Reviewerly/exHarmony huggingface dataset:
3D printing in preschool music education: Opportunities and challenges

In this paper we explore the potential of 3D printing in the context of preschool music learning, with the aim of making visible its educational value. After analyzing embodied and enactive approaches in preschool and the adoption of tangible interfaces for music education, we review novel technologies in the field of additive fabrication and 3D printing and highlight their potential for music learning. Subsequently, we propose didactic experiences based on a manipulative approach and using already-available or user-customized 3D models. Finally, we present a case study that investigates alternative forms of music notation.
Transfer learning

In machine learning, transfer learning is concerned with utilising prior knowledge as a way to improve the process of training a new model in a different, but related, domain. Transfer learning has been shown to be beneficial across a large set of problems. One of the main questions any transfer learning approach must address is "What to transfer?". This paper proposes a new transfer learning method in genetic programming (GP) to improve solving symbolic regression problems by extracting all potentially good and unique building blocks from a source problem. The proposed method is compared against standard GP and a state-of-the-art GP method on ten regression datasets. The experimental results show that the proposed method has achieved significantly better or comparable performance to that of the competitive methods. Furthermore, the proposed method shows better initial population and convergence compared to the other methods.
Why Ballot Secrecy Still Matters

Human-rights declarations and various legal instruments state that elections must be conducted by secret ballot, but are silent about precisely what that means. This paper discusses two situations in long-established democracies—the 2018 Swedish general elections and the 2017 Australian "plebisurvey" on same-sex marriage—in which recognized principles of electoral secrecy were clearly violated. It then identifies the main objectives of ballot secrecy (protecting the privacy of personal political beliefs, discouraging coercion, and preventing vote-buying) and the main measures needed to ensure them. Finally, it concludes that "secrecy" cannot be measured on a unidimensional scale.
Zero Shot Intent Classification Using Long-Short Term Memory Networks

We describe a zero shot approach to intent classification that allows for the identification of intents that were not present during training. Our approach makes use of a Long-short Term Memory neural network to encode user queries and intents and uses these encodings to score previously unseen intents based on their semantic similarity to the queries. We test our model on intent classification in a personal digital assistant and show an improvement of 15% over a strong baseline. We also investigate the effect of adding a few training samples for the previously unseen intents in a few shot learning setting and show improvements of up to 16% over the baseline method.
GECCO black-box optimization competitions

The GECCO Workshop on Real-Parameter Black-Box Optimization Benchmarking Series is a series of benchmarking workshops held every year since 2009 that evaluates the performance of new optimization algorithms.
Recommender Systems: A Healthy Obsession

We propose endurance sports as a rich and novel domain for recommender systems and machine learning research. As sports like marathon running, triathlons, and mountain biking become more and more popular among recreational athletes, there exists a growing opportunity to develop solutions to a number of interesting prediction, classification, and recommendation challenges, to better support the complex training and competition needs of athletes. Such solutions have the potential to improve the health and well-being of large populations of users, by promoting and optimising exercise as part of a productive and healthy lifestyle.
Deep Fully Connected Model for Collective Activity Recognition

Group activity recognition is a challenging task because there is an exponentially large number of semantic and geometrical relationships among individuals. This makes it difficult to model these interactions and merge them as a whole for group activity classification. In this paper, we propose a deep fully-connected model for group recognition, first we use the spatial-temporal model based on convolution neural network (CNN) and long short-term memory networks (LSTM) network to capture the dynamic features of each person. Then, we use the fully-connected conditional random field (FCCRF) to learn the interactions between people. Finally, with FCCRF potential functions we re-fine the activity recognition predicted by the spatial-temporal model. The experimental results on collective activity data-set and collective activity extended data-set show that our model improves recognition accuracy over baseline methods and gets competitive results in comparison to the state-of-the-art models.
Roles and jobs in the open research scholarly communications environment: analysing job descriptions to predict future trends

