Reviewerly/exHarmony huggingface dataset:
A Weakly supervised word sense disambiguation for Polish using rich lexical resources

Abstract Automatic word sense disambiguation (WSD) has proven to be an important technique in many natural language processing tasks. For many years the problem of sense disambiguation has been approached with a wide range of methods, however, it is still a challenging problem, especially in the unsupervised setting. One of the well-known and successful approaches to WSD are knowledge-based methods leveraging lexical knowledge resources such as wordnets. As the knowledge-based approaches mostly do not use any labelled training data their performance strongly relies on the structure and the quality of used knowledge sources. However, a pure knowledge-base such as a wordnet cannot reflect all the semantic knowledge necessary to correctly disambiguate word senses in text. In this paper we explore various expansions to plWordNet as knowledge-bases for WSD. Semantic links extracted from a large valency lexicon (Walenty), glosses and usage examples, Wikipedia articles and SUMO ontology are combined with plWordNet and tested in a PageRank-based WSD algorithm. In addition, we analyse also the influence of lexical semantics vector models extracted with the help of the distributional semantics methods. Several new Polish test data sets for WSD are also introduced. All the resources, methods and tools are available on open licences.
Hint-Embedding Attention-Based LSTM for Aspect Identification Sentiment Analysis

Aspect identification became an important task for aspect-based sentiment analysis. Previous approaches realized the importance of aspect identification in aspect-level sentiment analysis task. To this aim, there are different approaches proposed including rule-based and supervised learning based. Rule-based methods introduce rule mining based on features engineering, while supervised methods consider it as multi-task text classification problem. However, aspect identification is still a challenge from two perspectives: detecting the implicit aspect and mapping aspect-term into category. In this paper, we propose a novel neural network approach with Hint-embedding that aims at exploring the connection between an aspect and its semantic content in the sentence. Attention mechanism is designed to focus on different parts of a sentence based on aspects’ indicators. We experiment on benchmark datasets (SemEval 2014 task 4 restaurant and SemEval 2016 task 5 laptop), and results show that our model achieves considerable performance on aspect identification task.
Explaining Black-Box Models Using Interpretable Surrogates

Explaining black-box machine learning models is important for their successful applicability to many real world problems. Existing approaches to model explanation either focus on explaining a particular decision instance or are applicable only to specific models. In this paper, we address these limitations by proposing a new model-agnostic mechanism to black-box model explainability. Our approach can be utilised to explain the predictions of any black-box machine learning model. Our work uses interpretable surrogate models (e.g. a decision tree) to extract global rules to describe the preditions of a model. We develop an optimization procedure, which helps a decision tree to mimic a black-box model, by efficiently retraining the decision tree in a sequential manner, using the data labeled by the black-box model. We demonstrate the usefulness of our proposed framework using three applications: two classification models, one built using iris dataset, other using synthetic dataset and a regression model built for bike sharing dataset.
Teleportation-based collective attacks in Gaussian quantum key distribution

In Gaussian quantum key distribution eavesdropping attacks are conventionally modeled through the universal entangling cloner scheme, which is based on the premise that the whole environment is under control of the adversary, i.e., the eavesdropper purifies the system. This assumption implies that the eavesdropper has either access to an identity (noiseless) channel or an infinite amount of entanglement in order to simulate such an identity channel. In this work we challenge the necessity of this assumption and we propose a teleportation-based eavesdropping attack, where the eavesdropper is not assumed to have access to the shared channel, that represents the unavoidable noise due to the environment. Under collective measurements, this attack reaches optimality in the limit of an infinite amount of entanglement, while for finite entanglement resources it outperforms the corresponding optimal individual attack. We also calculate the minimum amount of distributed entanglement that is necessary for this eavesdropping scheme, since we consider it as the operationally critical quantity capturing the limitations of a realistic attack. We conclude that the fact that an infinite amount of entanglement is required for an optimal collective eavesdropping attack signifies the robustness of Gaussian quantum key distribution.Received 18 September 2019Accepted 23 December 2019DOI:https://doi.org/10.1103/PhysRevResearch.2.013208Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum cryptographyQuantum entanglementQuantum Information
Rexy, A Configurable Application for Building Virtual Teaching Assistants

