Reviewerly/exHarmony huggingface dataset:
IBM’s Qiskit Tool Chain: Working with and Developing for Real Quantum Computers

Quantum computers promise substantial speedups over conventional machines for many practical applications. While considered "dreams of the future" for a long time, first quantum computers are available now which can be utilized by anyone. A leading force within this development is IBM Research which launched the IBM Q Experience - the first industrial initiative to build universal quantum computers and make them accessible to a broad audience through cloud access. Along this initiative, the tool Qiskit has been launched which enables researchers, teachers, developers, and general enthusiasts to write corresponding code and to run experiments on those machines. At the same time, this provides an ideal playground for the design automation community which - through Qiskit - can deploy improved solutions e.g. on designing and realizing quantum applications. This special session summary aims to provide an introduction into Qiskit and is showcasing selected success stories on how to work with and develop for it. In addition to that, it provides corresponding references to further readings in terms of tutorials and scientific papers as well as links to publicly available implementations for Qiskit extensions.
SocInf: Membership Inference Attacks on Social Media Health Data With Machine Learning

Social media networks have shown rapid growth in the past, and massive social data are generated which can reveal behavior or emotion propensities of users. Numerous social researchers leverage machine learning technology to build social media analytic models which can detect the abnormal behaviors or mental illnesses from the social media data effectively. Although the researchers only public the prediction interfaces of the machine learning models, in general, these interfaces may leak information about the individual data records on which the models were trained. Knowing a certain user's social media record was used to train a model can breach user privacy. In this paper, we present SocInf and focus on the fundamental problem known as membership inference. The key idea of SocInf is to construct a mimic model which has a similar prediction behavior with the public model, and then we can disclose the prediction differences between the training and testing data set by abusing the mimic model. With elaborated analytics on the predictions of the mimic model, SocInf can thus infer whether a given record is in the victim model's training set or not. We empirically evaluate the attack performance of SocInf on machine learning models trained by Xgboost, logistics, and online cloud platform. Using the realistic data, the experiment results show that SocInf can achieve an inference accuracy and precision of 73% and 84%, respectively, in average, and of 83% and 91% at best.
Sentiment based matrix factorization with reliability for recommendation

Recommender systems aim at predicting users’ preferences based on abundant information, such as user ratings, demographics, and reviews. Although reviews are sparser than ratings, they provide more detailed and reliable information about users’ true preferences. Currently, reviews are often used to improve the explainability of recommender systems. In this paper, we propose the sentiment based matrix factorization with reliability (SBMF+R) algorithm to leverage reviews for prediction. First, we develop a sentiment analysis approach using a new star-based dictionary construction technique to obtain the sentiment score. Second, we design a user reliability measure that combines user consistency and the feedback on reviews. Third, we incorporate the ratings, reviews, and feedback into a probabilistic matrix factorization framework for prediction. Experiments on eight Amazon datasets demonstrated that SBMF+R is more accurate than state-of-the-art algorithms.
AIR: Attentional Intention-Aware Recommender Systems

The capability of extracting sequential patterns from the user-item interaction data is now becoming a key feature of recommender systems. Though it is important to capture the sequential effect, existing methods only focus on modelling the sparse item-wise sequential effect in user preference and only consider the homogeneous user interaction behaviors (i.e., a single type of user behavior). As a result, the data sparsity issue inevitably arises and makes the learned sequential patterns fragile and unreliable, impeding the sequential recommendation performance of existing methods. Hence, in this paper, we propose AIR, namely attentional intention-aware recommender systems to predict category-wise future user intention and collectively exploit the rich heterogeneous user interaction behaviors (i.e., multiple types of user behaviors). In AIR, we propose to represent user intention as an action-category tuple to discover category-wise sequential patterns and to capture varied effect of different types of actions for recommendation. A novel attentional recurrent neural network (ARNN) is proposed to model the intention migration effect and infer users' future intention. Besides, an intention-aware factorization machine (ITFM) is developed to perform intention-aware sequential recommendation. Experiments on two real-life datasets demonstrate the superiority and practicality of AIR in sequential top-k recommendation tasks.
Detecting Anomalies in Space using Multivariate Convolutional LSTM with Mixtures of Probabilistic PCA

