Reviewerly/exHarmony huggingface dataset:
Multilingual and Cross-Lingual Graded Lexical Entailment

Grounded in cognitive linguistics, graded lexical entailment (GR-LE) is concerned with fine-grained assertions regarding the directional hierarchical relationships between concepts on a continuous scale. In this paper, we present the first work on cross-lingual generalisation of GR-LE relation. Starting from HyperLex, the only available GR-LE dataset in English, we construct new monolingual GR-LE datasets for three other languages, and combine those to create a set of six cross-lingual GR-LE datasets termed CL-HYPERLEX. We next present a novel method dubbed CLEAR (Cross-Lingual Lexical Entailment Attract-Repel) for effectively capturing graded (and binary) LE, both monolingually in different languages as well as across languages (i.e., on CL-HYPERLEX). Coupled with a bilingual dictionary, CLEAR leverages taxonomic LE knowledge in a resource-rich language (e.g., English) and propagates it to other languages. Supported by cross-lingual LE transfer, CLEAR sets competitive baseline performance on three new monolingual GR-LE datasets and six cross-lingual GR-LE datasets. In addition, we show that CLEAR outperforms current state-of-the-art on binary cross-lingual LE detection by a wide margin for diverse language pairs.
K-Medoids Clustering Is Solvable in Polynomial Time for a 2d Pareto Front

The k-medoids problem is a discrete sum-of-square clustering problem, which is known to be more robust to outliers than k-means clustering. As an optimization problem, k-medoids is NP-hard. This paper examines k-medoids clustering in the case of a two-dimensional Pareto front, as generated by bi-objective optimization approaches. A characterization of optimal clusters is provided in this case. This allows to solve k-medoids to optimality in polynomial time using a dynamic programming algorithm. More precisely, having N points to cluster, the complexity of the algorithm is proven in $$O(N^3)$$ time and $$O(N^2)$$ memory space. This algorithm can also be used to minimize conjointly the number of clusters and the dissimilarity of clusters. This bi-objective extension is also solvable to optimality in $$O(N^3)$$ time and $$O(N^2)$$ memory space, which is useful to choose the appropriate number of clusters for the real-life applications. Parallelization issues are also discussed, to speed-up the algorithm in practice.
Live Migration of Virtual Machines in OpenStack: A Perspective from Reliability Evaluation

Virtualization technology is widely used in cloud data centers and today's IT infrastructure. A key technology for server virtualization is the live migration of virtual machines (VMs). This technology allows VMs to be moved from one physical host to another while minimizing service downtime. The cloud providers usually use cloud operating system for virtual machine management. Currently the most widely used open source cloud operating system is OpenStack. In this paper, we investigate the reliability of VM live migration in OpenStack by increasing the system pressures and injecting network failures during the migration. We analyze the impact of these pressures and failures on the performance of VM live migration. The experimental results can be used to guide data center administrators in migration decisions and fault localization. Furthermore, it can help researchers to find bottlenecks and optimization methods for live migration in OpenStack.
Noosphere Man in the Library of Future

Noosphere is understood as the sphere of living reason conditioned by the cosmic evolution of humanity. Noosphere man is the creator and inhabitant of the noosphere. The future of Russian libra ries is predetermined by civilization transformations; therefore, the noospheric man becomes an actual subject of library thought, in particular, the subject of this article. Interpreting this object is possible in two aspects: first, as a user (reader) of library of the future; secondly, as a library worker — professional in librarianship. The article considers three scenarios of formation of noosphere: technological singularity, transhumanist immortalism and panorama of the future of Russia. The author opposes two models of the man of future: technocratic and humanistic. The choice is made in favor of humanistic model of the noosphere man, as it can provide techno-humanitarian balance in post-industrial civilization. The article discusses the possibilities of cultivation of noosphere person and the role of libraries in this pedagogical process. The author emphasizes the need of the advanced education in library-information school. In conclusion, there are presented the alternatives of future development of humanity. In the first half of the 21st century, due to objective and subjective reasons, the humanity faces the choice: technocratic way of development — continuation of spontaneous increase of technological power, ultimately leading to the necrosphere, or humanistic way — cultivation of noosphere man, the creator and inhabitant of steadily developing noosphere. Ascent to the noosphere (the sphere of mind) is possible only under the condition of growing generation of people of noosphere formation. There is no pedagogically grounded model of noosphere person, but it is obvious that the basis for the formation of such model should be the cultural heritage of humankind. Library science, pedagogy and library profession as a whole face the important and responsible task — to ensure the use of diverse library resources for humanistic noospheric transformations of modern society. The national safety of Russia depends on the successful implementation of this task.
Frame Identification as Categorization: Exemplars vs Prototypes in Embeddingland

