Reviewerly/exHarmony huggingface dataset:
The Difficult Identification of Multiworld Expressions: From Decision Criteria to Annotated Corpora

Multiword expressions (MWEs) are known to be widespread in most languages, but how can we actually identify them in texts? How can we account for the diverse nature of these multiword expressions with a consistent typology? This paper aims to address this topic by carrying out two tasks: (1) conducting a small survey with expert linguists on several types of MWEs; (2) building an annotated corpus which includes several genres incorporating a large panel of multiword expressions. The results show that experts can reach a consensus and that the annotation task can be performed satisfactorily, as long as the typology is not overly complex and clear guidelines are provided. Finally, a useful application of the annotated corpus for statistical machine translation is presented, showing significant differences among multiword expressions considered in the study.
Android and FIREBASE mBaaS-based Information System Design of Students Activity Unit (SAU) Using the Rational Unified Process (RUP) Method

The study is aimed at developing an information system for the Students Activity Unit (SAU) of the Reasoning Division based on the Android and mobile Backend as a Service mBaaS and finding out the quality of the system on the aspects of functional suitability, compatibility, usability, and performance efficiency on the ISO 25010 criteria. The study was a research and development (R&D) project. For the research method, the study used the rational unified process RUP model for software development. The developed information system satisfied the ISO 25010 quality standard for all the required criteria. On the function suitability aspect, the system reached 100% percentage fulfilling the AQuA quality standard; on the compatibility aspect 89.99%; and on the usablity aspect 82.83% ("highly feasible"). On the performance efficiency aspect the system showed a response average of 3.46 seconds ("highly satisfying").
Micro-Hypervisors: What? Why?

In this chapter we will discuss what micro-hypervisors are and why they are important. We will present a discussion on competing system architectures and delineate the capabilities and importance of micro-hypervisors.
Sem2Vec: Semantic Word Vectors with Bidirectional Constraint Propagations

Word embeddings learn a vector representation of words, which can be utilized in a large number of natural language processing applications. Learning these vectors shares the drawback of unsupervised learning: representations are not specialized for semantic tasks. In this work, we propose a full-fledged formulation to effectively learn semantically specialized word vectors (Sem2Vec) by creating shared representations of online lexical sources such as Thesaurus and lexical dictionaries. These shared representations are treated as semantic constraints for learning the word embeddings. Our methodology addresses size limitation and weak informativeness of these lexical sources by employing a bidirectional constraint propagation step. Unlike raw unsupervised embeddings that exhibit low stability and easily subject to changes under randomness, our semantic formulation learns word vectors that are quite stable. An extensive empirical evaluation on the word similarity task comprised of 11 word similarity datasets is provided where our vectors suggest notable performance gains over state of the art competitors. We further demonstrate the merits of our formulation in document text classification task over large collections of documents.
Enhanced Problem Solving Approach to Tackle Repetitive System Failures

Abstract The paper describes an innovative approach to performance improvement using Causal Learning (CL), a method based on the general observation that a business performance is largely the outcome of the organization, processes and procedures, ways of working, constraints and norms – the systems that the business applies to itself. These system causes are often remote from physical causes of equipment failures and as such remain hidden until revealed by appropriate analysis. The objective of CL is discovering these system causes that ultimately lead to an undesired outcome or event. CL helps us "learn" the performance system, develop insights from these discoveries and recognize the specific aspects of a system that require change to shift business performance. The Company adopted this approach to improve problem solving and root cause analysis of machinery failures. The initial decision to apply CL followed several outages of power generation systems that continued to occur after previous analyses of similar events in the past. An Enhanced Problem Solving Team (EPST) was established and trained to apply Causal Learning principles to reveal the underlying system causes of these outages. In the time since that first analysis the tools and techniques of CL have been applied to other undesired or unexpected business outcomes including HSE and project work with little or no direct technical content. CL reveals the contribution of well-intended human behaviours behind unwanted outcomes (e.g. hardware failures), and importantly the underlying system causes of these human behaviours. This is predicated on the basis that people do their best to achieve the goals they believe they need to achieve. When it is revealed why those goal were important and why the actions taken were "their best" with the time, tools, processes available to the individual at that time at that place the systems causes can be properly understood. These findings often surprise the organization, particularly when it is made visible to Leaders at all levels how they created, or are responsible for, the systems that influenced those human behaviours.
Distributed and Centralized Options for Self-Learning

