Reviewerly/exHarmony huggingface dataset:
A STOCHASTIC DEVELOPMENT OF CLOUD COMPUTING BASED TASK SCHEDULING ALGORITHM

Due to diversity of services with respect to technology and resources, it is challenging to choose virtual machines (VM) from various data centres with varied features like cost minimization, reduced energy consumption, optimal response time and so on in cloud Infrastructure as a Service (IaaS) environment. The solutions available in the market are exhaustive computationally and aggregates multiple objectives to procure single trade-off that affects the solution quality inversely. This paper describes a hybrid algorithm that facilitates VM selection for scheduling applications based on Gravitational Search and Non-dominated Sorting Genetic Algorithm (GSA and NSGA). The efficiency of the proposed algorithm is verified by the simulation results.
Bayes EMbedding (BEM)

Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to content recommendation. The two types of graphs can contain distinct and complementary information for the same entities/nodes. However, previous works focus either on knowledge graph embedding or behavior graph embedding while few works consider both in a unified way. Here we present BEM, a Bayesian framework that incorporates the information from knowledge graphs and behavior graphs. To be more specific, BEM takes as prior the pre-trained embeddings from the knowledge graph, and integrates them with the pre-trained embeddings from the behavior graphs via a Bayesian generative model. BEM is able to mutually refine the embeddings from both sides while preserving their own topological structures. To show the superiority of our method, we conduct a range of experiments on three benchmark datasets: node classification, link prediction, triplet classification on two small datasets related to Freebase, and item recommendation on a large-scale e-commerce dataset.
Why is Data Visualization Important? What is Important in Data Visualization?

different ways in which data visualization is used, explores the opportunities for future research in the area, and looks at how data visualization is taught.
Text alignment in early printed books combining deep learning and dynamic programming

We describe a technique for transcript alignment in early printed books by using deep models in combination with dynamic programming algorithms. Two object detection models, based on Faster R-CNN, are trained to locate words. We first train an initial model to recognize generic words and hyphens by using information about the number of words in text lines. Using the model prediction on pages with a line-by-line ground-truth annotation is available, we train a second model able to detect landmark words. The alignment is then based on the identification of landmark words in pages where we only know the text corresponding to zones in the page. The proposed technique is evaluated on a publicly available digitization of the Gutenberg Bible while the transcription is based on the Vulgata, a late 4th century Latin translation of the Bible.
Graph Attention Networks for Neural Social Recommendation

In recent years, social recommendation is a research hotspot because it contains social network information which can effectively solve the problem of data sparsity and cold start. But the social recommendation task faces two problems: one is that how to accurately learn user latent vector and item latent vector from user-item interaction graph and social graph, the other is that how to depict the intrinsic and complex interaction between users and items. With the development of graph neural networks, node embedding is becoming more and more accurate on the graph. Besides neural collaborative filtering explores the interaction of users and items deeply. So in this paper, we propose a novel model: graph attention networks for neural social recommendation (GAT-NSR). This model adopts multi-head attention mechanism for message passing on the two graphs, which get user & item latent vector from different perspectives. And also we design a neural collaborative recommendation module to capture the inherent characteristics of user-item interaction behavior for recommendation. Finally, detailed experimental results on two real-world datasets clearly prove the effectiveness of our proposed model.
Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms.

As a popular meta-learning approach, the model-agnostic meta-learning (MAML) algorithm has been widely used due to its simplicity and effectiveness. However, the convergence of the general multi-step MAML still remains unexplored. In this paper, we develop a new theoretical framework to provide such convergence guarantee for two types of objective functions that are of interest in practice: (a) resampling case (e.g., reinforcement learning), where loss functions take the form in expectation and new data are sampled as the algorithm runs; and (b) finite-sum case (e.g., supervised learning), where loss functions take the finite-sum form with given samples. For both cases, we characterize the convergence rate and the computational complexity to attain an $\epsilon$-accurate solution for multi-step MAML in the general nonconvex setting. In particular, our results suggest that an inner-stage stepsize needs to be chosen inversely proportional to the number $N$ of inner-stage steps in order for $N$-step MAML to have guaranteed convergence. From the technical perspective, we develop novel techniques to deal with the nested structure of the meta gradient for multi-step MAML, which can be of independent interest.
Auto-Grading Jupyter Notebooks

