate that the probability density function of Bessel K form suitably models the heavy tail behavior of QPHT magnitudes, and thus the maximum likelihood decoder is developed by modeling QPHT magnitudes with Bessel K form distribution. Experimental results on standard benchmarks demonstrate that the proposed approach provides better imperceptibility and robustness against various kinds of attacks compared to previous state-of-the-art methods.
Lightweight Modules for Efficient Deep Learning Based Image Restoration

Low level image restoration is an integral component of modern artificial intelligence (AI) driven camera pipelines. Most of these frameworks are based on deep neural networks which present a massive computational overhead on resource constrained platform like a mobile phone. In this paper, we propose several lightweight low-level modules which can be used to create a computationally low cost variant of a given baseline model. Recent works for efficient neural networks design have mainly focused on classification. However, low-level image processing falls under the `image-to-image' translation genre which requires some additional computational modules not present in classification. This paper seeks to bridge this gap by designing generic efficient modules which can replace essential components used in contemporary deep learning based image restoration networks. We also present and analyse our results highlighting the drawbacks of applying depthwise separable convolutional kernel (a popular method for efficient classification network) for sub-pixel convolution based upsampling (a popular upsampling strategy for low-level vision applications). This shows that concepts from domain of classification cannot always be seamlessly integrated into `image-to-image' translation tasks. We extensively validate our findings on three popular tasks of image inpainting, denoising and super-resolution. Our results show that proposed networks consistently output visually similar reconstructions compared to full capacity baselines with significant reduction of parameters, memory footprint and execution speeds on contemporary mobile devices.
Verifiable Timed Signatures Made Practical

A verifiable timed signature (VTS) scheme allows one to time-lock a signature on a known message for a given amount of time T such that after performing a sequential computation for time T anyone can extract the signature from the time-lock. Verifiability ensures that anyone can publicly check if a time-lock contains a valid signature on the message without solving it first, and that the signature can be obtained by solving the same for time T.
When open source design is vital: critical making of DIY healthcare equipment during the COVID-19 pandemic

Shortages of personal protective equipment (PPE) and medical devices needed during the COVID-19 pandemic were widely reported in early 2020. In response, civic DIY volunteers explored how they could produce the required equipment. Members of communities such as hacker- and makerspaces employed their skills and tools to manufacture, for example, face shields and masks. The article discusses these civic innovation practices and their broader social implications by relating them to critical making theory. Methodologically, it is based on a digital ethnography approach, focusing on hacker and maker communities in the UK. Communities' DIY initiatives display characteristics of critical making and 'craftivism', as they assessed and counteracted politicised healthcare supply shortages. It is argued that their manufacturing activities during the COVID pandemic relate to UK austerity politics' effects on healthcare and government failure to ensure medical crisis supplies. Facilitated by open source design, communities' innovation enabled healthcare emergency equipment. At the same time, their DIY manufacturing raises practical as well as ethical issues concerning, among other things, efficacy and safety of use.
Manifold Transfer Learning via Discriminant Regression Analysis

In transfer learning, how to effectively transfer useful information from the source domain to the target domain is crucial. In this paper, we propose a novel transfer learning method for image classification, named manifold transfer learning via discriminant regression analysis (MTL-DRA), to transfer the local geometry structure information from the source domain to the target domain and ensure that the transform matrix is robust or sparse so that samples from different domains can be well combined. In MTL-DRA, we encode discriminant information of the source domain to the target domain by introducing between- and within-class graphs to preserve within-class similarity and reduce between-class similarity. With different norms as constraints, MTL-DRA overcomes the disturbance of noise and avoids negative transfer learning. To improve the robustness of MTL-DRA, we encode a nuclear norm constraint and propose robust MTL-DRA (RMTL-DRA). We analyzed the convergence and complexity of the two proposed methods. To verify the performance of the proposed methods, we conducted extensive experiments on five public image benchmarks. The experimental results show that the proposed methods outperform state-of-the-art transfer learning methods.
Subspace learning for unsupervised feature selection via adaptive structure learning and rank approximation

Traditional unsupervised feature selection methods usually construct a fixed similarity matrix. This matrix is sensitive to noise and becomes unreliable, which affects the performance of feature selection. The researches have shown that both the global reconstruction information and local structure information are important for feature selection. To solve the above problem effectively and make use of the global and local information of data simultaneously, a novel algorithm is proposed in this paper, called subspace learning for unsupervised feature selection via adaptive structure learning and rank approximation (SLASR). Specifically, SLASR learns the manifold structure adaptively, thus the preserved local geometric structure can be more accurate and more robust to noise. As a result, the learning of the similarity matrix and the low-dimensional embedding is completed in one step, which improves the effect of feature selection. Meanwhile, SLASR adopts the matrix factorization subspace learning framework. By minimizing the reconstruction error of subspace learning residual matrix, the global reconstruction information of data is preserved. Then, to guarantee more accurate manifold structure of the similarity matrix, a rank constraint is used to constrain the Laplacian matrix. Additionally, the l2,1/2 regularization term is used to constrain the projection matrix to select the most sparse and robust features. Experimental results on twelve benchmark datasets show that SLASR is superior to the six comparison algorithms from the literature.
A face recognition framework based on a pool of techniques and differential evolution

