Reviewerly/exHarmony huggingface dataset:
Inhomogeneous driving in quantum annealers can result in orders-of-magnitude improvements in performance

Abstract Quantum annealers are special-purpose quantum computers that primarily target solving Ising optimization problems. Theoretical work has predicted that the probability of a quantum annealer ending in a ground state can be dramatically improved if the spin driving terms, which play a crucial role in the functioning of a quantum annealer, have different strengths for different spins; that is, they are inhomogeneous. In this paper we describe a time-shift-based protocol for inhomogeneous driving and demonstrate, using an experimental quantum annealer, the performance of our protocol on a range of hard Ising problems that have been well-studied in the literature. Compared to the homogeneous-driving case, we find that we are able to improve the probability of finding a ground state by up to 10 7 × for Weak–Strong–Cluster problem instances, and by up to 10 3 × for more general spin-glass problem instances. In addition to being of practical interest as a heuristic speedup method, inhomogeneous driving may also serve as a useful tool for investigations into the physics of experimental quantum annealers.
Graph Neural Networks for Image Understanding Based on Multiple Cues: Group Emotion Recognition and Event Recognition as Use Cases

A graph neural network (GNN) for image understanding based on multiple cues is proposed in this paper. Compared to traditional feature and decision fusion approaches that neglect the fact that features can interact and exchange information, the proposed GNN is able to pass information among features extracted from different models. Two image understanding tasks, namely group-level emotion recognition (GER) and event recognition, which are highly semantic and require the interaction of several deep models to synthesize multiple cues, were selected to validate the performance of the proposed method. It is shown through experiments that the proposed method achieves state-of-the-art performance on the selected image understanding tasks. In addition, a new group-level emotion recognition database is introduced and shared in this paper.
The application of artificial intelligence in the internet of things

At present, the concepts of artificial intelligence (AI) and robots are widely known by people and artificial intelligence brings great convenience for people's life and study. With the progress of science and technology, artificial intelligence has become a popular development trend. At the same time, internet is also gradually publicized, which drives the rapid development of all walks of life. Under this situation, the internet of things is born and its driving role in China's economic development is continuously highlighted, which promotes the effective development of Chinese economy. In the essay, the comprehensive studies are conducted based on the different application scenarios of artificial intelligence in the internet of things, like autonomous vehicles, smart home, computer network, robot and so on, and its application prospect is discussed.
Efficient Video Semantic Segmentation with Labels Propagation and Refinement

This paper tackles the problem of real-time semantic segmentation of high definition videos using a hybrid GPU-CPU approach. We propose an Efficient Video Segmentation (EVS) pipeline that combines:(i)On the CPU, a very fast optical flow method, that is used to exploit the temporal aspect of the video and propagate semantic information from one frame to the next. It runs in parallel with the GPU.(ii)On the GPU, two Convolutional Neural Networks: A main segmentation network that is used to predict dense semantic labels from scratch, and a Refiner that is designed to improve predictions from previous frames with the help of a fast Inconsistencies Attention Module (IAM). The latter can identify regions that cannot be propagated accurately.We suggest several operating points depending on the desired frame rate and accuracy. Our pipeline achieves accuracy levels competitive to the existing real-time methods for semantic image segmentation (mIoU above 60%), while achieving much higher frame rates. On the popular Cityscapes dataset with high resolution frames (2048 × 1024), the proposed operating points range from 80 to 1000 Hz on a single GPU and CPU.
Canis: A High‐Level Language for Data‐Driven Chart Animations

Abstract In this paper, we introduce Canis, a high‐level domain‐specific language that enables declarative specifications of data‐driven chart animations. By leveraging data‐enriched SVG charts, its grammar of animations can be applied to the charts created by existing chart construction tools. With Canis, designers can select marks from the charts, partition the selected marks into mark units based on data attributes, and apply animation effects to the mark units, with the control of when the effects start. The Canis compiler automatically synthesizes the Lottie animation JSON files [Aira], which can be rendered natively across multiple platforms. To demonstrate Canis’ expressiveness, we present a wide range of chart animations. We also evaluate its scalability by showing the effectiveness of our compiler in reducing the output specification size and comparing its performance on different platforms against D3.
A Jaya algorithm based wrapper method for optimal feature selection in supervised classification