Categorization is a central capability of human cognition, and a number of theories have been developed to account for properties of categorization.Despite the fact that many semantic tasks involve categorization, theories of categorization do not play a major role in contemporary research in computational linguistics.This paper follows the idea that embedding-based models of semantics lend themselves well to being formulated in terms of classical categorization theories.The benefit is a group of models that enables (a) the formulation of hypotheses about the impact of major design decisions, and (b) a transparent assessment of these decisions.We instantiate this idea on the frame-semantic frame identification task.We define four models that cross two design variables: (a) the choice of prototype vs. exemplar categorization, corresponding to different degrees of generalization applied to the input, and (b) the presence vs. absence of a fine-tuning step, corresponding to generic vs. task-adaptive categorization.We find that for frame identification, generalization and task-adaptive categorization both yield substantial benefits.Our prototype-based, fine-tuned model, which combines the best choices over these variables, establishes a new state-of-the-art in frame identification.
Gamification concepts for leveraging knowledge sharing in Industry 4.0

This paper presents gamification concepts implemented by a gamification engine which is incorporated in a knowledge sharing web-based application. The engine aims at increasing user’s motivation and participation in knowledge sharing and training processes taking place on a factory’s shop floor, enhance socialization and support corrective feedback and positive reinforcement. In particular, it motivates workers to participate in discussions, propose solutions to work-related problems, and upload/view useful content even when being at the workplace. The gamification engine makes use of various gamification elements and is highly configurable in terms of management of gamified tasks. It is designed to support access by both standard display devices (PCs, tablets, mobile phones) as well as Mixed/Augmented Reality platforms, such as Microsoft HoloLens, which are gaining significant traction with industry verticals. The main novelty of the gamification concepts presented is the ability to utilize dynamic worker profile information which is stored in a central database, in order to improve the effectiveness of the gamified tasks, targeting at more effective usage of the knowledge sharing platform in the Industry 4.0 domain.
Temporal and Aspectual Entailment

Inferences regarding Jane's arrival in London from predications such as Jane is going to London or Jane has gone to London depend on tense and aspect of the predications.Tense determines the temporal location of the predication in the past, present or future of the time of utterance.The aspectual auxiliaries on the other hand specify the internal constituency of the event, i.e. whether the event of going to London is completed and whether its consequences hold at that time or not.While tense and aspect are among the most important factors for determining natural language inference, there has been very little work to show whether modern NLP models capture these semantic concepts.In this paper we propose a novel entailment dataset and analyse the ability of a range of recently proposed NLP models to perform inference on temporal predications.We show that the models encode a substantial amount of morphosyntactic information relating to tense and aspect, but fail to model inferences that require reasoning with these semantic properties.
QoE Analysis of Dense Multiview Video With Head-Mounted Devices

This paper presents a system and methodology for the analysis of quality of experience factors for dense multiview (MV) video using a head-mounted device (HMD). An MV-HMD player has been designed and implemented to immerse the users in a virtual environment, where they are placed in front of a virtual lightfield display that shows a different viewpoint depending on the position of their head. This paper describes a methodology for the analysis of the subjective perception of the transition among views (motion parallax), which is specific to the visualization of MV content. While previous works simulated the user movement by predefined view paths or used complex devices to track them, this system allows the observer to move freely, varying the perspective of the scene while easily tracking the observer's position. This paper is, up to our knowledge, the first providing a complete framework for the assessment of this subjective factor using an HMD. The subjective results obtained using this framework are used to 1) assess the influence of the user movement, display settings, and content characteristics in the perception of smoothness in the view transition, and 2) analyze the performance and limitations of a prediction model for subjective smoothness scores.
SINAI at SemEval-2019 Task 5: Ensemble learning to detect hate speech against inmigrants and women in English and Spanish tweets