In recent years, virtual assistants gained a pervasive role in many domains and education was not different from others. However, although some implementation of conversational agents for supporting students have already been presented, they were ad hoc systems, built for specific courses and impossible to generalize. Also, there is a lack of research about the effects that the development of systems capable of interacting with both the students and the professors would have. In this paper, we introduce Rexy, a configurable application that can be used to build virtual teaching assistants for diverse courses, and present the results of a user study carried out using it as a virtual teaching assistant for an on-site course held at Politecnico di Milano. The qualitative analysis of the usage that was made of the assistant and the results of a post study questionnaire the students were asked to fill showed that they see conversational agents as useful tools for helping them in their studies.
Measuring QoS and QoE for a Softwarized Video Surveillance System in a 5G Network

In the last few years, thanks to the SDN and NFV network paradigms and to the MEC cloud computing paradigm, network is modifying its ossified infrastructure through a softwarization process. This process is one of the main enabler of the forthcoming 5G network. Despite the huge amount of research that has been carried out in the recent literature around both service definition and deployment in a 5G network, and characterization of quality of service (QoS) and quality of experience (QoE), there is the need of testing services on field, in order to understand how key performance indices (KPI) depend on each level of the deployment architecture. This paper considers DiMoViS, an implementation of a softwarized video surveillance system in 5G networks, with the target of identifying the main QoS and QoE performance parameters. Measurements of these parameters are achieved by using a 5G platform provided by the European Fire TRIANGLE project.
Deepsub: A Novel Subset Selection Framework for Training Deep Learning Architectures

Deep learning algorithms automatically learn a set of informative features from a given dataset and have depicted commendable performance on a variety of computer vision applications. However, efficient training of deep learning architectures with a large number of hidden layers is mostly dependent on high-end GPUs and distributed computing infrastructures. Some applications (such as applications running on mobile platforms), have limited access to computational resources and face a fundamental challenge in handling large scale training data. Cloud services can be leveraged for training, but involve challenges with data privacy and cost. In such applications, it is crucial to select a subset of informative training samples and use only the extracted subset to induce the deep model. In this paper, we propose a novel subset selection algorithm, DeepSub, to address this practical challenge. Our framework is computationally efficient, easy to implement and also enjoys nice theoretical properties. Our extensive empirical studies on three challenging computer vision applications (face, handwritten digits and object recognition), using three popular deep learning architectures (AlexNet, GoogleNet and ResNet) corroborate the potential of DeepSub over competing baselines.
Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation.

Graph Neural Networks (GNNs) have been emerging as a promising method for relational representation including recommender systems. However, various challenging issues of social graphs hinder the practical usage of GNNs for social recommendation, such as their complex noisy connections and high heterogeneity. The oversmoothing of GNNs is an obstacle of GNN-based social recommendation as well. Here we propose a new graph embedding method Heterogeneous Graph Propagation (HGP) to tackle these issues. HGP uses a group-user-item tripartite graph as input to reduce the number of edges and the complexity of paths in a social graph. To solve the oversmoothing issue, HGP embeds nodes under a personalized PageRank based propagation scheme, separately for group-user graph and user-item graph. Node embeddings from each graph are integrated using an attention mechanism. We evaluate our HGP on a large-scale real-world dataset consisting of 1,645,279 nodes and 4,711,208 edges. The experimental results show that HGP outperforms several baselines in terms of AUC and F1-score metrics.
Policy Continuation with Hindsight Inverse Dynamics

Solving goal-oriented tasks is an important but challenging problem in reinforcement learning (RL). For such tasks, the rewards are often sparse, making it difficult to learn a policy effectively. To tackle this difficulty, we propose a new approach called Policy Continuation with Hindsight Inverse Dynamics (PCHID). This approach learns from Hindsight Inverse Dynamics based on Hindsight Experience Replay. Enabling the learning process in a self-imitated manner and thus can be trained with supervised learning. This work also extends it to multi-step settings with Policy Continuation. The proposed method is general, which can work in isolation or be combined with other on-policy and off-policy algorithms. On two multi-goal tasks GridWorld and FetchReach, PCHID significantly improves the sample efficiency as well as the final performance.
Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images