Detecting an anomaly is not only important for many terrestrial applications on Earth but also for space applications. Especially, satellite missions are highly risky because unexpected hardware and software failures can occur due to sudden or unforeseen space environment changes. Anomaly detection and spacecraft health monitoring systems have heavily relied on human expertise to investigate whether they are a true anomaly or not. Also, it is practically infeasible to produce labels on data due to the enormous amount of telemetries generated from a satellite. In this work, we propose a data-driven anomaly detection algorithm for Korea Multi-Purpose Satellite 2 (KOMPSAT-2). We develop a Multivariate Convolution LSTM with Mixtures of Probabilistic Principal Component Analyzers, where our approach uses both neural networks and probabilistic clustering to improve the anomaly detection performance. We evaluated our approach with a total of 22 million telemetry samples collected for 10 months from KOMPSAT-2. We also compare our approach with other state-of-the-art approaches. We show that our proposed approach is 35.8% better in precision, and 18.2% better in F-1 score than the best baseline approach. We plan to deploy our algorithm in the second half of 2019 to actually apply real operation of KOMPSAT-2.
Buying or Browsing?: Predicting Real-time Purchasing Intent using Attention-based Deep Network with Multiple Behavior

E-commerce platforms are becoming a primary place for people to find, compare and ultimately purchase products. One of the fundamental questions that arises in e-commerce is to predict user purchasing intent, which is an important part of user understanding and allows for providing better services for both sellers and customers. However, previous work cannot predict real-time user purchasing intent with a high accuracy, limited by the representation capability of traditional browse-interactive behavior adopted. In this paper, we propose a novel end-to-end deep network, named Deep Intent Prediction Network (DIPN), to predict real-time user purchasing intent. In particular, besides the traditional browse-interactive behavior, we collect a new type of user interactive behavior, called touch-interactive behavior, which can capture more fine-grained real-time user features. To combine these behavior effectively, we propose a hierarchical attention mechanism, where the bottom attention layer focuses on the inner parts of each behavior sequence while the top attention layer learns the inter-view relations between different behavior sequences. In addition, we propose to train DIPN with multi-task learning to better distinguish user behavior patterns. In the experiments conducted on a large-scale industrial dataset, DIPN significantly outperforms the baseline solutions. Notably, DIPN gains about 18.96% improvement on AUC than the state-of-the-art solution only using traditional browse-interactive behavior sequences. Moreover, DIPN has been deployed in the operational system of Taobao. Online A/B testing results with more than 12.9 millions of users reveal the potential of knowing users' real-time purchasing intent.
Age-Related Differences With Immersive and Non-immersive Virtual Reality in Memory Assessment

Memory decline associated with physiological aging and age-related neurological disorders has a direct impact on quality of life for seniors. With demographic aging, the assessment of cognitive functions is gaining importance, as early diagnosis can lead to more effective cognitive interventions. In comparison to classic paper-and-pencil approaches, virtual reality (VR) could offer an ecologically valid environment for assessment and remediation of cognitive deficits. Despite the rapid development and application of new technologies, the results of studies aimed at the role of VR immersion in assessing cognitive performance and the use of VR in aging populations are often ambiguous. VR can be presented in a less immersive form, with a desktop platform, or with more advanced technologies like head-mounted displays (HMDs). Both these VR platforms are associated with certain advantages and disadvantages. In this study, we investigated age-related differences related to the use of desktop and HMD platforms during memory assessment using an intra-subject design. Groups of seniors (N = 36) and young adults (N = 25) completed a virtual Supermarket Shopping task using desktop and HMD platforms in a counterbalanced order. Our results show that the senior performances were superior when using the non-immersive desktop platform. The ability to recall a shopping list in the young adult group remained stable regardless of the platform used. With the HMD platform, the performance of the subjects of both groups seemed to be more influenced by fatigue. The evaluated user experiences did not differ between the two platforms, and only minimal and rare side effects were reported by seniors. This implies that highly immersive technology has good acceptance among aging adults. These findings might have implications for the further use of HMD in cognitive assessment and remediation.
Unscented Kalman filter state estimation for manipulating unmanned aerial vehicles

Manipulating unmanned aerial vehicles (MUAVs) are aerial robots equipped with a mechanism to physically interact with the environment. State estimation of such robots is a challenging problem due to inherent couplings, nonlinearities and uncertainties of MUAV complex dynamics and, therefore, popular algorithms such as extended Kalman filter may not be applicable. With the above considerations, this paper formulates two variants of the unscented Kalman filter using (i) general and (ii) spherical unscented transform to address state estimation problem in MUAVs. In order to examine the effect of estimation quality on overall control performance, first the coupled dynamics of a quadcopter endowed with a robotic manipulator is presented. Next, a linear–quadratic–Gaussian (LQG) control is designed to achieve simultaneous control of the quadcopter and its manipulator. Then, the performance of each unscented Kalman filter algorithm is compared with that of extended Kalman filter in the context of estimation accuracy, overall control performance, and algorithm execution time. Additionally, sensitivity of the proposed approaches to increasing noise levels and total loss of sensory data are examined.
Public Service Chatbots: Automating Conversation with BBC News