Face Recognition (FR) systems are still facing significant challenges when different image issues, such as variation of illumination, pose, expression, and occlusion, are present in captured images. In many situations, it is only possible to obtain One Sample Per Person (OSPP) for training, representing a challenging real-world condition. The proposed FR framework is defined by an optimizer and a pool of preprocessing and feature extraction techniques. The approach makes a pool of techniques available to the optimizer, that can seek the best set of strategies, and tune its parameters. In this work, the FR framework uses the well-known Differential Evolution algorithm as an optimizer, denominated as FR-DE. Two experimental methodologies are employed to assess the performance of the proposed FR-DE framework. First, it is employed a standard dataset separation, and the Yale Extended B dataset is used, which presents severe illumination variation conditions. The second experimental methodology considers the OSPP problem with illumination and poses variations. Also, two well-known datasets are employed, named CMU-PIE and FERET. The proposed approach is compared with some state-of-art algorithms. The comparative analysis suggests that the proposed framework is competitive and suitable for FR systems.
SpaceNet: Make Free Space for Continual Learning

The continual learning (CL) paradigm aims to enable neural networks to learn tasks continually in a sequential fashion. The fundamental challenge in this learning paradigm is catastrophic forgetting previously learned tasks when the model is optimized for a new task, especially when their data is not accessible. Current architectural-based methods aim at alleviating the catastrophic forgetting problem but at the expense of expanding the capacity of the model. Regularization-based methods maintain a fixed model capacity; however, previous studies showed the huge performance degradation of these methods when the task identity is not available during inference (e.g. class incremental learning scenario). In this work, we propose a novel architectural-based method referred as SpaceNet for class incremental learning scenario where we utilize the available fixed capacity of the model intelligently. SpaceNet trains sparse deep neural networks from scratch in an adaptive way that compresses the sparse connections of each task in a compact number of neurons. The adaptive training of the sparse connections results in sparse representations that reduce the interference between the tasks. Experimental results show the robustness of our proposed method against catastrophic forgetting old tasks and the efficiency of SpaceNet in utilizing the available capacity of the model, leaving space for more tasks to be learned. In particular, when SpaceNet is tested on the well-known benchmarks for CL: split MNIST, split Fashion-MNIST, and CIFAR-10/100, it outperforms regularization-based methods by a big performance gap. Moreover, it achieves better performance than architectural-based methods without model expansion and achieved comparable results with rehearsal-based methods, while offering a huge memory reduction.
Towards a Knowledge-aware Food Recommender System Exploiting Holistic User Models

Food recommender systems typically rely on popularity, as well as similarity between recipes to generate personalized suggestions. However, this leaves little room for users to explore new preferences, such as to adopt healthier eating habits.
HTSS: A novel hybrid text summarisation and simplification architecture

Text simplification and text summarisation are related, but different sub-tasks in Natural Language Generation. Whereas summarisation attempts to reduce the length of a document, whilst keeping the original meaning, simplification attempts to reduce the complexity of a document. In this work, we combine both tasks of summarisation and simplification using a novel hybrid architecture of abstractive and extractive summarisation called HTSS. We extend the well-known pointer generator model for the combined task of summarisation and simplification. We have collected our parallel corpus from the simplified summaries written by domain experts published on the science news website EurekaAlert (www.eurekalert.org). Our results show that our proposed HTSS model outperforms neural text simplification (NTS) on SARI score and abstractive text summarisation (ATS) on the ROUGE score. We further introduce a new metric (CSS1) which combines SARI and Rouge and demonstrates that our proposed HTSS model outperforms NTS and ATS on the joint task of simplification and summarisation by 38.94% and 53.40%, respectively. We provide all code, models and corpora to the scientific community for future research at the following URL: https://github.com/slab-itu/HTSS/.
The Impact of Filter Size and Number of Filters on Classification Accuracy in CNN

Convolution Neural Networks (CNNs) have received considerable attention due to their ability to learn directly from data classification features. CNNs used for human motion classification, where predefined and fixed convolutional filter size used. In this paper, different sizes and numbers of filters were used with CNN to determine their effect on accuracy of human motion classification. This work has been done through series of experiments; in each experiment, different filter size and number of filters have been applied. The best performance has been obtained when using 4 convolution layers and 2 pooling layers, whereas has been used the large filter size with upper convolution layer and with each layer the size of filter decreased and number of filters increased, so that, the maximum value of the accuracy classification was 98.98%.