In recent years, Jaya optimization algorithm has been successfully applied in several optimization problems. This paper presents a novel feature selection (FS) approach based on Jaya optimization algorithm (FSJaya) along with supervised machine learning techniques to select the optimal features. This approach uses a search technique to find the best suitable features by updating the worst features to reduce the dimensions of the feature space. This improves the performance of supervised machine learning techniques. The effectiveness of the proposed approach is evaluated for ten benchmark datasets and compared with several FS approaches such as FS using genetic algorithm (FSGA), FS using particle swarm optimization algorithm (FSPSO), and FS using differential evolutionary (FSDE). The experimental result has shown that the average classification accuracy of FSJaya on most of the datasets is superior over the existing methods such as FSGA, FSPSO, and FSDE. The proof of statistical significance of the proposed approach has been validated by using Friedman and Holm test. This proposed approach is found efficient in selecting an optimal subset of features as compared to other counterparts.
Proxemics Beyond Proximity

Cross-device interactions enable ad hoc sharing of content and control in co-located collaboration. Cross-device research often draws from proxemics theory for designing interactions based on detection of spatial relations such as distance and orientation between people and devices. However, detection of human-human or human-device proximity also constrains flexibility in co-located social interaction. We suggest a proxemics-based approach to designing flexible cross-device interactions. From observations in a field study, we articulate how co-located sharing practices are shaped by the interplay between everyday mobile devices and the physical environment. Based on these insights, we present three cross-device prototypes as proofs-of-concept, demonstrating three design sensitivities for considering proxemics beyond proximity; incorporating features in the environment, enabling flexibility in interpersonal distance and orientation, and providing multiple alternative action possibilities. Drawing from characteristics of our prototypes, we discuss concrete proposals for designing cross-device interactions to enable flexible social interaction.
Integrating Co-Clustering and Interpretable Machine Learning for the Prediction of Intravenous Immunoglobulin Resistance in Kawasaki Disease

Identifying intravenous immunoglobulin-resistant patients is essential for the prompt and optimal treatment of Kawasaki disease, suggesting the need for effective risk assessment tools. Data-driven approaches have the potential to identify the high-risk individuals by capturing the complex patterns of real-world data. To enable clinically applicable prediction of intravenous immunoglobulin resistance addressing the incompleteness of clinical data and the lack of interpretability of machine learning models, a multi-stage method is developed by integrating data missing pattern mining and intelligible models. First, co-clustering is adopted to characterize the block-wise data missing patterns by simultaneously grouping the clinical features and patients to enable (a) group-based feature selection and missing data imputation and (b) patient subgroup-specific predictive models considering the availability of data. Second, feature selection is performed using the group Lasso to uncover group-specific risk factors. Third, the Explainable Boosting Machine, which is an interpretable learning method based on generalized additive models, is applied for the prediction of each patient subgroup. The experiments using real-world Electronic Health Records demonstrate the superior performance of the proposed framework for predictive modeling compared with a set of benchmark methods. This study highlights the integration of co-clustering and supervised learning methods for incomplete clinical data mining, and promotes data-driven approaches to investigate predictors and effective algorithms for decision making in healthcare.
An Improved Approach for Sentiment Analysis of Arabic Tweets in Twitter Social Media

Recently, sentiment analysis of social media contents is very important for opinion mining in several applications and different fields. Arabic sentiment analysis is one of the more complicated sentiment analysis tools of social media due to the informal noisy contents and the rich morphology of Arabic language. There is a number of works has been proposed for Arabic sentiment analysis. However, these works need an improvement in terms of effectiveness and accuracy. Consequently, in this paper, a corpus-based approach is proposed for Arabic sentiment analysis of tweets annotated as either negative or positive in twitter social media. The approach is based on a Discriminative multinomial naïve Bayes (DMNB) method with N-grams tokenizer, stemming, and term frequency-inverse document frequency (TF-IDF) techniques. The experiments are conducted using a set of performance evaluation metrics on a public twitter dataset to test the proposed sentiment analysis approach. Experimental results demonstrated the usefulness of the proposed approach. Furthermore, the comparison results showed that the approach outperformed the related work and improved the accuracy with 0.3%.
On the number of roots of some linearized polynomials

Linearized polynomials appear in many different contexts, such as rank metric codes, cryptography and linear sets, and the main issue regards the characterization of the number of roots from their coefficients. Results of this type have been already proved in [7], [10], [24]. In this paper we provide bounds and characterizations on the number of roots of linearized polynomials of this formax+b0xqs+b1xqs+n+b2xqs+2n+…+bt−1xqs+n(t−1)∈Fqnt[x], with gcd⁡(s,n)=1. Also, we characterize the number of roots of such polynomials directly from their coefficients, dealing with matrices which are much smaller than the relative Dickson matrices and the companion matrices used in the previous papers. Furthermore, we develop a method to find explicitly the roots of a such polynomial by finding the roots of a qn-polynomial. Finally, as an application of the above results, we present a family of linear sets of the projective line whose points have a small spectrum of possible weights, containing most of the known families of scattered linear sets. In particular, we carefully study the linear sets in PG(1,q6) presented in [9].
Improving Humans' Ability to Interpret Deictic Gestures in Virtual Reality