Automation of journalistic tasks is growing with the development of increasingly sophisticated software for newsgathering, production, and distribution. Bots are one form of algorithmic technology that has found a place in the modern newsroom, with chatbots leading the way as news organisations seek to attract new audiences using conversational forms of journalism. Recent advances in artificial intelligence (AI) and machine learning (ML) have fuelled increasing experimentation with machine autonomy and there has been much hyperbole in the press about the extent and impact of this on journalism. Looking at on-the-ground trials in audience-facing bots at the UK's largest public broadcaster, we find a significantly more restricted picture. News bots at The BBC to-date have been basic, do not use ML, and have rarely been integrated into news production. The organisation is laying groundwork for development of more interactive news formats with an increasingly conversational tone and individual mode of address as part of a strategy for increased personalisation, which is likely to involve growing levels of ML. In the process, bots are reconfiguring working practices and infrastructure, posing new editorial and technical challenges, and redefining relationships with audiences. We discuss the implications of this for public service media.
Current state of the art in the use of augmented reality in dentistry: a systematic review of the literature

The aim of the present systematic review was to screen the literature and to describe current applications of augmented reality.The protocol design was structured according to PRISMA-P guidelines and registered in PROSPERO. A review of the following databases was carried out: Medline, Ovid, Embase, Cochrane Library, Google Scholar and the Gray literature. Data was extracted, summarized and collected for qualitative analysis and evaluated for individual risk of bias (R.O.B.) assessment, by two independent examiners. Collected data included: year of publishing, journal with reviewing system and impact factor, study design, sample size, target of the study, hardware(s) and software(s) used or custom developed, primary outcomes, field of interest and quantification of the displacement error and timing measurements, when available. Qualitative evidence synthesis refers to SPIDER.From a primary research of 17,652 articles, 33 were considered in the review for qualitative synthesis. 16 among selected articles were eligible for quantitative synthesis of heterogenous data, 12 out of 13 judged the precision at least as acceptable, while 3 out of 6 described an increase in operation timing of about 1 h. 60% (n = 20) of selected studies refers to a camera-display augmented reality system while 21% (n = 7) refers to a head-mounted system. The software proposed in the articles were self-developed by 7 authors while the majority proposed commercially available ones. The applications proposed for augmented reality are: Oral and maxillo-facial surgery (OMS) in 21 studies, restorative dentistry in 5 studies, educational purposes in 4 studies and orthodontics in 1 study. The majority of the studies were carried on phantoms (51%) and those on patients were 11 (33%).On the base of literature the current development is still insufficient for full validation process, however independent sources of customized software for augmented reality seems promising to help routinely procedures, complicate or specific interventions, education and learning. Oral and maxillofacial area is predominant, the results in precision are promising, while timing is still very controversial since some authors describe longer preparation time when using augmented reality up to 60 min while others describe a reduced operating time of 50/100%.The following systematic review was registered in PROSPERO with RN: CRD42019120058.
A Novel Debt-Credit Mechanism for Blockchain-Based Data-Trading in Internet of Vehicles

With the advancement and emergence of diverse network services in Internet of Vehicles (IoV), large volume of data are collected and stored, making data important properties. Data will be one of the most important commodities in the future blockchain-based IoV systems. However, efficiency challenges have been commonly found in blockchain-based data markets, which is mainly caused by transaction confirmation delays and the cold-start problems for new users. To address the efficiency challenges, we propose a secure, decentralized IoV data-trading system by exploiting the blockchain technology, and design an efficient debt-credit mechamism to support efficient data-trading in IoV. In the debt-credit mechanism, a vehicle with loan demand could loan from multivehicles by promising to pay interest and reward. In particular, we encourage loaning among vehicles by a motivation-based investing and pricing mechanism. We formulate a two-stage Stackelberg game to maximize the profits of borrower vehicle and lender vehicles jointly. In the first stage, the borrower vehicle set the interest rate and reward for the loan as its pricing strategies. In the second stage, the lender vehicles decide on their investing strategies. We apply backward induction to analyze the subgame perfect equilibrium at each stage for both independent and uniform pricing schemes. We also validate the existence and uniqueness of Stackelberg equilibrium. The numerical results illustrate the efficiency of the proposed pricing schemes.
Handwriting Recognition in Low-Resource Scripts Using Adversarial Learning