In general, the availability of enough real data from real fog computing scenarios to produce accurate Machine Learning (ML) models is rarely ensured since new equipment, techniques, etc., are continuously being deployed in the field. Although an option is to generate data from simulation and lab experiments, such data could not cover the whole features space, which would translate into ML models inaccuracies. In this paper, we propose a self-learning approach to facilitate ML deployment in real scenarios. A dataset for ML training can be initially populated based on the results from simulation and lab experiments and once ML models are generated, ML re-training can be performed after inaccuracies are detected to improve their precision. Illustrative numerical results show the benefits from the proposed self-learning approach for two general use cases of regression and classification.
Automated Web Test for Loophole Detection

The Internet, computing systems, and web consumers have become prone to cyber-attacks. Malicious uniform resource locator (URL) is a well-known cyber-attack commonly used with the intent of data, money, or personal information stealing. This work focuses on analyzing URLs through machine learning techniques for their attack type. Our work uses lexical features of URLs to classify them according to their attack, namely phishing, Spam, benign, and malware. Four different classifiers are used, such as Decision Tree, Random Forest, SVM (Support Vector Machine), and Neural Network. The test results on our informational index show that this paper achieved the highest accuracy of 99% using Random Forest classification algorithm.
SOA and the Button Problem

Service-oriented architecture (SOA) is a popular architectural style centered around services, loose coupling, and interoperability. A recurring problem in SOA development is the Button Problem; how to ensure that whenever a “button is pressed” on some service—no matter what—the performance of other key services remains unaffected? The Button Problem is especially complex to solve in systems that have devolved into hardly comprehensible spaghettis of service dependencies. In a collaborative effort with industry partner First8, we present the first formal framework to help SOA developers solve the Button Problem, enabling automated reasoning about service sensitivities and candidate refactorings. Our formalization provides a rigorous foundation for a tool that was already successfully evaluated in industrial case studies, and it is built against two unique requirements: “whiteboard level of abstraction” and non-quantitative analysis.
Analyzing the Drivers of the Strategies Adoptions to Increase Customer Value in Agile Software Development

Agile Software Development (ASD) has been widely accepted in the software industry as a means to increase customer value, despite the few empirical studies related to the subject. A recent systematic review study, based on academic works and industry experiences, mapped a set of strategies to increase customer value in the ASD. However, the factors that drive the adoption of these strategies were not mentioned. Based on a survey of 378 software professionals from 123 software-intensive companies in Brazil, we examined the associations of these strategies mapped to six drivers, including the team context (product type, product owner type and customer type) and the context of the organization (size of unit, level of agile adoption and agile experience). The results showed that the organizational context's driving factors are associated with the adoption of a more significant number of strategies than the factors related to the team context, focusing mainly on large companies, with 100% of the teams using agile methods and with having used ASD for more than five years.
Hybrid quantum error correction in qubit architectures

Noise and errors are inevitable parts of any practical implementation of a quantum computer. As a result, large-scale quantum computation will require ways to detect and correct errors in quantum information. Here we present such a quantum error-correcting scheme for correcting the dominant phase and decay errors in superconducting qubit architectures using a hybrid approach combining autonomous correction based on engineered dissipation with traditional measurement-based quantum error correction. Using numerical simulations with realistic device parameters for superconducting circuits, we show that this scheme can achieve a five- to tenfold increase in storage time while using only six qubits for the encoding and two ancillary qubits for the operation of the autonomous correction, providing a potentially large reduction of qubit overhead compared to typical measurement-based error-correction schemes. Furthermore, the scheme relies on standard interactions and qubit driving available in most major quantum computing platforms, making it implementable in a wide range of architectures.
Nonparametric empirical Bayesian method for noncontractual setting of customer-base analysis

In the noncontractual setting of customer-base analysis, heterogeneity parameters in purchase model and lifetime model are usually assumed to follow some familiar parametric distribution such as gamma or log-normal distribution. But, in many applications, these assumptions may be questionable because the true distributions of heterogeneity parameters are usually unknown. To this end, this paper relaxes these assumptions imposed on heterogeneity parameters to develop a nonparametric approach to purchase model and lifetime model, in which unknown distributions of heterogeneity parameters are approximated by a truncated Dirichlet process prior. A nonparametric empirical Bayesian method is developed to obtain Bayesian estimations of unknown parameters in the proposed nonparametric models. The blocked Gibbs sampler is presented to draw observations required for Bayesian inference from the corresponding posterior distributions of the components of parameters. Extensive simulation studies and a CDNOW data set are presented to illustrate the newly developed methodologies.
xOOHDM