Recently, Stein's unbiased risk estimator (SURE) has been applied to unsupervised training of deep neural network Gaussian denoisers that outperformed classical non-deep learning based denoisers and yielded comparable performance to those trained with ground truth. While SURE requires only one noise realization per image for training, it does not take advantage of having multiple noise realizations per image when they are available (e.g., two uncorrelated noise realizations per image for Noise2Noise). Here, we propose an extended SURE (eSURE) to train deep denoisers with correlated pairs of noise realizations per image and applied it to the case with two uncorrelated realizations per image to achieve better performance than SURE based method and comparable results to Noise2Noise. Then, we further investigated the case with imperfect ground truth (i.e., mild noise in ground truth) that may be obtained considering painstaking, time-consuming, and even expensive processes of collecting ground truth images with multiple noisy images. For the case of generating noisy training data by adding synthetic noise to imperfect ground truth to yield correlated pairs of images, our proposed eSURE based training method outperformed conventional SURE based method as well as Noise2Noise. Code is available at https://github.com/Magauiya/Extended_SURE
Learning from Trajectories via Subgoal Discovery

Learning to solve complex goal-oriented tasks with sparse terminal-only rewards often requires an enormous number of samples. In such cases, using a set of expert trajectories could help to learn faster. However, Imitation Learning (IL) via supervised pre-training with these trajectories may not perform as well and generally requires additional finetuning with expert-in-the-loop. In this paper, we propose an approach which uses the expert trajectories and learns to decompose the complex main task into smaller sub-goals. We learn a function which partitions the state-space into sub-goals, which can then be used to design an extrinsic reward function. We follow a strategy where the agent first learns from the trajectories using IL and then switches to Reinforcement Learning (RL) using the identified sub-goals, to alleviate the errors in the IL step. To deal with states which are under-represented by the trajectory set, we also learn a function to modulate the sub-goal predictions. We show that our method is able to solve complex goal-oriented tasks, which other RL, IL or their combinations in literature are not able to solve.
Band-Limited Gaussian Processes: The Sinc Kernel

We propose a novel class of Gaussian processes (GPs) whose spectra have compact support, meaning that their sample trajectories are almost-surely band limited. As a complement to the growing literature on spectral design of covariance kernels, the core of our proposal is to model power spectral densities through a rectangular function, which results in a kernel based on the sinc function with straightforward extensions to non-centred (around zero frequency) and frequency-varying cases. In addition to its use in regression, the relationship between the sinc kernel and the classic theory is illuminated, in particular, the Shannon-Nyquist theorem is interpreted as posterior reconstruction under the proposed kernel. Additionally, we show that the sinc kernel is instrumental in two fundamental signal processing applications: first, in stereo amplitude modulation, where the non-centred sinc kernel arises naturally. Second, for band-pass filtering, where the proposed kernel allows for a Bayesian treatment that is robust to observation noise and missing data. The developed theory is complemented with illustrative graphic examples and validated experimentally using real-world data.
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation

Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the first privacy-preserving solution for text classification that is provably secure. Our method, which is based on Secure Multiparty Computation (SMC), encompasses both feature extraction from texts, and subsequent classification with logistic regression and tree ensembles. We prove that when using our secure text classification method, the application does not learn anything about the text, and the author of the text does not learn anything about the text classification model used by the application beyond what is given by the classification result itself. We perform end-to-end experiments with an application for detecting hate speech against women and immigrants, demonstrating excellent runtime results without loss of accuracy.
Comparison between the Stemmer Porter Effect and Nazief-Adriani on the Performance of Winnowing Algorithms for Measuring Plagiarism

Current technological developments change physical paper patterns into digital, and this has a very high impact. Positive impact because paper waste is reduced, on the other hand, the rampant copying of digital data raises the amount of plagiarism that is increasing. At present, there are many efforts made by experts to overcome the problem of plagiarism, one of which is by utilizing the winnowing algorithm as a tool to detect plagiarism data. In its development, many optimizing winnowing algorithms used stemming techniques. The most widely used stemmer algorithms include stemmer porter and nazief-adriani. However, there has not been a discussion on the comparison of the effect of performance using stemmer on the winnowing algorithm in measuring the value of plagiarism. So it is necessary to research the effect of stemmer algorithms on winnowing algorithms so that the results of plagiarism detection are more optimal. The results of this study indicate that the effect of nazief-adriani stemmer on the winnowing algorithm is superior to the stemmer porter, only decreasing the detection performance of the 0.28% similarity value while the Porter stemmer is superior in increasing the processing time to 69% faster.
Function-Space Distributions over Kernels

