Reviewerly/exHarmony huggingface dataset:
Towards a Postphenomenological Approach to Wearable Technology through Design Journeys

The field of wearable technology has extensively described the opportunities, challenges as well as the concerns aroundintegrating digital technologies into fashion. However, it has so far not provided a sufficiently clear and embodiedunderstanding of technology. Technology therefore is often still limited to something that adds functionality to textiles orclothing. Additionally, technology is seen as a way to enhance the visual expression and thereby the representational character of clothing. Both approaches seem to hinder the intersection of digital technologies and textiles on a material and embodied level. While the role of wearable technologies in the everyday could benefit from it. In this paper, we therefore argue for reframing the understanding of technology to better facilitate the integration of digital technologies (i.e. electronicsand software) into everyday fashion. A reflective analysis of the process of designing wearable technologies, based on the design practice of the first author, reveals that technology is considered a material and that its material qualities are not thought of as either functional or aesthetic. These insights, arising from practice, have led us to turn to postphenomenology (a strand of philosophy of technology) to come to further conceptualization. Postphenomenology looks at the ways in which technologies mediate or “coshape” the relationship between human beings and the world. By seeing technologies as the media that connect humans to the world, they acquire a material and contextual dimension. The idea that artifacts mediate materially thus offers a very useful starting point for understanding and designing wearable technologies. The contributionof this article is twofold, namely, first, to provide a material understanding of the technology on the basis of design practice. And secondly, to bridge design practice with theory by suggesting to bring postphenomenology into fashion to better facilitate the design of wearable technologies for the everyday.
Marc: Multi-Granular Representation Learning for Networks Based on the 3-Clique

Network embedding is of paramount importance in many real applications, such as node classification, network visualization, and link prediction. Existing methods can effectively encode different structural properties into representations. Most of them are single-granular representation learning methods that do not enable the network to be easily analyzed at various scales. There are many kinds of hierarchical structures in reality. In this paper, we propose a novel algorithm multi-granular representation learning for networks based on the 3-clique named Marc. It makes the representations of the current network be formed by both considering the structure of this granular network and inheriting the coarser network representation results. Firstly, we propose the 3-clique coarsening strategy. A 3-clique is coarsened to be a supernode in the coarser network. These coarsened networks with different granularities, preserve the original network's main structure. Secondly, we use the single-granular optimization objective function to obtain the nodes' representations of each granular network. Finally, the refinement model learns the nodes' representations, starting from the coarsest network. The learned representations of supernodes serve as good initializations for embedding the corresponding coarsened nodes from the finer network. This process is repeated until we obtain an embedding for each node in the original network which preserves the relationship among multi-granular networks. The experimental results on five public datasets, Wiki, BlogCatalog, Cora, CiteSeer, and DBLP, demonstrate that Marc has a better Macro F1 value for classification tasks than the baseline methods.
Research on fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming

Regarding the problems of insufficient image segmentation intelligence, low compression rate, slow speed for global searching to find the optimal fractal image compression encoding, and bad decoding effect, this article proposes the fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming. Firstly, according to the accurate and fast image classification of deep convolutional neural network and the fast search and matching encoding advantages of gene expression programming, it realizes theoretically the action mechanism of fractal image compression hybrid encoding by combining the convolutional neural network and the gene expression programming; then, it uses the deep convolutional neural network to train and classify the image, and uses the adaptive quadtree segmentation method to segment the classified image, thus generating the domain block and range block classification set. According to the action mechanism of gene expression programming in fractal image compression encoding, it then quickly obtains the optimal solution of fractal image compression encoding by searching and encoding the sub-blocks of range block classification set and the classification set corresponding to the domain. Finally, in the CPU/GPU environment, it conducts the comparative experiment with basic fractal image compression algorithm and fractal image compression algorithm based on convolutional neural network. The experimental results show that this proposed algorithm outperforms similar algorithms in terms of image segmentation speed and accuracy as well as fractal compression encoding speed and compression ratio. Therefore, this algorithm is a fractal image compression algorithm with intelligent segmentation, fast encoding and high compression ratio.
Scale Voting With Pyramidal Feature Fusion Network for Person Search

