Reviewerly/exHarmony huggingface dataset:
Deadline‐constrained cost‐energy aware workflow scheduling in cloud

Abstract Nowadays, scientists are dealing with large‐scale scientific workflows that need a high processing capacity platform to facilitate on‐time completion. Cloud computing is the ideal platform to overcome this problem as it has several resources that scientists may choose from depending on the size of their applications. However, using cloud computing requires some monetary charges. Recently cloud computing providers started a new pricing schema that offers to their users a set of resources with specific combinations of CPU frequency configurations settings and price. The selected configurations settings reflect energy consumption. Besides, the configuration selection to meet users' satisfaction (minimum cost) and providers' satisfaction (energy saving) is crucial. Therefore, a multiobjective (cost and energy) efficient mechanism is essential. In this article, we address an important novel problem concerning multiobjective deadline constrained workflow scheduling in the cloud. We first study the relationship between cost minimization and minimization of the energy consumption in a cloud environment, and then discuss, develop, and propose an algorithm with two variants to help the system satisfy both sides (users and providers) at the same time during the selection of the configuration. The proposed heuristic is evaluated using specified real‐world applications. The observed results indicate that our heuristic can reduce significantly the energy consumption and the cost at the same time.
Augmented Reality Technology ReAR Contribution to The Student Interest in High Schools Pontianak Indonesia

This research looks at the effectiveness of classroom learning with Augmented Reality application in learning Chemical Electrolyte Solutions. This research employs a qualitative methodology. Immanuel High School Pontianak is the site of this research. Observation, document analysis, and interviews were used to gather data information. Interviews with teaching staff and students were performed. In addition to more interviews with school educators and instructors, triangulation was utilized to evaluate the veracity of the data. The assessment of the instructor on learning media utilizing technology for increased reality reveals valid criteria with an average value of 3,55. Based on the questionnaire response criteria table, the average score of the student questionnaire of 28 students showed that the overall average score was 1106 with a good category. Augmented Reality technology as a virtual lab proves that it can simplify and help student learning. During the COVID-19 pandemic, the use of technology with Augmented Reality for learning in schools may also be an alternative to virtual laboratories.
Wireless network security game based on conditional privacy policy

With the development of Internet technology, wireless body area network technology is proposed to improve people’s scientific and technological information level in this environment. Since the information collected and processed by wireless body area network is the privacy data of users, security and privacy are the focus of wireless body area network research. In this paper, a scheme to establish a variable radius mixing region is proposed. This scheme obtains the combined communication range by analyzing the user privacy conditions after the hybrid zone is established. According to the different conditions of users, a variable radius dynamic hybrid zone was established to maximize benefits. After the establishment of the hybrid zone is initiated, the members in the zone use the temporary symmetric secret key obtained from the unit for encrypted communication to ensure the demand for real-time message transmission. And finally an anonymous change protocol is proposed. The experimental data prove the feasibility of the renaming scheme of the mixed zone. Compared with other methods, the computation and traffic of this protocol are increased by 7% and 9 % respectively. The validation efficiency of batch validation algorithm is 93 %. The scheme in this paper is more flexible than the establishment of other fixed mixed zones, and the accuracy of this scheme is more accurate than the establishment of general dynamic mixed zones. Finally, it is proved that this scheme not only meets the conditional privacy policy but also uses the model to establish a dynamic hybrid area, which has more accurate accuracy and lower computational consumption, and plays an important role in the security protection of network privacy.
The short texts classification based on neural network topic model

Aiming at the low effectiveness of short texts feature extraction, this paper proposes a short texts classification model based on the improved Wasserstein-Latent Dirichlet Allocation (W-LDA), which is a neural network topic model based on the Wasserstein Auto-Encoder (WAE) framework. The improvements of W-LDA are as follows: Firstly, the Bag of Words (BOW) input in the W-LDA is preprocessed by Term Frequency–Inverse Document Frequency (TF-IDF); Subsequently, the prior distribution of potential topics in W-LDA is replaced from the Dirichlet distribution to the Gaussian mixture distribution, which is based on the Variational Bayesian inference; And then the sparsemax function layer is introduced after the hidden layer inferred by the encoder network to generate a sparse document-topic distribution with better topic relevance, the improved W-LDA is named the Sparse Wasserstein-Variational Bayesian Gaussian mixture model (SW-VBGMM); Finally, the document-topic distribution generated by SW-VBGMM is input to BiGRU (Bidirectional Gating Recurrent Unit) for the deep feature extraction and the short texts classification. Experiments on three Chinese short texts datasets and one English dataset represent that our model is better than some common topic models and neural network models in the four evaluation indexes (accuracy, precision, recall, F1 value) of text classification.
Foids