During the past two-decades academic libraries updated current staff job responsibilities or created brand new roles. This allowed them to adapt to scholarly communication developments and consequently enabled them to offer efficient services to their users. The global calls for openly accessible research results has shifted the institutional, national and international focus and their constant evolvement has required the creation of new research positions in academic libraries. This study reports on the findings of an analysis of job descriptions in the open research services as advertised by UK academic libraries.METHOD: From March 2015 to March 2017, job advertisements relating to open access, repositories and research data management were collected.RESULTS: The analysis of the data showed that the primary responsibilities of the open research support staff were: to ensure and facilitate compliance with funders’ open access policies, maintain the tools that enable compliance, create reports and collect statistics that measure compliance rates and commit to continuous liaising activities with research stakeholders.DISCUSSION: It is clear that the open research services is a complex environment, requiring a variety of general and subject specific skill sets, while often a role may involve more than one area of expertise.CONCLUSION: The results of this study could benefit prospective employees and universities that wish to embed open research skills in their curriculum.
How to End Password Reuse on the Web

We present a framework by which websites can coordinate to make it difficult for users to set similar passwords at these websites, in an effort to break the culture of password reuse on the web today.Though the design of such a framework is fraught with risks to users' security and privacy, we show that these risks can be effectively mitigated through careful scoping of the goals for such a framework and through principled design.At the core of our framework is a private set-membership-test protocol that enables one website to determine, upon a user setting a password for use at it, whether that user has already set a similar password at another participating website, but with neither side disclosing to the other the password(s) it employs in the protocol.Our framework then layers over this protocol a collection of techniques to mitigate the leakage necessitated by such a test.We verify via probabilistic model checking that these techniques are effective in maintaining account security, and since these mechanisms are consistent with common user experience today, our framework should be unobtrusive to users who do not reuse similar passwords across websites (e.g., due to having adopted a password manager).Through a working implementation of our framework and optimization of its parameters based on insights of how passwords tend to be reused, we show that our design can meet the scalability challenges facing such a service.
Capacity Control of ReLU Neural Networks by Basis-Path Norm

Recently, path norm was proposed as a new capacity measure for neural networks with Rectified Linear Unit (ReLU) activation function, which takes the rescaling-invariant property of ReLU into account. It has been shown that the generalization error bound in terms of the path norm explains the empirical generalization behaviors of the ReLU neural networks better than that of other capacity measures. Moreover, optimization algorithms which take path norm as the regularization term to the loss function, like Path-SGD, have been shown to achieve better generalization performance. However, the path norm counts the values of all paths, and hence the capacity measure based on path norm could be improperly influenced by the dependency among different paths. It is also known that each path of a ReLU network can be represented by a small group of linearly independent basis paths with multiplication and division operation, which indicates that the generalization behavior of the network only depends on only a few basis paths. Motivated by this, we propose a new norm Basis-path Norm based on a group of linearly independent paths to measure the capacity of neural networks more accurately. We establish a generalization error bound based on this basis path norm, and show it explains the generalization behaviors of ReLU networks more accurately than previous capacity measures via extensive experiments. In addition, we develop optimization algorithms which minimize the empirical risk regularized by the basis-path norm. Our experiments on benchmark datasets demonstrate that the proposed regularization method achieves clearly better performance on the test set than the previous regularization approaches.
Adapting

Kechen Qin, Cheng Li, Virgil Pavlu, Javed Aslam. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Understanding VAEs in Fisher-Shannon Plane

In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the uncertainty in specifying the outcome of given variables. These two quantities are complementary and are jointly applied to information behavior analysis in most cases. The uncertainty property in information asserts a fundamental trade-off between Fisher information and Shannon information, which enlightens us the relationship between the encoder and the decoder in variational auto-encoders (VAEs). In this paper, we investigate VAEs in the Fisher-Shannon plane, and demonstrate that the representation learning and the log-likelihood estimation are intrinsically related to these two information quantities. Through extensive qualitative and quantitative experiments, we provide with a better comprehension of VAEs in tasks such as high-resolution reconstruction, and representation learning in the perspective of Fisher information and Shannon information. We further propose a variant of VAEs, termed as Fisher auto-encoder (FAE), for practical needs to balance Fisher information and Shannon information. Our experimental results have demonstrated its promise in improving the reconstruction accuracy and avoiding the noninformative latent code as occurred in previous works.
Active Learning for Decision-Making from Imbalanced Observational Data

Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action $a$ to take for a target unit after observing its covariates $\tilde{x}$ and predicted outcomes $\hat{p}(\tilde{y} \mid \tilde{x}, a)$. An example case is personalized medicine and the decision of which treatment to give to a patient. A common problem when learning these models from observational data is imbalance, that is, difference in treated/control covariate distributions, which is known to increase the upper bound of the expected ITE estimation error. We propose to assess the decision-making reliability by estimating the ITE model's Type S error rate, which is the probability of the model inferring the sign of the treatment effect wrong. Furthermore, we use the estimated reliability as a criterion for active learning, in order to collect new (possibly expensive) observations, instead of making a forced choice based on unreliable predictions. We demonstrate the effectiveness of this decision-making aware active learning in two decision-making tasks: in simulated data with binary outcomes and in a medical dataset with synthetic and continuous treatment outcomes.
Towards a Unified View of AI Planning and Reactive Synthesis

Automated planning and reactive synthesis are well-established techniques for sequential decision making. In this paper we examine a collection of AI planning problems with temporally extended goals, specified in Linear Temporal Logic (LTL). We characterize these so-called LTL planning problems as two-player games and thereby establish their correspondence to reactive synthesis problems. This unifying view furthers our understanding of the relationship between plan and program synthesis, establishing complexity results for LTL planning tasks. Building on this correspondence, we identify restricted fragments of LTL for which plan synthesis can be realized more efficiently.
Method for coregistration of optical measurements of breast tissue with histopathology: the importance of accounting for tissue deformations

For the validation of optical diagnostic technologies, experimental results need to be benchmarked against the gold standard. Currently, the gold standard for tissue characterization is assessment of hematoxylin and eosin (H&amp;E)-stained sections by a pathologist. When processing tissue into H&amp;E sections, the shape of the tissue deforms with respect to the initial shape when it was optically measured. We demonstrate the importance of accounting for these tissue deformations when correlating optical measurement with routinely acquired histopathology. We propose a method to register the tissue in the H&amp;E sections to the optical measurements, which corrects for these tissue deformations. We compare the registered H&amp;E sections to H&amp;E sections that were registered with an algorithm that does not account for tissue deformations by evaluating both the shape and the composition of the tissue and using microcomputer tomography data as an independent measure. The proposed method, which did account for tissue deformations, was more accurate than the method that did not account for tissue deformations. These results emphasize the need for a registration method that accounts for tissue deformations, such as the method presented in this study, which can aid in validating optical techniques for clinical use.
Subgoal-Based Temporal Abstraction in Monte-Carlo Tree Search

We propose an approach to general subgoal-based temporal abstraction in MCTS. Our approach approximates a set of available macro-actions locally for each state only requiring a generative model and a subgoal predicate. For that, we modify the expansion step of MCTS to automatically discover and optimize macro-actions that lead to subgoals. We empirically evaluate the effectiveness, computational efficiency and robustness of our approach w.r.t. different parameter settings in two benchmark domains and compare the results to standard MCTS without temporal abstraction.
Learning to Draw Text in Natural Images with Conditional Adversarial Networks

In this work, we propose an entirely learning-based method to automatically synthesize text sequence in natural images leveraging conditional adversarial networks. As vanilla GANs are clumsy to capture structural text patterns, directly employing GANs for text image synthesis typically results in illegible images. Therefore, we design a two-stage architecture to generate repeated characters in images. Firstly, a character generator attempts to synthesize local character appearance independently, so that the legible characters in sequence can be obtained. To achieve style consistency of characters, we propose a novel style loss based on variance-minimization. Secondly, we design a pixel-manipulation word generator constrained by self-regularization, which learns to convert local characters to plausible word image. Experiments on SVHN dataset and ICDAR, IIIT5K datasets demonstrate our method is able to synthesize visually appealing text images. Besides, we also show the high-quality images synthesized by our method can be used to boost the performance of a scene text recognition algorithm.
Worst-Case Discriminative Feature Selection