Jupyter Notebooks are becoming more widely used, both for data science applications and as a convenient environment for learning Python. Currently, grading of assignments done in Jupyter Notebooks is typically done manually. Manual grading results in students receiving feedback only long after the assignment is complete. We implemented support for auto-grading programs written in Jupyter Notebooks within the Web-CAT auto-grading system. Scores received are directly reported to the Canvas gradebook. A Jupyter notebook extension allows students to upload their notebook files to Web-CAT directly. Survey results from class use show that 80% of students believe that getting immediate feedback from Web-CAT improved their performance. Instructors report that this implementation has significantly reduced their workload.
Novel Method of Semantic Segmentation Applicable to Augmented Reality

This paper proposes a novel method of semantic segmentation, consisting of modified dilated residual network, atrous pyramid pooling module, and backpropagation, that is applicable to augmented reality (AR). In the proposed method, the modified dilated residual network extracts a feature map from the original images and maintains spatial information. The atrous pyramid pooling module places convolutions in parallel and layers feature maps in a pyramid shape to extract objects occupying small areas in the image; these are converted into one channel using a 1 × 1 convolution. Backpropagation compares the semantic segmentation obtained through convolution from the final feature map with the ground truth provided by a database. Losses can be reduced by applying backpropagation to the modified dilated residual network to change the weighting. The proposed method was compared with other methods on the Cityscapes and PASCAL VOC 2012 databases. The proposed method achieved accuracies of 82.8 and 89.8 mean intersection over union (mIOU) and frame rates of 61 and 64.3 frames per second (fps) for the Cityscapes and PASCAL VOC 2012 databases, respectively. These results prove the applicability of the proposed method for implementing natural AR applications at actual speeds because the frame rate is greater than 60 fps.
Internet users’ attitudes towards social media advertisements: The role of advertisement design and users’ motives

This research paper aims to examine the impact of social media advertisement attributes, including informativeness, trust, and irritation, and internet users' motivational factors; namely investigation, customization, opportunity seeking, interaction, and entertainment on individuals' attitude towards advertisements over social media sites in Jordan.A conceptual research model was proposed based on the relevant literature to be empirically tested.Data was collected using an online questionnaire survey administered to 256 participants.Hypotheses were tested using single and multiple linear regression analyses.Results show that advertisement design factors along with internet users' motives were all significant factors affecting the attitude towards advertisements on social media sites with entertainment, interaction and informativeness had the highest impact.Based on the research findings, recommendations were reported for academicians and practitioners.Limitations and promising areas for further research were discussed.
Group-Based Recurrent Neural Networks for POI Recommendation

With the development of mobile Internet, many location-based services have accumulated a large amount of data that can be used for point-of-interest (POI) recommendation. However, there are still challenges in developing an unified framework to incorporate multiple factors associated with both POIs and users due to the heterogeneity and implicity of this information. To alleviate the problem, this work proposes a novel group-based method for POI recommendation jointly considering the reviews, categories, and geographical locations, called the Group-based Temporal Sentiment-Aspect-Region Recurrent Neural Network (GTSAR-RNN). We divide the users into different groups and then train an individual RNN for each group with the goal of improving its pertinence. In GTSAR-RNN, we consider not only the effects of temporal and geographical contexts but also the users’ sentimental opinions on locations. Experimental results show that GTSAR-RNN acquires significant improvements over the baseline methods on real datasets.
The ‘as code’ activities: development anti-patterns for infrastructure as code

The ‘as code’ suffix in infrastructure as code (IaC) refers to applying software engineering activities, such as version control, to maintain IaC scripts. Without the application of these activities, defects that can have serious consequences may be introduced in IaC scripts. A systematic investigation of the development anti-patterns for IaC scripts can guide practitioners in identifying activities to avoid defects in IaC scripts. Development anti-patterns are recurring development activities that relate with defective IaC scripts. The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by identifying development activities that relate with defective IaC scripts. We identify development anti-patterns by adopting a mixed-methods approach, where we apply quantitative analysis with 2,138 open source IaC scripts and conduct a survey with 51 practitioners. We observe five development activities to be related with defective IaC scripts from our quantitative analysis. We identify five development anti-patterns namely, ‘boss is not around’, ‘many cooks spoil’, ‘minors are spoiler’, ‘silos’, and ‘unfocused contribution’. Our identified development anti-patterns suggest the importance of ‘as code’ activities in IaC because these activities are related to quality of IaC scripts.
Node Representation Learning for Directed Graphs