Gaussian processes are flexible function approximators, with inductive biases controlled by a covariance kernel. Learning the kernel is the key to representation learning and strong predictive performance. In this paper, we develop functional kernel learning (FKL) to directly infer functional posteriors over kernels. In particular, we place a transformed Gaussian process over a spectral density, to induce a non-parametric distribution over kernel functions. The resulting approach enables learning of rich representations, with support for any stationary kernel, uncertainty over the values of the kernel, and an interpretable specification of a prior directly over kernels, without requiring sophisticated initialization or manual intervention. We perform inference through elliptical slice sampling, which is especially well suited to marginalizing posteriors with the strongly correlated priors typical to function space modeling. We develop our approach for non-uniform, large-scale, multi-task, and multidimensional data, and show promising performance in a wide range of settings, including interpolation, extrapolation, and kernel recovery experiments.
Continuous-time Models for Stochastic Optimization Algorithms

We propose new continuous-time formulations for first-order stochastic optimization algorithms such as mini-batch gradient descent and variance-reduced methods. We exploit these continuous-time models, together with simple Lyapunov analysis as well as tools from stochastic calculus, in order to derive convergence bounds for various types of non-convex functions. Guided by such analysis, we show that the same Lyapunov arguments hold in discrete-time, leading to matching rates. In addition, we use these models and Ito calculus to infer novel insights on the dynamics of SGD, proving that a decreasing learning rate acts as time warping or, equivalently, as landscape stretching.
Investigating technology and organizational performance of private higher learning institutions in Malaysia

Purpose The purpose of this paper is to investigate the influence of technology on organizational performance of private higher learning institutions (HLIs) in Malaysia and to determine the area of focus for private HLIs in Malaysia. Design/methodology/approach Data collection was carried out over two months through an online self-administered questionnaire and yielded 155 samples. Subsequently, the partial least squares structural equation modeling (PLS-SEM) was used to test the reflective measurement model and the structural model for validity, reliability and hypotheses, respectively. Findings This paper discovered that between the two constructs of technology tested (technology management and technology usage), only technology management influenced organizational performance. Originality/value As Industry 4.0 is disrupting the existing business environment, inquiring into the influence of technology is of critical importance for the organizational performance of private HLIs in Malaysia. This paper provides a different perspective of how technology affects the overall organizational performance of private HLIs which differs from past studies which focused more on the effects of technology on individuals such as teachers/instructors and learners.
Texture-Aware Superpixel Segmentation

Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level. Hence, they may need fine parameter tuning to balance the two measures, and highly fail to group pixels with similar local texture properties. In this paper, we address these issues with a new Texture-Aware SuperPixel (TASP) method. To accurately segment textured and smooth areas, TASP automatically adjusts its spatial constraint according to the local feature variance. Then, to ensure texture homogeneity within superpixels, a new pixel to super-pixel patch-based distance is proposed. TASP outperforms the segmentation accuracy of the state-of-the-art methods on texture and also natural color image datasets.
Distributional Policy Optimization: An Alternative Approach for Continuous Control

We identify a fundamental problem in policy gradient-based methods in continuous control. As policy gradient methods require the agent's underlying probability distribution, they limit policy representation to parametric distribution classes. We show that optimizing over such sets results in local movement in the action space and thus convergence to sub-optimal solutions. We suggest a novel distributional framework, able to represent arbitrary distribution functions over the continuous action space. Using this framework, we construct a generative scheme, trained using an off-policy actor-critic paradigm, which we call the Generative Actor Critic (GAC). Compared to policy gradient methods, GAC does not require knowledge of the underlying probability distribution, thereby overcoming these limitations. Empirical evaluation shows that our approach is comparable and often surpasses current state-of-the-art baselines in continuous domains.
Is Deeper Better only when Shallow is Good

Understanding the power of depth in feed-forward neural networks is an ongoing challenge in the field of deep learning theory. While current works account for the importance of depth for the expressive power of neural-networks, it remains an open question whether these benefits are exploited during a gradient-based optimization process. In this work we explore the relation between expressivity properties of deep networks and the ability to train them efficiently using gradient-based algorithms. We give a depth separation argument for distributions with fractal structure, showing that they can be expressed efficiently by deep networks, but not with shallow ones. These distributions have a natural coarse-to-fine structure, and we show that the balance between the coarse and fine details has a crucial effect on whether the optimization process is likely to succeed. We prove that when the distribution is concentrated on the fine details, gradient-based algorithms are likely to fail. Using this result we prove that, at least in some distributions, the success of learning deep networks depends on whether the distribution can be approximated by shallower networks, and we conjecture that this property holds in general.