Collaborative Virtual Environments (CVEs) offer unique opportunities for human communication. Humans can interact with each other over a distance in any environment and visual embodiment they want. Although deictic gestures are especially important as they can guide other humans' attention, humans make systematic errors when using and interpreting them. Recent work suggests that the interpretation of vertical deictic gestures can be significantly improved by warping the pointing arm. In this paper, we extend previous work by showing that models enable to also improve the interpretation of deictic gestures at targets all around the user. Through a study with 28 participants in a CVE, we analyzed the errors users make when interpreting deictic gestures. We derived a model that rotates the arm of a pointing user's avatar to improve the observing users' accuracy. A second study with 24 participants shows that we can improve observers' accuracy by 22.9%. As our approach is not noticeable for users, it improves their accuracy without requiring them to learn a new interaction technique or distracting from the experience.
SemiText: Scene text detection with semi-supervised learning

Scene text detection is an important step of scene text recognition and has achieved significant progress. However, the requirement of large amounts of annotated training data, which is used for training text detection model, has become a great challenge for existing methods. In this paper, we propose a semi-supervised scene text detection framework (SemiText), which trains robust and accurate scene text detectors using a pre-trained supervised model and the unannotated data. With a pre-trained model that is pre-trained on the fully annotated synthetic dataset, i.e., SynthText, we investigate the inductive and transductive semi-supervised learning on the unannotated dataset respectively. For inductive learning, the pre-trained model is applied to the unannotated training dataset to search for more training examples, which are further combined with SynthText to fine-tune the pre-trained model and achieve a superior detection model. For transductive learning, the unannotated training dataset is replaced with the unannotated test dataset. Meanwhile, for the aim of real-world applications, we adopt Mask R-CNN to detect text with arbitrary shapes and exploit context information to suppress false positives. Extensive experiments on different datasets show that the performance of our text detection method can be clearly improved under both inductive and transductive semi-supervision. Additionally, we also achieve state-of-the-art performance under full supervision.
Investigating User Expectations on the Roles of Family-shared AI Speakers

AI assistants that use a voice user interface (VUI), such as AI speakers, have become popular in family homes. However, it is still unclear what roles the AI speaker can support within the family unit. We investigated the roles of an AI speaker as a family-shared technology. By conducting a one-week participatory user study, we discovered that family members' co-ownership toward the AI speaker was the key in the development of its family-oriented roles. Our findings showed seven domains of user expectations on these roles, and we realized that all the expectations can be represented as family cohesion. In addition, privacy awareness was emphasized regarding personal supports. Finally, we discuss a new perspective for AI speaker design and offer two suggestions: 1) leveraging human-likeness to develop its potential roles of supporting the unit of a family and 2) interpreting the home context to seamlessly connect family and personal supporting roles.
The effect of using augmented reality and sensing technology to teach magnetism in high school physics

The purpose of this study is to investigate the effect of MagAR, an instructional material for teaching magnetism using augmented reality and sensing technology, on students’ academic achievement and learning process, and to identify students’ views about augmented reality. An embedded mixed-method approach was employed in this study. The study’s results suggest that AR learning environments are effective in teaching physics, and facilitate learning by adding visual and textual components to the learning process. In learning activities integrated with AR, the students were observed to participate more, appeared more comfortable, were able to answer questions related to the subject more easily, had increased self-confidence and exhibited higher academic achievement levels in physics. The results suggest that AR should not be considered as an independent learning environment for the teaching of physics, but would be more effective as supplementary to the laboratory environment.
Optimization of drop ejection frequency in EHD inkjet printing system using an improved Firefly Algorithm

Swarm intelligence approaches have been used to solve various optimization real-world applications in recent years. Firefly Algorithm (FA) is one of the popular stochastic swarm intelligence paradigms developed in the recent past. In order to solve the slow convergence speed of the standard FA, a new improved Firefly Algorithm (iFA) is proposed in this research. Instead of keeping a constant initial brightness coefficient, a new rule has been proposed for updating the brightness of the fireflies based on a selection probability during generations which leads to a better balance between exploration and exploitation. The efficiency of the iFA has been tested by solving benchmark mathematical functions as well as in a real-world engineering problems. In order to comprehensively compare the performance of the iFA, several other metaheuristic algorithms were used for solving the same benchmark functions and the real-world problems. The iFA shows its superiority in almost all the cases for finding better optima in terms of the objective function value. The droplet ejection speed of an electrohydrodynamic inkjet printing system has been significantly improved by the iFA, which hence fully demonstrates its potential to solve real-world problems. Additionally, the iFA proves its efficiency in solving some challenging, classic engineering design problems with unknown search space. The source codes of the iFA are publiclyavailable at https://www.amitball.com/projects/iFA.
Maintenance with Augmented Reality Remote Support in Comparison to Paper-Based Instructions: Experiment and Analysis