Person search aims to find the target person among a large gallery set of real scene images.Candidates should be detected and cropped before recognizing their identifications.The detection process results in a great variance of object image scales.To address this we propose Pyramidal Feature Fusion Network that integrates the top-down mid-level features to provide multi-level feature outputs.Furthermore, we propose to apply each of the mid-level features independently in ranking and we design a ranking algorithm named Scale Voting that votes among these ranking results from different feature levels to get the final ranking order.In this approach, we can make better use of the diversity and consistency information that is hidden in different mid-levels of features.The proposed algorithm achieves the state-of-the-art performance on prevalent person search datasets.
Flow Constraint Language for Coordination by Exogenous Connectors

Construction of Cyber-Physical Systems (CPS) by reusing or composing existing components (sub-systems) is the emerging need in software based system development. For software development, a number of paradigms exists to deal with the reusability at different levels. Component-based development (CBD) represents a paradigm shift in software development for its emphasis on development for/with reuse. Software composition mechanisms are the essence of this paradigm. In general, a software system in CBD is comprised of two kinds of elements: computation and communication. Defining/creating these elements in a generic way and for system construction allowing these elements to be customised in specific to the system needs can increase the level of reusability. In this paper, for a development paradigm (referred to as EX-MAN in CBD) for CPS construction, we focus on software elements dealing with the communications elements (referred to as connectors) among the computational elements. We define constraints (written in our proposed flow constraint language (FCL)) as a property for coordination by the exogenous connectors to customise the behaviour of connectors for system construction. In this paper, the semantics of FCL constraints for a sample exogenous connector is described for system construction/execution. In order to verify the operational semantics of FCL constraints of this exogenous connector, we use Coloured Petri Nets (in CPN tools) to model and simulate the connectors with constraints. Exogenous connectors are implemented in a tool exogenous composition framework (ECF) for system construction.
HaarPooling: Graph Pooling with Compressive Haar Basis

Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs adapt to input graphs of varying size and structure. We propose a new graph pooling operation based on compressive Haar transforms -- HaarPooling. HaarPooling implements a cascade of pooling operations; it is computed by following a sequence of clusterings of the input graph. A HaarPooling layer transforms a given input graph to an output graph with a smaller node number and the same feature dimension; the compressive Haar transform filters out fine detail information in the Haar wavelet domain. In this way, all the HaarPooling layers together synthesize the features of any given input graph into a feature vector of uniform size. Such transforms provide a sparse characterization of the data and preserve the structure information of the input graph. GNNs implemented with standard graph convolution layers and HaarPooling layers achieve state of the art performance on diverse graph classification and regression problems.
Geometry and Analogies: A Study and Propagation Method for Word Representations

In this paper we discuss the well-known claim that language analogies yield almost parallel vector differences in word embeddings. On the one hand, we show that this property, while it does hold for a handful of cases, fails to hold in general especially in high dimension, using the best known publicly available word embeddings. On the other hand, we show that this property is not crucial for basic natural language processing tasks such as text classification. We achieve this by a simple algorithm which yields updated word embeddings where this property holds: we show that in these word representations, text classification tasks have about the same performance.
Optimal Sorting with Persistent Comparison Errors.

We consider the problem of sorting n elements in the case of persistent comparison errors. In this problem, each comparison between two elements can be wrong with some fixed (small) probability p, and comparisons cannot be repeated (Braverman and Mossel, SODA'08). Sorting perfectly in this model is impossible, and the objective is to minimize the dislocation of each element in the output sequence, that is, the difference between its true rank and its position. Existing lower bounds for this problem show that no algorithm can guarantee, with high probability, maximum dislocation and total dislocation better than Omega(log n) and Omega(n), respectively, regardless of its running time.In this paper, we present the first O(n log n)-time sorting algorithm that guarantees both O(log n) maximum dislocation and O(n) total dislocation with high probability. This settles the time complexity of this problem and shows that comparison errors do not increase its computational difficulty: a sequence with the best possible dislocation can be obtained in O(n log n) time and, even without comparison errors, Omega(n log n) time is necessary to guarantee such dislocation bounds. In order to achieve this optimality result, we solve two sub-problems in the persistent error comparisons model, and the respective methods have their own merits for further application. One is how to locate a position in which to insert an element in an almost-sorted sequence having O(log n) maximum dislocation in such a way that the dislocation of the resulting sequence will still be O(log n). The other is how to simultaneously insert m elements into an almost sorted sequence of m different elements, such that the resulting sequence of 2m elements remains almost sorted.
Computer Based Test (CBT) System for Student Academic Examination