Handwritten Word Recognition and Spotting is a challenging field dealing with handwritten text possessing irregular and complex shapes. The design of deep neural network models makes it necessary to extend training datasets in order to introduce variations and increase the number of samples; word-retrieval is therefore very difficult in low-resource scripts. Much of the existing literature comprises preprocessing strategies which are seldom sufficient to cover all possible variations. We propose an Adversarial Feature Deformation Module (AFDM) that learns ways to elastically warp extracted features in a scalable manner. The AFDM is inserted between intermediate layers and trained alternatively with the original framework, boosting its capability to better learn highly informative features rather than trivial ones. We test our meta-framework, which is built on top of popular word-spotting and word-recognition frameworks and enhanced by AFDM, not only on extensive Latin word datasets but also on sparser Indic scripts. We record results for varying sizes of training data, and observe that our enhanced network generalizes much better in the low-data regime; the overall word-error rates and mAP scores are observed to improve as well.
Global Explanations of Neural Networks

A barrier to the wider adoption of neural networks is their lack of interpretability. While local explanation methods exist for one prediction, most global attributions still reduce neural network decisions to a single set of features. In response, we present an approach for generating global attributions called GAM, which explains the landscape of neural network predictions across subpopulations. GAM augments global explanations with the proportion of samples that each attribution best explains and specifies which samples are described by each attribution. Global explanations also have tunable granularity to detect more or fewer subpopulations. We demonstrate that GAM's global explanations 1) yield the known feature importances of simulated data, 2) match feature weights of interpretable statistical models on real data, and 3) are intuitive to practitioners through user studies. With more transparent predictions, GAM can help ensure neural network decisions are generated for the right reasons.
Troubling Trends in Machine Learning Scholarship

Flawed scholarship threatens to mislead the public and stymie future research by compromising ML’s intellectual foundations. Indeed, many of these problems have recurred cyclically throughout the history of AI and, more broadly, in scientific research. In 1976, Drew McDermott chastised the AI community for abandoning self-discipline, warning prophetically that "if we can’t criticize ourselves, someone else will save us the trouble." The current strength of machine learning owes to a large body of rigorous research to date, both theoretical and empirical. By promoting clear scientific thinking and communication, our community can sustain the trust and investment it currently enjoys.
Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-To-Image Translation

The applicability of computer vision to real paintings and artworks has been rarely investigated, even though a vast heritage would greatly benefit from techniques which can understand and process data from the artistic domain. This is partially due to the small amount of annotated artistic data, which is not even comparable to that of natural images captured by cameras. In this paper, we propose a semantic-aware architecture which can translate artworks to photo-realistic visualizations, thus reducing the gap between visual features of artistic and realistic data. Our architecture can generate natural images by retrieving and learning details from real photos through a similarity matching strategy which leverages a weakly-supervised semantic understanding of the scene. Experimental results show that the proposed technique leads to increased realism and to a reduction in domain shift, which improves the performance of pre-trained architectures for classification, detection, and segmentation. Code is publicly available at: https://github.com/aimagelab/art2real.
AutoCross

Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. In this paper, we present AutoCross, an automatic feature crossing tool provided by 4Paradigm to its customers, ranging from banks, hospitals, to Internet corporations. By performing beam search in a tree-structured space, AutoCross enables efficient generation of high-order cross features, which is not yet visited by existing works. Additionally, we propose successive mini-batch gradient descent and multi-granularity discretization to further improve efficiency and effectiveness, while ensuring simplicity so that no machine learning expertise or tedious hyper-parameter tuning is required. Furthermore, the algorithms are designed to reduce the computational, transmitting, and storage costs involved in distributed computing. Experimental results on both benchmark and real-world business datasets demonstrate the effectiveness and efficiency of AutoCross. It is shown that AutoCross can significantly enhance the performance of both linear and deep models.
Improving Aspect Term Extraction With Bidirectional Dependency Tree Representation

Aspect term extraction is one of the important subtasks in aspect-based sentiment analysis. Previous studies have shown that using dependency tree structure representation is promising for this task. However, most dependency tree structures involve only one directional propagation on the dependency tree. In this paper, we first propose a novel bidirectional dependency tree network to extract dependency structure features from the given sentences. The key idea is to explicitly incorporate both representations gained separately from the bottom-up and top-down propagation on the given dependency syntactic tree. An end-to-end framework is then developed to integrate the embedded representations and BiLSTM plus CRF to learn both tree-structured and sequential features to solve the aspect term extraction problem. Experimental results demonstrate that the proposed model outperforms state-of-the-art baseline models on four benchmark SemEval datasets.