We present a bio-inspired fish simulation platform, which we call "Foids", to generate realistic synthetic datasets for an use in computer vision algorithm training. This is a first-of-its-kind synthetic dataset platform for fish, which generates all the 3D scenes just with a simulation. One of the major challenges in deep learning based computer vision is the preparation of the annotated dataset. It is already hard to collect a good quality video dataset with enough variations; moreover, it is a painful process to annotate a sufficiently large video dataset frame by frame. This is especially true when it comes to a fish dataset because it is difficult to set up a camera underwater and the number of fish (target objects) in the scene can range up to 30,000 in a fish cage on a fish farm. All of these fish need to be annotated with labels such as a bounding box or silhouette, which can take hours to complete manually, even for only a few minutes of video. We solve this challenge by introducing a realistic synthetic dataset generation platform that incorporates details of biology and ecology studied in the aquaculture field. Because it is a simulated scene, it is easy to generate the scene data with annotation labels from the 3D mesh geometry data and transformation matrix. To this end, we develop an automated fish counting system utilizing the part of synthetic dataset that shows comparable counting accuracy to human eyes, which reduces the time compared to the manual process, and reduces physical injuries sustained by the fish.
Participation reimagined: co-design of the self through territory, memory, and dignity

Participation is a key theoretical and methodological aspect at the nexus of tensions between design as professional practice and design in community settings. In this article, we navigate some of these tensions by thinking with a case of community-based co-design in Colombia. We build on the work of participatory design scholars to surface the challenges posed by participation when dealing with design’s critical examination, and the need for new frameworks for theory and practice, especially when working with historically underrepresented populations. Using narrative inquiry, we centre and explore the methodology used by an ‘experiencia comunitaria’ (communal experience) in Colombia: the ‘Saber y Vida’ programme. We highlight three key aspects of the programme’s propositions: (1) identity as an anchor to participation; (2) symbolic language as a device mediating between individuals and communities; and (3) dignity as a pre-condition to participation. We end by reflecting on how these features are in tension and resonance with current design theory and practice.
Finding Their Data Voice: Practices and Challenges of Dashboard Users

Dashboards are the ubiquitous means of data communication within organizations. Yet we have limited understanding of how they factor into data practices in the workplace, particularly for data workers who do not self-identify as professional analysts. We focus on data workers who use dashboards as a primary interface to data, reporting on an interview study that characterizes their data practices and the accompanying barriers to seamless data interaction. While dashboards are typically designed for data consumption, our findings show that dashboard users have far more diverse needs. To capture these activities, we frame data workers' practices as data conversations: conversations with data capture classic analysis (asking and answering data questions), while conversations through and around data involve constructing representations and narratives for sharing and communication. Dashboard users faced substantial barriers in their data conversations: their engagement with data was often intermittent, dependent on experts, and involved an awkward assembly of tools. We challenge the visualization and analytics community to embrace dashboard users as a population and design tools that blend seamlessly into their work contexts.
A Deep Learning-based Indoor Scene Classification Approach Enhanced with Inter-Object Distance Semantic Features

Convolutional Neural Networks (CNNs) have been increasingly applied in visual classification tasks by replacing hand-crafted features with deep features. However, problems such as inter-class similarity and intra-class variation led to the need of obtaining more descriptive features. To accomplish this, a new semantic inter-object relationship approach is proposed, which is based on the distance relationships between recognized objects. This new source of information represents how close or apart objects belonging to two object classes are, which, together with the number of object occurrences, allows to develop a more descriptive semantic feature representation of the scene. To exploit such semantic features, a two-branch CNN architecture based on 1D and 2D convolutional layers, is proposed. Also, an enhancement version, GSF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> AppV2, of the Global and Semantic Feature Fusion described in [1] is proposed by integrating the new semantic inter-object relationship approach, as well as the aforementioned two-branch CNN architecture. The GSF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> AppV2 is also composed of a CNN-based global feature branch, where five different CNN-based feature extraction approaches were assessed as global feature extraction modules. Moreover, to combine global and semantic features, two feature fusion approaches are proposed and evaluated: correlation and triple concatenation. GSF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> AppV2 was evaluated in two benchmark datasets: the SUN RGB-D and NYU Depth V2. State-of-the-art results were achieved on both datasets, showing the effectiveness of the proposed semantic feature approach on the pipeline.
An Improved Non-Negative Latent Factor Model for Missing Data Estimation via Extragradient-Based Alternating Direction Method