Computer based test (CBT) becomes more pervasive in the last few decades, there is growing need for educators to consider modes of assessment because there are many advantages.CBT is an effective solution for mass education evaluation, so that CBT is very necessary applied especially in evaluation and assessment for students.The purpose of this research is to design a system for student academic examination based on computer based test (CBT).The system is designed to facilitate the examnination processes and manage challenges surrounding the conduct of examination, autosubmission, auto-marking and examinaton result report generation.The kind of this research is part of research and development (R&D) research.Model design is adopted from Thiagaraja, which have four-D model: (1) define, (2) design, (3) development, (4) disseminate.This research was executed until the limited trial of design model because the lack of the time of this study.Define was analysed student"s need, the goal, and the CBT"s concept.Design was choosed the CBT"s software, software that used in this study was learning management system (LMS) Moodle.The goal of this study is create CBT system for student examination and showed the effectivity of the system.
Highly-parallel Optical Fourier Intensity Convolution Filter For Image Classification

Convolution Neural Networks (CNN) extracts features from large dataset by spatial filtering. We experimentally demonstrate convolutional filtering using Fourier optics. Unlike phase-based modulation, we show amplitude-based convolutions performing one layer of an artificial CNN.
Risk-averse Distributional Reinforcement Learning: A CVaR Optimization Approach

Conditional Value-at-Risk (CVaR) is a well-known measure of risk that has been directly equated to robustness, an important component of Artificial Intelligence (AI) safety. In this paper we focus on optimizing CVaR in the context of Reinforcement Learning (RL), as opposed to the usual risk-neutral expectation. As a first original contribution, we improve the CVaR Value Iteration algorithm (Chow et al., 2015) in a way that reduces computational complexity of the original algorithm from polynomial to linear time. Secondly, we propose a sampling version of CVaR Value Iteration we call CVaR Q-learning. We also derive a distributional policy improvement algorithm, and later use it as a heuristic for extracting the optimal policy from the converged CVaR Q-learning algorithm. Finally, to show the scalability of our method, we propose an approximate Q-learning algorithm by reformulating the CVaR Temporal Difference update rule as a loss function which we later use in a deep learning context. All proposed methods are experimentally analyzed, including the Deep CVaR Q-learning agent which learns how to avoid risk from raw pixels.
Announcing the Journal of the Medical Library Association’s data sharing policy

As librarians are generally advocates of open access and data sharing, it is a bit surprising that peer-reviewed journals in the field of librarianship have been slow to adopt data sharing policies. Starting October 1, 2019, the Journal of the Medical Library Association (JMLA) is taking a step forward and implementing a firm data sharing policy to increase the rigor and reproducibility of published research, enable data reuse, and promote open science. This editorial explains the data sharing policy, describes how compliance with the policy will fit into the journal’s workflow, and provides further guidance for preparing for data sharing.
Application of the Support Vector Machine and Neural Network Model Based on Particle Swarm Optimization for Breast Cancer Prediction

There are several studies in the medical field that classify data to diagnose and analyze decisions. To predict breast cancer, this study compares two methods, the Support Vector Machine method and the Neural Network method based on Particle Swarm Optimization (PSO) which is intended to determine the highest accuracy value in the Coimbra dataset data. To implement the Support Vector Machine and Neural Network method based on PSO, RapidMiner software is used. Then the application results are compared using Confusion Matrix and ROC Curve. Based on the accuracy of the two models, it is known that the PSO-based Neural Network model has a higher accuracy value of 84.55% than the results of the PSO-based Vector Support Machine with an accuracy value of 80.08%. The calculation results, the accuracy of the AUC performance obtained by the results of the study are, the two methods are PSO-based Neural Network with AUC value of 0.885 and PSO-based Support Vector Machine with a value of 0.819 included in the category of Good Classification.
SUBMANIFOLD SPARSE CONVOLUTIONAL NETWORKS

We present method for effective kidney and kidney's tumor segmentation based on the 3-dimensional model reconstructed from the computed tomography (CT) input images.Analysis of the 3D model is done using submanifold sparse convolutional network (SSCN).
CBRm: Case based Reasoning Approach for Imputation of Medium Gaps

This paper presents a new algorithm called CBRm for univariate time series imputation of medium-gaps inspired by the algorithm called Case Based Reasoning Imputation (CBRi) for short-gaps.The performance of the proposed algorithm is analyzed in meteorological time series corresponding to maximum temperatures; also it was compared with several similar techniques.Although the algorithm failed to overcome in some cases to other proposals regarding precision, the results achieved are encouraging considering that some weaknesses of other proposals with which it was compared were outperformed.
New Designs of k-means Clustering and Crossover Operator for Solving Traveling Salesman Problems using Evolutionary Algorithms