We propose a novel approach for learning node representations in directed graphs, which maintains separate views or embedding spaces for the two distinct node roles induced by the directionality of the edges. We argue that the previous approaches either fail to encode the edge directionality or their encodings cannot be generalized across tasks. With our simple alternating random walk strategy, we generate role specific vertex neighborhoods and train node embeddings in their corresponding source/target roles while fully exploiting the semantics of directed graphs. We also unearth the limitations of evaluations on directed graphs in previous works and propose a clear strategy for evaluating link prediction and graph reconstruction in directed graphs. We conduct extensive experiments to showcase our effectiveness on several real-world datasets on link prediction, node classification and graph reconstruction tasks. We show that the embeddings from our approach are indeed robust, generalizable and well performing across multiple kinds of tasks and graphs. We show that we consistently outperform all baselines for node classification task. In addition to providing a theoretical interpretation of our method we also show that we are considerably more robust than the other directed graph approaches.
Project repositories for machine learning with TensorFlow

In machine learning, models capture intelligence from data using algorithms implemented on frameworks like TensorFlow. Models learn during the training phase; an iterative process in which parameters are tuned to improve the prediction accuracy. Software repositories are used to save the artifacts of model development so that they can be modified in subsequent releases and shared between development teams. Issues in saving the state of machine learning projects is different from standard software practices. Machine learning models are equivalent to binary executable programs and ideally one should be able to recreate the model from the information preserved in project repositories. Recreation of the model becomes necessary when there is a change in the development team as part of the product transition. Retraining of models also becomes necessary when the inaccuracy in the predictions made on new data increases beyond the acceptable limit. When traditional source code management systems are used for maintaining repositories of machine learning projects, we face challenges in keeping complete information required for model development starting from saved states. This paper presents the results of the studies conducted to identify the challenges in maintaining TensorFlow machine learning projects in repositories. Some of the existing tools are compared and recommendations are made to improve the ease of recreation of machine learning models by saving complete information in project repositories maintained in normal source code control systems.
Conceptualizing Augmented Reality Television for the Living Room

We examine the concept and characteristics of “Augmented Reality Television” (ARTV) using a four-step investigation method consisting of (1) an analysis of commonly-accepted perspectives on Augmented and Mixed Reality systems, (2) a literature survey of previous work on ARTV, (3) relevant connections with other areas of scientific investigation from TVX/IMX, such as Ambient Media, Interactive TV, and 3-D TV, and (4) by proposing a conceptual framework for ARTV called the “Augmented Reality Television Continuum.” Our work comes at a moment when the excitement and hype about the potential of AR for home entertainment has overlooked rigorous analysis and clear-cut examinations of the concepts involved, which should be the hallmark of any exact science. With this work, our goal is to draw the community’s attention toward fundamentals and first principles of ARTV and to tease out its salient qualities on solid foundations.
Effect handlers, evidently

Algebraic effect handlers are a powerful way to incorporate effects in a programming language. Sometimes perhaps even _too_ powerful. In this article we define a restriction of general effect handlers with _scoped resumptions_. We argue one can still express all important effects, while improving reasoning about effect handlers. Using the newly gained guarantees, we define a sound and coherent evidence translation for effect handlers, which directly passes the handlers as evidence to each operation. We prove full soundness and coherence of the translation into plain lambda calculus. The evidence in turn enables efficient implementations of effect operations; in particular, we show we can execute tail-resumptive operations _in place_ (without needing to capture the evaluation context), and how we can replace the runtime search for a handler by indexing with a constant offset.
Infrared marker tracking with the HoloLens for neurosurgical interventions

Abstract Patient tracking is an essential part in a surgical augmented reality system for correct hologram to patient registration. Augmented reality can support a surgeon with visual assistance to navigate more precisely during neurosurgical interventions. In this work, a system for patient tracking based on infrared markers is proposed. These markers are widely used in medical applications and meet the special medical requirements such as sterilizability. A tracking accuracy of 0.76 mm is achieved when using the near field reflectivity and depth sensor of the HoloLens. On the HoloLens a performance of 55–60 fps is reached, which grants a sufficiently stable placement of the holograms in the operating room.
Continuous-variable measurement-device-independent quantum key distribution via quantum catalysis

The continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) is a promising candidate for the immunity to side-channel attacks, but unfortunately seems to face the limitation of transmission distance in contrast to discrete-variable (DV) counterpart. In this paper, we suggest a method of improving the performance of CV-MDI-QKD involving the achievable secret key rate and transmission distance by using zero-photon catalysis (ZPC), which is indeed a noiseless attenuation process. The numerical simulation results show that the transmission distance of ZPC-based CV-MDI-QKD under the extreme asymmetric case is better than that of the original protocol. Attractively, in contrast to the previous single-photon subtraction (SPS)-based CV-MDI-QKD, the proposed scheme enables a higher secret key rate and a longer transmission distance. In particular, the ZPC-based CV-MDI-QKD can tolerate more imperfections of detectors than both the original protocol and the SPS-based CV-MDI-QKD.
Multilingual emoji prediction using BERT for sentiment analysis