Misogyny and xenophobia are some of the most important social problems.With the increase in the use of social media, this feeling of hatred towards women and immigrants can be more easily expressed, therefore it can cause harmful effects on social media users.For this reason, it is important to develop systems capable of detecting hateful comments automatically.In this paper, we describe our system to analyze the hate speech in English and Spanish tweets against Immigrants and Women as part of our participation in SemEval-2019 Task 5: hatEval.Our main contribution is the integration of three individual algorithms of prediction in a model based on Vote ensemble classifier.
Non-structured DNN Weight Pruning Considered Harmful

Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compression with two main approaches. Weight pruning leverages the redundancy in the number of weights and can be performed in a non-structured, which has higher flexibility and pruning rate but incurs index accesses due to irregular weights, or structured manner, which preserves the full matrix structure with lower pruning rate. Weight quantization leverages the redundancy in the number of bits in weights. Compared to pruning, quantization is much more hardware-friendly, and has become a must-do step for FPGA and ASIC implementations. This paper provides a definitive answer to the question for the first time. First, we build ADMM-NN-S by extending and enhancing ADMM-NN, a recently proposed joint weight pruning and quantization framework. Second, we develop a methodology for fair and fundamental comparison of non-structured and structured pruning in terms of both storage and computation efficiency. Our results show that ADMM-NN-S consistently outperforms the prior art: (i) it achieves 348x, 36x, and 8x overall weight pruning on LeNet-5, AlexNet, and ResNet-50, respectively, with (almost) zero accuracy loss; (ii) we demonstrate the first fully binarized (for all layers) DNNs can be lossless in accuracy in many cases. These results provide a strong baseline and credibility of our study. Based on the proposed comparison framework, with the same accuracy and quantization, the results show that non-structrued pruning is not competitive in terms of both storage and computation efficiency. Thus, we conclude that non-structured pruning is considered harmful. We urge the community not to continue the DNN inference acceleration for non-structured sparsity.
Using Diagnostic Classification Models to Validate Attribute Hierarchies and Evaluate Model Fit in Bayesian Networks

We investigate the relationship between Bayesian inference networks (BayesNets) and diagnostic classification models (DCMs). Specifically, we demonstrate and empirically examine the equivalency of parameterizations between BayesNets and DCMs. Then, we propose a model-comparison framework for testing the model fit of BayesNets, in which we show how BayesNets are nested within the saturated DCM structural models. Additionally, we show when attributes feature a linear hierarchy, the Hierarchical DCM is nested within both BayesNets and saturated DCMs. The usefulness of proposed framework and model-fit testing strategy was supported by the results of analyzing both simulated and empirical data.
Novel Recommendation System for Tourist Spots Based on Hierarchical Sampling Statistics and SVD++

Recommendation system for tourist spots has very high potential value including social and economic benefits. The traditional clustering algorithms were usually used to build a recommendation system. However, clustering algorithms have the risk on falling into local minimums, which may decrease the final recommendation performance heavily. Few works focused their research on tourist spots recommendation and few recommendation systems consider the population attributes information for fitting the user implicit preference. To address the problem, we focused our research work on designing a novel recommendation system for tourist spots. First a new dataset named “Smart Travel” is created for the following experiments. Then hierarchical sampling statistics (HSS) model is used to acquire the user preference for different population attributes. A new recommendation list named <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">A</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> is generated in turn by fitting the excavated the user preference. More importantly, SVD++ algorithm rather than those traditional clustering algorithms is used to predict the user ratings. And a new recommendation list named <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">B</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> is generated in turn on the basis of rating predictions. Finally, the two lists <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">A</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant="normal">B</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> are fused together to boost the final recommendation performance. Experimental results demonstrate that the mean precision, mean recall, and mean F1 values of the proposed recommendation system improve about 7.5%, 6.2%, and 6.5%, respectively, compared to the best competitor. The novel recommendation system is especially better at recommending a group of tourist spots, which means it has higher practical value.
Group Event Recommendation in Event-Based Social Networks Considering Unexperienced Events