In this article, an improved double factorization-based symmetric and non-negative latent factor (Im-DF-SNLF) model is proposed to make the estimation for missing data in symmetric, high-dimensional, and sparse (SHiDS) matrices. The main idea of the Im-DF-SNLF model is fourfold: 1) considering the data variety in the practical engineering, non-negative latent factors (NLFs) in different cases are considered to better reflect the latent relationships between entries; 2) the l2 -norm regularization and the Lagrangian multiplier technique are simultaneously adopted to handle the overfitting and satisfy the non-negative constraint for latent factors (LFs); 3) the extragradient-based alternating direction (EGAD) method is utilized to accelerate the model training and rigidly guarantee the non-negativity of LFS; and 4) a rigorous proof is provided to show that, under the given assumption that the objective function is smooth and has a Lipschitz continuous gradient, the designed algorithm can find an ϵ -optimal solution within O(1/ϵ) , and the upper bound of the learning rate is given by 1/2. Finally, experimental results on public datasets are given to demonstrate the effectiveness of our proposed Im-DF-SNLF model with EGAD.
Mask encoding: A general instance mask representation for object segmentation

Instance segmentation is one of the most challenging tasks in computer vision, which requires separating each instance in pixels. To date, a low-resolution binary mask is the dominant paradigm for representation of instance mask. For example, the size of the predicted mask in Mask R-CNN is usually 28×28. Generally, a low-resolution mask can not capture the object details well, while a high-resolution mask dramatically increases the training complexity. In this work, we propose a flexible and effective approach to encode the high-resolution structured mask to the compact representation which shares the advantages of high-quality and low-complexity. The proposed mask representation can be easily integrated into two-stage pipelines such as Mask R-CNN, improving mask AP by 0.9% on the COCO dataset, 1.4% on the LVIS dataset, and 2.1% on the Cityscapes dataset. Moreover, a novel single shot instance segmentation framework can be constructed by extending the existing one-stage detector with a mask branch for this instance representation. Our model shows its superiority over the explicit contour-based pipelines in accuracy with similar computational complexity. We also evaluate our method for video instance segmentation, achieving promising results on YouTube-VIS dataset. Code is available at: https://git.io/AdelaiDet
Two-Branch Deconvolutional Network With Application in Stereo Matching

Deconvolutional networks have attracted extensive attention and have been successfully applied in the field of computer vision. In this paper we propose a novel two-branch deconvolutional network (TBDN) that can improve the performance of conventional deconvolutional networks and reduce the computational complexity. A feasible iterative algorithm is designed to solve the optimization problem for the TBDN model, and a theoretical analysis of the convergence and computational complexity for the algorithm is also provided. The application of the TBDN in stereo matching is presented by constructing a disparity estimation network. Extensive experimental results on four commonly used datasets demonstrate the efficiency and effectiveness of the proposed TBDN.
A Feedback Architecture of High Speed True Random Number Generator based on Ring Oscillator

True random number generators (TRNG) are widely used to generate encryption keys in information security systems [1]–[2]. In TRNG, entropy source is a critical module who provides the source of randomness of output bit stream. The unavoidable electrical noise in circuit becomes an ideal entropy source due to its unpredictability. Among the methods of capturing electrical noise, ring oscillator-based entropy source makes the TRNG most robust to deterministic noise and 1/f noise which means the strongest anti-interference capability, so it is simple in structure and easy to integrate [3]. Thus, great research attention has focused on ring oscillator-based TRNGs [3] –[7]. In [4], a high-speed TRNG with 100Mbps output bit rate was proposed, but it took up too much power and area. A TRNG based on tetrahedral ring oscillator was proposed in [5]. Its power consumption was very low but the output bit rate was also very low. A ring oscillator-based TRNG with low output bit rate but high power was proposed in [7]. In a word, none of the above architectures achieve an appropriate compromise between bit rate and power consumption. This work presents a new feedback architecture of TRNG based on tetrahedral ring oscillator. The output random bit stream generates a relative random control voltage that acts on the transmission gates in oscillator through a feedback loop, thus increasing phase jitter of the oscillator and improving output bit rate. Furthermore, an XOR chain-based post-processing unit is added to eliminate the statistical deviations and correlations between raw bits.
Tone mapping high dynamic range images based on region-adaptive self-supervised deep learning