Purpose Recently, Unicode has been standardized with the penetration of social networking services, the use of emojis has become common. Emojis, as they are also known, are most effective in expressing emotions in sentences. Sentiment analysis in natural language processing manually labels emotions for sentences. The authors can predict sentiment using emoji of text posted on social media without labeling manually. The purpose of this paper is to propose a new model that learns from sentences using emojis as labels, collecting English and Japanese tweets from Twitter as the corpus. The authors verify and compare multiple models based on attention long short-term memory (LSTM) and convolutional neural networks (CNN) and Bidirectional Encoder Representations from Transformers (BERT). Design/methodology/approach The authors collected 2,661 kinds of emoji registered as Unicode characters from tweets using Twitter application programming interface. It is a total of 6,149,410 tweets in Japanese. First, the authors visualized a vector space produced by the emojis by Word2Vec. In addition, the authors found that emojis and similar meaning words of emojis are adjacent and verify that emoji can be used for sentiment analysis. Second, it involves entering a line of tweets containing emojis, learning and testing with that emoji as a label. The authors compared the BERT model with the conventional models [CNN, FastText and Attention bidirectional long short-term memory (BiLSTM)] that were high scores in the previous study. Findings Visualized the vector space of Word2Vec, the authors found that emojis and similar meaning words of emojis are adjacent and verify that emoji can be used for sentiment analysis. The authors obtained a higher score with BERT models compared to the conventional model. Therefore, the sophisticated experiments demonstrate that they improved the score over the conventional model in two languages. General emoji prediction is greatly influenced by context. In addition, the score may be lowered due to a misunderstanding of meaning. By using BERT based on a bi-directional transformer, the authors can consider the context. Practical implications The authors can find emoji in the output words by typing a word using an input method editor (IME). The current IME only considers the most latest inputted word, although it is possible to recommend emojis considering the context of the inputted sentence in this study. Therefore, the research can be used to improve IME performance in the future. Originality/value In the paper, the authors focus on multilingual emoji prediction. This is the first attempt of comparison at emoji prediction between Japanese and English. In addition, it is also the first attempt to use the BERT model based on the transformer for predicting limited emojis although the transformer is known to be effective for various NLP tasks. The authors found that a bidirectional transformer is suitable for emoji prediction.
Empty Cities: A Dynamic-Object-Invariant Space for Visual SLAM

In this paper we present a data-driven approach to obtain the static image of a scene, eliminating dynamic objects that might have been present at the time of traversing the scene with a camera. The general objective is to improve vision-based localization and mapping tasks in dynamic environments, where the presence (or absence) of different dynamic objects in different moments makes these tasks less robust. We introduce an end-to-end deep learning framework to turn images of an urban environment that include dynamic content, such as vehicles or pedestrians, into realistic static frames suitable for localization and mapping. This objective faces two main challenges: detecting the dynamic objects, and inpainting the static occluded back-ground. The first challenge is addressed by the use of a convolutional network that learns a multi-class semantic segmentation of the image. The second challenge is approached with a generative adversarial model that, taking as input the original dynamic image and the computed dynamic/static binary mask, is capable of generating the final static image. This framework makes use of two new losses, one based on image steganalysis techniques, useful to improve the inpainting quality, and another one based on ORB features, designed to enhance feature matching between real and hallucinated image regions. To validate our approach, we perform an extensive evaluation on different tasks that are affected by dynamic entities, i.e., visual odometry, place recognition and multi-view stereo, with the hallucinated images. Code has been made available on https://github.com/bertabescos/EmptyCities_SLAM.
Convergence of Gaussian Process Regression with Estimated Hyper-Parameters and Applications in Bayesian Inverse Problems

This work is concerned with the convergence of Gaussian process regression. A particular focus is on hierarchical Gaussian process regression, where hyper-parameters appearing in the mean and covariance structure of the Gaussian process emulator are a-priori unknown and are learned from the data, along with the posterior mean and covariance. We work in the framework of empirical Bayes, where a point estimate of the hyper-parameters is computed, using the data, and then used within the standard Gaussian process prior to posterior update. We provide a convergence analysis that (i) holds for a given, deterministic function $f$ to be emulated, and (ii) shows that convergence of Gaussian process regression is unaffected by the additional learning of hyper-parameters from data and is guaranteed in a wide range of scenarios. As the primary motivation for the work is the use of Gaussian process regression to approximate the data likelihood in Bayesian inverse problems, we provide a bound on the error introduced in the Bayesian posterior distribution in this context.