Reviewerly/exHarmony huggingface dataset:
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation

We study the Kronecker product regression problem, in which the design matrix is a Kronecker product of two or more matrices. Given $A_i \in \mathbb{R}^{n_i \times d_i}$ for $i=1,2,\dots,q$ where $n_i \gg d_i$ for each $i$, and $b \in \mathbb{R}^{n_1 n_2 \cdots n_q}$, let $\mathcal{A} = A_1 \otimes A_2 \otimes \cdots \otimes A_q$. Then for $p \in [1,2]$, the goal is to find $x \in \mathbb{R}^{d_1 \cdots d_q}$ that approximately minimizes $\|\mathcal{A}x - b\|_p$. Recently, Diao, Song, Sun, and Woodruff (AISTATS, 2018) gave an algorithm which is faster than forming the Kronecker product $\mathcal{A}$ Specifically, for $p=2$ their running time is $O(\sum_{i=1}^q \text{nnz}(A_i) + \text{nnz}(b))$, where nnz$(A_i)$ is the number of non-zero entries in $A_i$. Note that nnz$(b)$ can be as large as $n_1 \cdots n_q$. For $p=1,$ $q=2$ and $n_1 = n_2$, they achieve a worse bound of $O(n_1^{3/2} \text{poly}(d_1d_2) + \text{nnz}(b))$. In this work, we provide significantly faster algorithms. For $p=2$, our running time is $O(\sum_{i=1}^q \text{nnz}(A_i) )$, which has no dependence on nnz$(b)$. For $p&lt;2$, our running time is $O(\sum_{i=1}^q \text{nnz}(A_i) + \text{nnz}(b))$, which matches the prior best running time for $p=2$. We also consider the related all-pairs regression problem, where given $A \in \mathbb{R}^{n \times d}, b \in \mathbb{R}^n$, we want to solve $\min_{x} \|\bar{A}x - \bar{b}\|_p$, where $\bar{A} \in \mathbb{R}^{n^2 \times d}, \bar{b} \in \mathbb{R}^{n^2}$ consist of all pairwise differences of the rows of $A,b$. We give an $O(\text{nnz}(A))$ time algorithm for $p \in[1,2]$, improving the $Ω(n^2)$ time needed to form $\bar{A}$. Finally, we initiate the study of Kronecker product low rank and low $t$-rank approximation. For input $\mathcal{A}$ as above, we give $O(\sum_{i=1}^q \text{nnz}(A_i))$ time algorithms, which is much faster than computing $\mathcal{A}$.
Recent Advances in Stochastic Sensor Control for Multi-Object Tracking

In many multi-object tracking applications, the sensor(s) may have controllable states. Examples include movable sensors in multi-target tracking applications in defence, and unmanned air vehicles (UAVs) as sensors in multi-object systems used in civil applications such as inspection and fault detection. Uncertainties in the number of objects (due to random appearances and disappearances) as well as false alarms and detection uncertainties collectively make the above problem a highly challenging stochastic sensor control problem. Numerous solutions have been proposed to tackle the problem of precise control of sensor(s) for multi-object detection and tracking, and, in this work, recent contributions towards the advancement in the domain are comprehensively reviewed. After an introduction, we provide an overview of the sensor control problem and present the key components of sensor control solutions in general. Then, we present a categorization of the existing methods and review those methods under each category. The categorization includes a new generation of solutions called selective sensor control that have been recently developed for applications where particular objects of interest need to be accurately detected and tracked by controllable sensors.
The University of Sydney’s Machine Translation System for WMT19

This paper describes the University of Sydney's submission of the WMT 2019 shared news translation task.We participated in the Finnish→English direction and got the best BLEU(33.0)score among all the participants.Our system is based on the self-attentional Transformer networks, into which we integrated the most recent effective strategies from academic research (e.g., BPE, back translation, multi-features data selection, data augmentation, greedy model ensemble, reranking, ConMBR system combination, and postprocessing).Furthermore, we propose a novel augmentation method Cycle Translation and a data mixture strategy Big/Small parallel construction to entirely exploit the synthetic corpus.Extensive experiments show that adding the above techniques can make continuous improvements of the BLEU scores, and the best result outperforms the baseline (Transformer ensemble model trained with the original parallel corpus) by approximately 5.3 BLEU score, achieving the state-of-the-art performance.
Theoretical Issues of Creation of Model Municipal Libraries