Basic repair and maintenance tasks of industrial machines are usually performed by local service personnel at the location of the component to be maintained. Complex maintenance tasks require an expert who travels to the customer to solve a specific problem. This process is time and cost intensive. Augmented Reality (AR) Remote Assistance offers means to support newcomers on site by guiding personnel through the maintenance process with novel visualizations. This paper describes an Augmented Reality based remote assistance tool for industrial purposes. We present the technology and our implementation approach, as well as the challenges and limitations we have encountered. The potential of the tool was evaluated and analyzed in an industry oriented use case for repairing an industrial PC. The results were compared with a maintenance procedure that was carried out using standard paper repair instructions. Our approach enabled the users to perform complex tasks faster and with fewer errors. Overall, the error rate decreased significantly due to the AR-based remote support. We also found that paper-based instructions are still more efficient in solving simple tasks.
Cryptϵ

Differential privacy (DP) is currently the de-facto standard for achieving privacy in data analysis, which is typically implemented either in the "central" or "local" model. The local model has been more popular for commercial deployments as it does not require a trusted data collector. This increased privacy, however, comes at the cost of utility and algorithmic expressibility as compared to the central model. In this work, we propose, Cryptε, a system and programming framework that (1) achieves the accuracy guarantees and algorithmic expressibility of the central model (2) without any trusted data collector like in the local model. Cryptε achieves the "best of both worlds" by employing two non-colluding untrusted servers that run DP programs on encrypted data from the data owners. In theory, straightforward implementations of DP programs using off-the-shelf secure multi-party computation tools can achieve the above goal. However, in practice, they are beset with many challenges like poor performance and tricky security proofs. To this end, Cryptε allows data analysts to author logical DP programs that are automatically translated to secure protocols that work on encrypted data. These protocols ensure that the untrusted servers learn nothing more than the noisy outputs, thereby guaranteeing DP (for computationally bounded adversaries) for all Cryptε programs. Cryptε supports a rich class of DP programs that can be expressed via a small set of transformation and measurement operators followed by arbitrary post-processing. Further, we propose performance optimizations leveraging the fact that the output is noisy. We demonstrate Cryptε's practical feasibility with extensive empirical evaluations on real world datasets.
Automatically Generating Data Exploration Sessions Using Deep Reinforcement Learning

Exploratory Data Analysis (EDA) is an essential yet highly demanding task. To get a head start before exploring a new dataset, data scientists often prefer to view existing EDA notebooks -- illustrative, curated exploratory sessions, on the same dataset, that were created by fellow data scientists who shared them online. Unfortunately, such notebooks are not always available (e.g., if the dataset is new or confidential). To address this, we present ATENA, a system that takes an input dataset and auto-generates a compelling exploratory session, presented in an EDA notebook. We shape EDA into a control problem, and devise a novel Deep Reinforcement Learning (DRL) architecture to effectively optimize the notebook generation. Though ATENA uses a limited set of EDA operations, our experiments show that it generates useful EDA notebooks, allowing users to gain actual insights.
Lexicon-Based Sentiment Convolutional Neural Networks for Online Review Analysis

With the growing availability and popularity of sentiment-rich resources like blogs and online reviews, new opportunities and challenges have emerged regarding the identification, extraction, and organization of sentiments from user-generated documents or sentences. Recently, many studies have exploited lexicon-based methods or supervised learning algorithms to conduct sentiment analysis tasks separately; however, the former approaches ignore contextual information of sentences and the latter ones do not take sentiment information embedded in sentiment words into consideration. To tackle these limitations, we propose a new model named Sentiment Convolutional Neural Network (SentiCNN) to analyze the sentiments of sentences with both contextual and sentiment information of sentiment words, in which, contextual information is captured from word embeddings and sentiment information is identified using existing lexicons. We incorporate a Highway Network into our model to adaptively combine sentiment and contextual information from sentences by strengthening the connection between features of both sentences and their sentiment words. Furthermore, we propose three lexicon-based attention mechanisms (LBAMs) for our SentiCNN model to find the most important indicators of sentiments and make predictions more effectively. Experiments over two well-known datasets indicate that sentiment words, the Highway Network, and LBAMs contribute to sentiment analysis.