Web applications have several characteristics that differentiate them from conventional applications. However, they are often developed without considering such characteristics, even though there are specific methods for the design of these applications. This work proposes the combined use of the OOHDM method and executable models in an integrated environment for the model-driven development of Web applications. This environment provides a DSML for navigation modeling using the notations of the OOHDM and automatically generates the corresponding executable model, which permits simulating and testing this navigation without the need of code generation. It allows developers addressing specific aspects of web applications, such as hypertext, and identifying and fixing design problems beforehand via model execution.
Neighbor-rank densities for non-metric data

In this work we propose rank densities, a novel density measure for non-metric data. Unlike typical measures employed in density clustering algorithms, rank densities are defined via ranks of patterns in k-nearest neighbor lists. Therefore they depend on the structure of the k-nearest neighbor graph, rather than exact values of the similarity measure. This property makes them robust to noise and easier to use with complicated similarity measures. We propose a simple iterative algorithm to estimate rank densities and demonstrate that it converges to a solution that does not depend on the initial configuration or the optimization step. Finally, we demonstrate an example application of rank densities to a real-world non-metric dataset.
Application of Extreme Learning Machine to Automatic License Plate Recognition

This paper proposes a new method for vehicle license plate recognition on the basis of Extreme Learning Machine. ELM is a new category of neural networks which possesses compelling characteristics essential for license plate recognition, such as low computational complexity, fast training, and good generalization (as opposed to traditional neural networks). The proposed method studies incorporation of three ELMs (i.e., basic ELM, incremental ELM, and enhanced incremental ELM) into a typical pipeline for automatic license plate recognition. In the preliminary study (under Windows PC with MATLAB), the success rate of recognition is 87.5% with execution time of 0.3s. A comparative study also shows that the proposed method outperforms conventional approaches (template matching, edge statistics, RBF, and SVM) in terms of accuracy and speed.
The Effects of Targeted Instruction and Other Interventions on the Interlibrary Loan Use Patterns of Historians and Their Students

Prior to this study, the Access Services Librarian systematically improved interlibrary loan (ILL) and related services offered by the R. Barbara Gitenstein Library of The College of New Jersey (TCNJ). She and her staff reviewed and revised workflows even as the Humanities Librarian focused on instructional efforts to further drive ILL use. Patrons’ confidence in ILL expanded. Requests for non-returnables, primarily journal articles and book chapters delivered electronically and within twenty-four hours, increased significantly. Returnables now arrive on average within six business days, more than double the speed previously achieved. Despite vastly improved turnaround times for all material types and efforts to boost users’ awareness of those improvements, faculty members affiliated with the History Department whose students rely on printed monographs told us that they remained reluctant to initiate ILL requests. Moving beyond anecdote, the Humanities and Access Services Librarians surveyed TCNJ historians and their capstone students on attitudes toward and use of ILL. Survey results are cross-referenced with ILL use statistics to aggregate reality versus perception among study participants. Changes in target patrons’ use of, confidence in, and feelings about TCNJ ILL over time are described.
History and Development Tendency of Human - Computer Dialogue System

As the core technology in the field of artificial intelligence, human-computer dialogue system is developing rapidly, and it is more widely used in various fields. The development of science and technology is increasing people and computer communications. In order to clarify the direction, the development process and complex nature of human-computer dialogue system are classified and analysed. The development of human-computer dialogue system can be considered as three stages: embryonic, development and breakthrough according to chronological order and technical characteristics. We comprehensively combine and summarize the representative results and shortcomings of each stage, and analyze the opportunities and challenges faced by human-computer dialogue systems in terms of capabilities and technical means to adapt to human needs. It is anticipated that the work will help to understand the direction of this line of research and contribute to the development of human-computer dialogue.
Subspace-Based Encryption

The use of signal processing in the field of cryptography is an attractive approach in the recent years. As an example, the concept of Blind Source Separation (BSS) has been used for speech encryption. However, some weaknesses of this proposal from a cryptographic point of view have been observed. This chapter proposes to bypass the weaknesses, from a security point of view, of such encryption schemes based on blind source separation techniques by using vectorial subspace concepts. This leads to the proposal of new subspace-based encryption systems. The new subspace-based encryption method together with its iterative version are designed and analyzed in terms of security robustness and quality of recovered signals. Security analysis is conducted using cryptanalysis attacks, whereas quality assessment is achieved using both objective and subjective tools. The proposed subspace-based encryption system is applied for speech and images. Experimental results show an enhancement in the performances beside some interesting and specific features, from a cryptographic point of view, brought by the proposed encryption system.
CREATING GROUP PROJECTS IN SCRATCH 3.0