The traveling salesman problem is a well-known combinatorial optimization problem with permutation-based variables, which has been proven to be an NP-complete problem. Over the last few decades, many evolutionary algorithms have been developed for solving it. In this study, a new design that uses the k-means clustering method, is proposed to be used as a repairing method for the individuals in the initial population. In addition, a new crossover operator is introduced to improve the evolving process of an evolutionary algorithm and hence its performance. To investigate the performance of the proposed mechanism, two popular evolutionary algorithms (genetic algorithm and differential evolution) have been implemented for solving 18 instances of traveling salesman problems and the results have been compared with those obtained from standard versions of GA and DE, and 3 other state-of-the-art algorithms. Results show that the proposed components can significantly improve the performance of EAs while solving TSPs with small, medium and large-sized problems.
An Empirical Evaluation of Arabic-Specific Embeddings for Sentiment Analysis

In this paper, we propose several specific embeddings in Arabic sentiment analysis (SA) framework. Indeed, Arabic is characterized by its agglutination and morphological richness contributing to great sparsity that could affect embedding quality. This work presents a rigorous study that compares different types of Arabic-specific embeddings. We evaluate them with 2 neural architectures: one based on convolutional neural network (CNN) and the other one based on Bidirectional Long Short-Term Memory Bi-LSTM. Experiments are done on the Large Arabic-Book Reviews corpus LABR. Our best results boost previous published accuracy by 1.9%. Moreover, we experiment combination of our individual systems defining very confident decision, reaching an accuracy of 92.2% on 98.25% of LABR test dataset.
The Power Cepstrum Calculation with Convolutional Neural Networks

A model of neural network with convolutional layers that calculates the power cepstrum of the input signal is proposed. To achieve it, the network calculates the discrete-time short-term Fourier transform internally, obtaining the spectrogram of the signal as an intermediate step. The weights of the neural network can be calculated in a direct way or they can be obtained through training with the gradient descent method. The behaviour of the training is analysed. The model originally proposed cannot be trained in a complete way, but both the part that calculates the spectrogram and also a variant of the cepstrum equivalent to the autocovariance that keeps a big part of its usefulness can be trained. For the cases of successful training, an analysis of the obtained weights is done. The main conclusions indicate, on the one hand, that it is possible to calculate the power cepstrum with a neural network; on the other hand, that it is possible to use these networks as the initial layers of a deep learning model for the case of trainable models. In these layers, weights are initialised with the discrete Fourier transform (DFT) coefficients and they are trained to adapt to specific classification or regression problems.
Fuzzy Systems-as-a-Service in Cloud Computing

Fuzzy systems have become widely accepted and applied in a host of domains such as control, electronics or mechanics.The software for construction of these systems has traditionally been exploited from tools, platforms and languages run on-premise computing infrastructure.On the other hand, rise and ubiquity of the cloud computing model has brought a revolutionary way for computing services deployment.The boost of cloud services is leading towards increasingly specific service offering just as data mining and machine learning service.Unfortunately, so far, no definition for fuzzy system as service is available.This paper identifies this opportunity and focus on developing a proposal for fuzzy system-as-a-service definition.To achieve this, the proposal pursues three objectives: the complete description of cloud services for fuzzy systems using semantic technology, the composition of services and the exploitation of the model in cloud platforms for integration with other services.As an illustrative case, a real-world problem is addressed with the proposed specification.
TANTANGAN DAN SOLUSI DALAM PERKEMBANGAN TEKNOLOGI PENDIDIKAN DI INDONESIA

Educational technology in general can be interpreted as the application of technology to educational activities. The most important thing here is the process of integration between people, ideas, organizations and equipment. Based on this assumption, educational technology can be interpreted as a logical, systematic, and scientific approach to educational activities. Development of Educational Technology is currently undergoing very rapid changes, especially in the field of education. In the use of ICTs in the field of education often face several obstacles, including: the lack of procurement of ICT infrastructure in various regions, the use of used technology equipment, the lack of legal instruments in the ICT field, and the high cost of procurement and use of ICT facilities. To solve the problems mentioned above, there needs to be a solution as a condition for the successful implementation of ICT in the learning process, namely: teachers and students must have access to digital and internet technology in schools, interactive learning materials that use laptops / computers, teachers must have the ability and skills in using digital tools, there must be sufficient budget to hold, develop, and maintain facilities and infrastructure and support from all parties, principals, teachers, and students in implementing ICT learning.