Feature selection plays a critical role in data mining, driven by increasing feature dimensionality in target problems. In this paper, we propose a new criterion for discriminative feature selection, worst-case discriminative feature selection (WDFS). Unlike Fisher Score and other methods based on the discriminative criteria considering the overall (or average) separation of data, WDFS adopts a new perspective called worst-case view which arguably is more suitable for classification applications. Specifically, WDFS directly maximizes the ratio of the minimum of between-class variance of all class pairs over the maximum of within-class variance, and thus it duly considers the separation of all classes. Otherwise, we take a greedy strategy by finding one feature at a time, but it is very easy to implement. Moreover, we also utilize the correlation between features to help reduce the redundancy and extend WDFS to uncorrelated WDFS (UWDFS). To evaluate the effectiveness of the proposed algorithm, we conduct classification experiments on many real data sets. In the experiment, we respectively use the original features and the score vectors of features over all class pairs to calculate the correlation coefficients, and analyze the experimental results in these two ways. Experimental results demonstrate the effectiveness of WDFS and UWDFS.
Overfitting in Synthesis: Theory and Practice

In syntax-guided synthesis (SyGuS), a synthesizer's goal is to automatically generate a program belonging to a grammar of possible implementations that meets a logical specification. We investigate a common limitation across state-of-the-art SyGuS tools that perform counterexample-guided inductive synthesis (CEGIS). We empirically observe that as the expressiveness of the provided grammar increases, the performance of these tools degrades significantly. We claim that this degradation is not only due to a larger search space, but also due to overfitting. We formally define this phenomenon and prove no-free-lunch theorems for SyGuS, which reveal a fundamental tradeoff between synthesizer performance and grammar expressiveness. A standard approach to mitigate overfitting in machine learning is to run multiple learners with varying expressiveness in parallel. We demonstrate that this insight can immediately benefit existing SyGuS tools. We also propose a novel single-threaded technique called hybrid enumeration that interleaves different grammars and outperforms the winner of the 2018 SyGuS competition (Inv track), solving more problems and achieving a $5\times$ mean speedup.
C-QWERTY: a text entry method for circular smartwatches (S)

We present a study aimed at comparing different circular layouts for entering text on smartwatches.In particular, we measured the extent through which the use of a QWERTY layout increases user performance in the earliest sessions of use.To this aim, we designed C-QWERTY, a soft keyboard designed for circular smartwatches in which the keys are arranged along the edge of the screen in a circular layout.In order to make the keyboard more familiar to users, the order of the keys is similar to the one of traditional rectangular QWERTY keyboards.The method supports two interaction modes: tapping and gesture, in which a whole word can be written with a single gesture.As an evaluation, we compared the C-QWERTY layout with Cirrin, another circular layout in witch the order of the characters has been optimized to minimize the distance between successive keys during writing.The experimental results showed that the C-QWERTY, with a text entry speeds of 9.1 wpm and 7.7 wpm (tapping and gesture mode, respectively), outperformed the Cirrin layout with entry speeds of 6.6 wpm and 5.5 wpm.The increase in text entry speed due to the use of the QWERTY layout was 38 percent.
Data Mining Menggunakan Algoritma Apriori untuk Rekomendasi Produk bagi Pelanggan

The implementation of a marketing strategy requires a reference so that promotion can be on target, such as by looking for similarities between product items. This study examines the application of the association rule method and apriori algorithm to the purchase transaction dataset to assist in forming candidate combinations among product items for customer recommended product promotion. The purchase transaction dataset was collected in October and November 2018 with a total data of 1027. In the experiment, the minimum value of support is 85%, and the minimum confidence value is 90% by processing data using the Weka software 3.9 version. Apriori algorithm can form association rules as a reference in the promotion of company products and decision support in providing product recommendations to customers based on defined minimum support and confidence values.
SimNet: Similarity-based network embeddings with mean commute time

In this paper, we propose a new approach for learning node embeddings for weighted undirected networks. We perform a random walk on the network to extract the latent structural information and perform node embedding learning under a similarity-based framework. Unlike previous works, we apply a different criterion to capture the proximity information between nodes in a network, and use it for improved modeling of similarities between nodes. We show that the mean commute time (MCT) between two nodes, defined as the average time a random walker takes to reach a target node and return to the source, plays a crucial role in quantifying the actual degree of proximity between two nodes of the network. We then introduce a novel definition of a similarity matrix that is based on the pair-wise mean commute time captured, which enables us to adequately represent the connection of similar nodes. We utilize pseudoinverse of the Laplacian matrix of the graph for calculating such a proximity measure, capturing rich structural information out of the graph for learning more adequate node representations of a network. The results of different experiments on three real-world networks demonstrate that our proposed method outperforms existing related efforts in classification, clustering, visualization as well as link prediction tasks.