Creation of model municipal libraries of new generation is carried out within the framework of the National project “Culture” developed in pursuance of the Edict of The President of the Russian Federation № 204 of May 7, 2018. The aim of the project is to find new models of library development that combine cultural, educational, scientific and educational functions. Modernization of municipal libraries is carried out on the basis of principles of modernization of public libraries, recommended by the Model Standard for Public Library Services. The authors consider the library as a system object and on the basis of internal and external relations of the object determine the place, role and functions of library in the communication process. The study is based on the concept of library as a four-element system. The article describes each of its elements in the process of their evolution in the modern information environment. Based on the spatial approach, the authors identify the components and levels of the library space, determine directions of its development and substantiate the principles of its construction. The authors conclude that development of the model of library operation in the territorial information space should reflect the specific features of the formation of its own information space as a documentary-communication institution.
Region-specific Diffeomorphic Metric Mapping

We introduce a region-specific diffeomorphic metric mapping (RDMM) registration approach. RDMM is non-parametric, estimating spatio-temporal velocity fields which parameterize the sought-for spatial transformation. Regularization of these velocity fields is necessary. In contrast to existing non-parametric registration approaches using a fixed spatially-invariant regularization, for example, the large displacement diffeomorphic metric mapping (LDDMM) model, our approach allows for spatially-varying regularization which is advected via the estimated spatio-temporal velocity field. Hence, not only can our model capture large displacements, it does so with a spatio-temporal regularizer that keeps track of how regions deform, which is a more natural mathematical formulation. We explore a family of RDMM registration approaches: 1) a registration model where regions with separate regularizations are pre-defined (e.g., in an atlas space or for distinct foreground and background regions), 2) a registration model where a general spatially-varying regularizer is estimated, and 3) a registration model where the spatially-varying regularizer is obtained via an end-to-end trained deep learning (DL) model. We provide a variational derivation of RDMM, showing that the model can assure diffeomorphic transformations in the continuum, and that LDDMM is a particular instance of RDMM. To evaluate RDMM performance we experiment 1) on synthetic 2D data and 2) on two 3D datasets: knee magnetic resonance images (MRIs) of the Osteoarthritis Initiative (OAI) and computed tomography images (CT) of the lung. Results show that our framework achieves comparable performance to state-of-the-art image registration approaches, while providing additional information via a learned spatio-temporal regularizer. Further, our deep learning approach allows for very fast RDMM and LDDMM estimations. Code is available at https://github.com/uncbiag/registration.
Social cohesion in education: Cognitive research in the university community

The present article highlights the results of social cohesion study fulfilled in the university community of National Pedagogical Dragomanov University. It contains the consideration of social cohesion as an important factor of interpersonal communication, other social skills which are important for the community development. The purpose of the study was to identify the level of social cohesion for university community, to test the Bertelsmann Stiftung Model of Social Cohesion at the community level. Methods that were used in the study are synthesis, analysis, math, social questionnaire, etc. In the research the only data collection tool was the author’s questionnaire. There were 112 people interviewed, among them 47 employees and 65 students of National Pedagogical Dragomanov University. According to the study results, the level of social cohesion in the university community is quite sufficient, which positively characterizes the attitude of employees and students to each other, reflects their readiness for mutual respect and support, acceptance of differences and tolerance etc. Also the research highlights weak points of social interactions that form the base for further investigations and actions on social cohesion development.
An intelligent water drops-based approach for workflow scheduling with balanced resource utilisation in cloud computing

The problem of finding optimal solutions for scheduling scientific workflows in cloud environment has been thoroughly investigated using various nature-inspired algorithms. These solutions minimise the execution time of workflows, however may result in severe load imbalance among Virtual Machines (VMs) in cloud data centres. Cloud vendors desire the proper utilisation of all the VMs in the data centres to have efficient performance of overall system. Thus, load balancing of VMs becomes an important aspect while scheduling tasks in cloud environment. In this paper, we propose an approach based on Intelligent Water Drops (IWD) algorithm to minimise the execution time of workflows while balancing the resource utilisation of VMs in cloud computing environment. The proposed approach is compared with a variety of well-known heuristic and meta-heuristic techniques using three real-time scientific workflows, and experimental results show that the proposed algorithm performs better than these existing techniques in terms of makespan and load balancing.
Machine Translation and the Evaluation of Its Quality