As users in event-based social networks (EBSNs) usually participate in events together with friends, classmates, colleagues, or family members, recommending events to a group of users is attracting increasing attention in EBSNs recent years.However, existing studies are lack of attention to the fact that groups in EBSNs may have potential desires for participating in the unexperienced events.In order to deal with the challenges, including mining implicit friendships between users, simulating the consulting process between users and their friends outside the groups, and simulating the negotiating process among members inside the groups, we propose a two-phase group event recommendation (2PGER) model for EBSNs.First, we leverage information, such as online social behaviors, users' event participation records, and topological structures of EBSNs to establish a global trust network among users and establish egotrust networks of all users.Then, we perform random walks on the pre-built egotrust network for each user to acquire the user's predicted preferences on the unexperienced events.Third, we adopt a random walk with restarts (RWR) method to aggregate users' preferences and recommend top N events to groups.In the end, we compare 2PGER with several baseline approaches on real datasets from Meetup.The results show that 2PGER outperforms baselines.
Manipulation of multimode squeezing in a coupled waveguide array

We present a scheme for generating and manipulating three-mode squeezed states with genuine tripartite entanglement by injecting single-mode squeezed light into an array of coupled optical waveguides. We explore the possibility to selectively generate single-mode squeezing or multimode squeezing at the output of an elliptical waveguides array, determined solely by the input light polarization. We study the effect of losses in the waveguides array and show that quantum correlations and squeezing are preserved for realistic parameters. Our results show that arrays of optical waveguides are suitable platforms for generating multimode quantum light, which could lead to novel applications in quantum metrology.
Incorporating Topic Information in a Global Feature Selection Schema for Authorship Attribution

Authorship attribution (AA) is a stylometric analysis task of finding the author of an anonymous/disputed text document. In AA, the performance improvement of class-based feature selection schemas, such as Chi-square, and Gini index over frequency-based feature selection schemas, such as document frequency, common n-grams, and inverted document frequency has been shown to be limited. In AA, the feature selection process is significantly affected by topic distributions. In this paper, we assess the performance of a global feature selection approach into which the document's topic category is incorporated to scale the existing feature weights. In this approach, the common features of an author among different topics indicate higher relevance for the author and thus have higher weights. On the other hand, features with biased topic distributions are assumed to have high topic relevance and lower weights. In this approach, the global topic measure and the author specific topic measure are combined in order to scale the existing selection weights of the features. The ten-fold cross-validation experiment result on a multi-topic dataset with a random topic distribution indicates that our approach improves the performance of Chi-square, modified Gini index, and common n-grams schemas significantly in the best performing configurations of the classifiers.
A Hierarchical Approach to Active Semantic Mapping Using Probabilistic Logic and Information Reward POMDPs

Maintaining a semantic map of a complex and dynamic environment, where the uncertainty originates in both noisy perception and unexpected changes, is a challenging problem. In particular, we focus on the problem of maintaining a semantic map of an environment by a mobile agent. In this paper we address this problem in an hierarchical fashion. Firstly, we employ a probabilistic logic model representing the semantic map, as well as the associated uncertainty. Secondly, we model the interaction of the robot with the environment with a set of information-reward POMDP models, one for each partition of the environment (e.g., a room). The partition is performed in order to address the scalability limitations of POMDP models over very large state spaces. We then use probabilistic inference to determine which POMDP and policy to execute next. Experimental results show the efficiency of this architecture in real domestic service robotic scenarios.
Lecture2Note: Automatic Generation of Lecture Notes from Slide-Based Educational Videos

Given rapid development witnessed by open educational resources (OER) in the past few decades, a considerable number of online educational videos emerge on various MOOC platforms such as Coursera and YouTube. Nevertheless, most educational videos on the internet are lengthy and lack of elaborate annotations, which poses a challenge for learners to explore and locate content of interest efficiently. To address this, we present an automatic note-generating method to establish correspondences between visual entities in the slide-based lecture video and their descriptive speech texts by evaluating the semantic relationship. Firstly, the visual entities are extracted and recognised from the presentation slides. Then, each of visual entities is associated with its corresponding descriptive speech text. Finally, a placement optimisation scheme is put forward to pack the visual entities and speech texts into a note-like layout in a compact fashion, which can help learners to improve their learning efficiency. The experimental results show that the efficient performances about visual entity extraction and correspondence matching are efficient. The user study is also designed to investigate the performance of Lecture2Note in facilitating learning. Compared with peer methods, the auto-generated note created by our method achieves a higher user satisfaction level regarding a properly structured layout as well as efficient content navigation and exploration.