The article discusses the problems of organizing group work in education, in particular, group work in the Scratch visual programming environment, where it is possible to use tools that help in teamwork — Backpack, Remix, Scratch Studio. An example of creating a project by a group of students is given.
School and Municipal Stages of the National Russian Language Competition for Schoolchildren: Regional Experience

The article analyses the experience of Russian regions in designing tasks for the school and municipal stages of the National Russian Language Competition for schoolchildren. The tasks are considered in terms of their compliance with the existing recommendations, as well as facilitation of intellectual development in schoolchildren. The authors propose a number of recommendations aimed at improving materials for the school and municipal stages of the competition.
A Data Fusion System for Aerial Surveillance Information Based on Neural Network

Aerial surveillance information fusion is a critical step in air traffic control systems. The surveillance system employs fusion algorithms to process measurement data from surveillance source such as radar, reduce measurement error, predict the future position of the aircraft, and provide the supervisor with predicted data according to the time interval set by the system. The most widely used fusion algorithm in air traffic control systems is the Kalman filter algorithm. This algorithm has been applied for several decades in related fields, but it requires considerable manpower and time to adjusting parameters, so it's use-cost is relatively high. The Kalman filter algorithm needs to adjust the parameters of the algorithm in different scenarios according to the position-accurate calibration data, considering the sector of the radar, the distance between the aircraft and the surveillance source, and the terrain. The process is very similar to the training process of supervised learning. This paper attempts to use neural networks to fuse surveillance information in order to replace the complex manual tuning process. Firstly, the neural network has the ability to represent complex features of things. The appropriate number of network layers and neurons can characterize the distribution of measurement errors in surveillance data. Secondly, we could train the network by using sufficient surveillance data and accurate target data to automatically capture features. It reduces labor and time commitments, and reduce the use-cost of the algorithm. This paper analyzed the shortcomings of the traditional fusion algorithm, introduced the overall structure of the fusion system, focused on the application of the neural network algorithm in the system, and gave the fusion result data of the algorithm.
Robotic Harvesting of a Moving Swarm Represented by a Markov Process

This paper investigates motion planning for one or more robot(s) that attempt to harvest agents from a moving swarm. Generating motion paths that maximize the number of agents harvested differs from many traditional coverage problems because the agents move. This movement allows previously cleared areas to become recontaminated. We assume that the swarm agents prefer certain regions over others, and that we can represent the swarm by a Markov Process that encodes the agents’ preferred regions and their speed of motion. We exploit this model to design and simulate robotic coverage paths that maximize the number of agents harvested by a fleet of robots in a given time budget.
Accelerating Distributed Graphical Fluid Simulations with Micro‐partitioning

Abstract Graphical fluid simulations are CPU‐bound. Parallelizing simulations on hundreds of cores in the computing cloud would make them faster, but requires evenly balancing load across nodes. Good load balancing depends on manual decisions from experts, which are time‐consuming and error prone, or dynamic approaches that estimate and react to future load, which are non‐deterministic and hard to debug. This paper proposes Birdshot scheduling, an automatic and purely static load balancing algorithm whose performance is close to expert decisions and reactive algorithms without their difficulty or complexity. Birdshot scheduling's key insight is to leverage the high‐latency, high‐throughput, full bisection bandwidth of cloud computing nodes. Birdshot scheduling splits the simulation domain into many micro‐partitions and statically assigns them to nodes randomly. Analytical results show that randomly assigned micro‐partitions balance load with high probability. The high‐throughput network easily handles the increased data transfers from micro‐partitions, and full bisection bandwidth allows random placement with no performance penalty. Overlapping the communications and computations of different micro‐partitions masks latency. Experiments with particle‐level set, SPH, FLIP and explicit Eulerian methods show that Birdshot scheduling speeds up simulations by a factor of 2‐3, and can out‐perform reactive scheduling algorithms. Birdshot scheduling performs within 21% of state‐of‐the‐art dynamic methods that require running a second, parallel simulation. Unlike speculative algorithms, Birdshot scheduling is purely static: it requires no controller, runtime data collection, partition migration or support for these operations from the programmer.