Machine translation has already become part of our everyday life.This chapter gives an overview of machine translation approaches.Statistical machine translation was a dominant approach over the past 20 years.It brought many cases of practical use.It is described in more detail in this chapter.Statistical machine translation is not equally successful for all language pairs.Highly inflectional languages are hard to process, especially as target languages.As statistical machine translation has almost reached the limits of its capacity, neural machine translation is becoming the technology of the future.This chapter also describes the evaluation of machine translation quality.It covers manual and automatic evaluations.Traditional and recently proposed metrics for automatic machine translation evaluation are described.Human translation still provides the best translation quality, but it is, in general, time-consuming and expensive.Integration of human and machine translation is a promising workflow for the future.Machine translation will not replace human translation, but it can serve as a tool to increase productivity in the translation process.
Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence

Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation. Existing studies concatenate target keyphrases in different orders but no study has examined the effects of ordering on models' behavior. In this paper, we propose several orderings for concatenation and inspect the important factors for training a successful keyphrase generation model. By running comprehensive comparisons, we observe one preferable ordering and summarize a number of empirical findings and challenges, which can shed light on future research on this line of work.
Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks

Item-based models are among the most popular collaborative filtering approaches for building recommender systems. Random walks can provide a powerful tool for harvesting the rich network of interactions captured within these models. They can exploit indirect relations between the items, mitigate the effects of sparsity, ensure wider itemspace coverage, as well as increase the diversity of recommendation lists. Their potential however, can be hindered by the tendency of the walks to rapidly concentrate towards the central nodes of the graph, thereby significantly restricting the range of K -step distributions that can be exploited for personalized recommendations. In this work, we introduce RecWalk ; a novel random walk-based method that leverages the spectral properties of nearly uncoupled Markov chains to provably lift this limitation and prolong the influence of users’ past preferences on the successive steps of the walk—thereby allowing the walker to explore the underlying network more fruitfully. A comprehensive set of experiments on real-world datasets verify the theoretically predicted properties of the proposed approach and indicate that they are directly linked to significant improvements in top- n recommendation accuracy. They also highlight RecWalk’s potential in providing a framework for boosting the performance of item-based models. RecWalk achieves state-of-the-art top- n recommendation quality outperforming several competing approaches, including recently proposed methods that rely on deep neural networks.
<i>bAIcis</i>: A Novel Bayesian Network Structural Learning Algorithm and Its Comprehensive Performance Evaluation Against Open-Source Software

Structural learning of Bayesian networks (BNs) from observational data has gained increasing applied use and attention from various scientific and industrial areas. The mathematical theory of BNs and their optimization is well developed. Although there are several open-source BN learners in the public domain, none of them are able to handle both small and large feature space data and recover network structures with acceptable accuracy. bAIcis® is a novel BN learning and simulation software from BERG. It was developed with the goal of learning BNs from “Big Data” in health care, often exceeding hundreds of thousands features when research is conducted in genomics or multi-omics. This article provides a comprehensive performance evaluation of bAIcis and its comparison with the open-source BN learners. The study investigated synthetic datasets of discrete, continuous, and mixed data in small and large feature space, respectively. The results demonstrated that bAIcis outperformed the publicly available algorithms in structure recovery precision in almost all of the evaluated settings, achieving the true positive rates of 0.9 and precision of 0.8. In addition, bAIcis supports all data types, including continuous, discrete, and mixed variables. It is effectively parallelized on a distributed system and can work with datasets of thousands of features that are infeasible for any of the publicly available tools with a desired level of recovery accuracy.
Towards Automatic Mathematical Exercise Solving

Knowledge graphs are widely applied in many applications. Automatically solving mathematical exercises is also an interesting task which can be enhanced by knowledge reasoning. In this paper, we design MathGraph, a knowledge graph aiming to solve high school mathematical exercises. Since it requires fine-grained mathematical derivation and calculation of different mathematical objects, we design a crowdsourcing-based method to help build MathGraph. MathGraph supports massive kinds of mathematical objects, operations and constraints which may be involved in exercises. Furthermore, we propose an algorithm to align a semantically parsed exercise to MathGraph and figure out the answer automatically. Extensive experiments on real-world datasets verify the effectiveness of MathGraph.
Path planning based on map matching in UAV/UGV collaboration system

Purpose Unmanned aerial/ground vehicles (UAV/UGV) collaboration systems are increasingly being used to perform reconnaissance and rescue missions autonomously, especially in disaster areas. The paper aims to discuss this issue. Design/methodology/approach To improve visibility, this study proposes a path-planning algorithm based on map matching. Continuous ground images are first collected aerially using the UAV vision system. Subsequently, a global map of the ground environment is created by processing the collected images using the methods of image correction, image mosaic and obstacle recognition. The local map of the ground environment is obtained using the 2D laser radar sensor of the UGV. A set of features for both global and local maps is established. Unknown values during map matching are determined via the least squares method. Based on the matched mapping, the traditional A* algorithm is used for the planning of global path in the global map, and the dynamic window method is used for adjustment of the local map. Findings Simulation experiments were carried out to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm can construct a global map of the wide environment and effectively bypass the obstacles missed by the UAV. Research limitations/implications Prior to map matching, there is a need to extract the edge of obstacles in the global map. Originality/value This paper proposed a path planning algorithm based on map matching, yielding insights into the application of the UAV/UGV collaboration systems in disaster areas.
Speech Driven Backchannel Generation Using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction

We present a novel method for training a social robot to generate backchannels during human-robot interaction. We address the problem within an off-policy reinforcement learning framework, and show how a robot may learn to produce non-verbal backchannels like laughs, when trained to maximize the engagement and attention of the user. A major contribution of this work is the formulation of the problem as a Markov decision process (MDP) with states defined by the speech activity of the user and rewards generated by quantified engagement levels. The problem that we address falls into the class of applications where unlimited interaction with the environment is not possible (our environment being a human) because it may be time-consuming, costly, impracticable or even dangerous in case a bad policy is executed. Therefore, we introduce deep Q-network (DQN) in a batch reinforcement learning framework, where an optimal policy is learned from a batch data collected using a more controlled policy. We suggest the use of human-to-human dyadic interaction datasets as a batch of trajectories to train an agent for engaging interactions. Our experiments demonstrate the potential of our method to train a robot for engaging behaviors in an offline manner.
TIC para la educación: sistema adaptativo basado en mecanismos de aprendizaje automático para la apropiación de tecnologías en estudiantes de educación media

The research presents an approach based on data analytics and automatic learning mechanisms integrated into one of the most widely used digital learning platforms in the world (open EDX) as a contribution to the improvement of learning processes in middle school students in municipalities of Colombia. Methodologically, according to Creswell y Plano Clark (2007), and Clements et al. (2017) a system was created to recommend educational contents adapted to the individual characteristics of students considering the limitations in the use of technologies in educational institutions; we developed interviews with teachers and focus groups with students of 10º and 11º grade; as a result, a functional architecture proposal was generated that allows the generation of initial recommendations of contents managed according to the students' performance and the characteristics of the territory.
Key-Based Cookie-Less Session Management Framework for Application Layer Security

The goal of this study is to extend the guarantees provided by the secure transmission protocols such as Secure Sockets Layer (SSL) or Transport Layer Security (TLS) and apply them to the application layer.This paper proposes a comprehensive scheme that allows the unification of multiple security mechanisms, thereby removing the burden of authentication, mutual authentication, continuous authentication, and session management from the application development life-cycle.The proposed scheme will allow creation of high-level security mechanisms such as access control and group authentication on top of the extended security provisions.This scheme effectively eliminates the need for session cookies, session tokens and any similar technique currently in use.Hence reducing the attack surface and nullifying a vast group of attack vectors.
Business Object Centric Microservices Patterns

A key impediment towards maturing microservice architecture conceptions is the uncertainty about what it means to design fine-grained functionality for microservices. Under a traditional service-oriented architecture (SOA), the unit of functionality for software components concerns individual business domain objects and encapsulated operations, enabling desirable architectural properties such as high cohesion and loose-coupling of its components. However, at present it is not clear how this SOA design strategy should be refined for microservices nor, more generally, how design considerations for different degrees of granularity apply, in a consistent and systematic way, for large SOA systems to smaller microservices. This paper proposes microservice patterns, as a contribution to the maturity of microservice architectures, through the refinement of the functional structure of SOAs. The patterns are derived by considering the splitting of business object (BO) operations and salient types of BO relationships, which influence software structure (as captured in UML): object association, exclusive containment, inclusive containment and specialisation (i.e., subtyping). The viability of the patterns for evolving large SOA systems into microservices is demonstrated through automated microservices discovery algorithms, on two open-source enterprise systems used widely in practice, Dolibarr and SugarCRM.
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks

The problem of explaining deep learning models, and model predictions generally, has attracted intensive interest recently. Many successful approaches forgo global approximations in order to provide more faithful local interpretations of the model's behavior. LIME develops multiple interpretable models, each approximating a large neural network on a small region of the data manifold and SP-LIME aggregates the local models to form a global interpretation. Extending this line of research, we propose a simple yet effective method, NormLIME for aggregating local models into global and class-specific interpretations. A human user study strongly favored class-specific interpretations created by NormLIME to other feature importance metrics. Numerical experiments confirm that NormLIME is effective at recognizing important features.