This paper presents a region-adaptive self-supervised deep learning (RASSDL) technique for high dynamic range (HDR) image tone mapping. The RASSDL tone mapping operator (TMO) is a convolutional neural network (CNN) trained on local image regions that can seamlessly tone map images of arbitrary sizes. The training of RASSDL TMO is through the design of a self-supervising target that automatically adapts to the local image regions based on their information contents. The self-supervising target is designed to ensure the tone-mapped output achieves a balance between preserving the relative contrast of the original scene and the visibilities of the fine details to achieve faithful reproduction of the HDR scene. Distinguishing from many existing TMOs that require manual tuning of parameters, RASSDL is parameter-free and completely automatic. Experimental results demonstrate that RASSDL TMO can achieve state-of-the-art performance in terms of preserving overall contrasts, revealing fine details, and being free from visual artifacts.
A Study on the Content of Mental and Physical Stability Game in Virtual Reality through EEG Detection

This paper proposes a study of mental and physical stable game content in virtual reality through brainwave detection. More and more people are complaining of fatigue due to stress caused by continuous work or excessive use of electronic devices. To relieve stress, it is important to rest your brain through sufficient sleep or mental relaxation time. Experts also stress that rest is important to improve the brain's memory, encouraging the brain to perform time to organize the period through mental relaxation. It is a virtual reality content that can build a realistic environment, and it proposes content that can confirm the process of resting through light activities and factors that help rest as a process of brain wave change.
Generative Adversarial Network-based Channel Estimation in High-Speed Mobile Scenarios

To reduce the complexity of the channel estimation method and improve the estimation performance in the high-speed mobile scenario, a generative adversarial network (GAN) based channel estimation method is proposed to ensure the stability of the high-speed railway communication system with the fast time-varying and non-stationary channel. Firstly, we use the GAN's discriminator to learn and extract channel time-varying features. And the standard wireless high-speed mobile channel data is used for offline training of the network. After that, the GAN's generator is used to extract the features of high-dimensional time-varying channel to generate and restore channel information close to reality. The simulation results show that the proposed channel estimation method can effectively extract the characteristic and distribution of fast time-varying channel and predict the channel response.
Exploiting Heterogeneous Architectures for Rigid Image Registration

Current medical practice increasingly relies on multiple imaging techniques to deliver faster and more accurate diagnosis as well as live medical imaging integration to guide critical surgery procedures. An essential stage of these processes is Image Registration, which is responsible for geometrically overlaying floating images on a reference. However, most of the available tools are proprietary, cumbersome to use/customize, or do not deliver real-time performance. Therefore, we propose an easy-to-use open-source framework for rigid multi-modal image registration that exploits heterogeneous architectures and algorithmic optimizations. Compared to literature software tools, our solution shows outstanding speedups from a 3.8 x to a 16 x and an improved Intersection over Union of 0.97.
A Systematic Review on Dyadic Conversation Visualizations

As many services are provided through text and voice systems, including voice calls over the internet, messaging, and emails, there is a growing need for both individuals and organizations to understand these conversations better and find actionable insights to improve social skills. Effective visualizations that provide a lucid account of the conversation allow the user to explore such insights. In this paper, we present a systematic survey of the various methods of visualizing a conversation and research papers involving interactive visualizations and human participants. Findings from the survey show that there have been attempts to visualize most, if not all, of the types of conversation that are taking place digitally – from speech to messages and emails. Through this survey, we make two contributions. One, we summarize the current practices in the domain of visualizing dyadic conversations. Two, we provide suggestions for future dialogue visualization research.
Blockchain Adoption for Data Integrity in Higher Education E-Learning

Recent technological development has transformed the traditional learning environment in higher education. Blockchain has been identified as one of the disruptive technologies with great accountability and visibility. Blockchain has gained wide popularity because of its vast applicability to transform established processes but acceptance is still at a nascent stage. Given the low acceptability of blockchain, the purpose of this work was to investigate learners’ willingness to use it in higher education e-learning. Perceived convenience was used as an additional construct to the existing Technology Adoption Model (TAM) variables attitude, perceived usefulness, and adoption intention to extend the model. Partial Least Square–Structure Equation Modelling (PLS-SEM) was deployed to examine the proposed conceptual model in the study. The findings of the study explain that the variable adopted in the model is the right fit to predict user behavioral intention towards blockchain adoption in higher education e-learning. The validated model in the research would assist professionals and policymakers in effective blockchain implementation.