Reviewerly/exHarmony huggingface dataset:
PEVALUATING ENGINEERING DESIGN METHODS: TAKING INSPIRATION FROM SOFTWARE ENGINEERING AND THE HEALTH SCIENCES

Abstract Engineering design methods are typically evaluated via case studies, surveys, and experiments. Meanwhile, domains such as the health sciences as well as software engineering have developed further powerful evaluation approaches. The objective of this paper is to show how evaluation approaches from the health sciences and software engineering might further the evaluation of engineering design methods. We survey these approaches and show which approaches could be transferred to the evaluation of engineering design methods.
Normal Information Diffusion Distribution and Its Application in Inferring the Optimal Probability Density Functions of the Event Coordinates From the Microseismic or Acoustic Emission Sources

To obtain optimal probability density functions (PDFs) or cumulative density functions (CDFs) of the event coordinates from the microseismic or acoustic emission sources, the normal information diffusion (NID) method based on the “3σ ” truncated interval is introduced. Six sets of different data of the event coordinates from the locating sources are used to illustrate the goodness-of-fit of the NID method, log-logistic (3P) method, lognormal method, and normal method. The results show that the Kolmogorov-Smirnov (K-S) and chi-square test values of the NID distributions (NIDDs) are always less than those of the log-logistic (3P) distributions (LLD3s), lognormal distributions (LNDs), and normal distributions (NDs); the cumulative probability values of the NIDDs are equal to 1, while those of the LLD3s, LNDs, and NDs are less than 1; the curves of the NIDDs have multimodal feature and can reflect the fluctuation of the event coordinates' data. The conclusion can be drawn that the NIDDs are the optimal PDFs or CDFs of the event coordinates from the microseismic or acoustic emission sources. In the locating methods of the microseismic or acoustic emission sources, it is suggested that the NID method can be further used to improve the locating accuracy.
Research on Online Learning Platform Based on Cloud Computing and Big Data Technology

The online learning platform is the key category of online education research, cloud computing and big data technology combined with the form of online learning, points out the new direction of development, had a great change and to enhance the online learning platform of the traditional operation mode and method of data analysis. Aiming at the problems of online learning platform, the use of cloud computing and big data technology, the design and implementation of online learning platform, improve the interaction effect between teachers and learners online.
Decision-Tree-Based Interpolation for Multidimensional Signal Compression

We investigate a decision-tree-based interpolator as part of multidimensional signal compression methods. This interpolator selects the interpolating function at each signal point through a decision tree. We propose a learning algorithm for this decision tree. This algorithm is based on a recursive procedure for calculating the entropy of quantized interpolation errors. We adapt the decision-tree-based interpolator for the hierarchical compression method. We propose a multidimensional local feature and a set of interpolating functions for this compression method. We research the decision-tree-based interpolator for compression of natural multi-dimensional signals. The experimental results prove that this interpolator can significantly (up to 30%) increase the efficiency of the hierarchical compression method.
English Phonetic Synthesis Based on DFGA G2P Conversion Algorithm

Abstract In English phonetic synthesis, it is impossible to create a thesaurus containing all vocabulary as English has an almost unlimited vocabulary. Hence, for English words that are not included in the thesaurus, generating phonetic symbols through the “Grapheme-to-phoneme (G2P)” algorithm is the best solution. For this purpose, a dynamic finite generalization (DFGA) machine learning algorithm for the rules of G2P conversion is proposed in this paper. The dictionary library used for learning has 27,040 words, 90% of which are used for rule learning, and the remaining 10% are used for testing. After ten rounds of cross-validation, the average grapheme conversion accuracy in the learning and test sets is 99.78% and 93.14%, and the average vocabulary conversion accuracy is 99.56% and 73.51%, respectively.
The Chain Alignment Problem

This paper introduces two new combinatorial optimization problems involving strings, namely, the Open image in new window , and a multiple version of it, the Open image in new window . For the first problem, a polynomial-time algorithm using dynamic programming is presented, and for the second one, a proof of its \(\mathcal {NP}\)-hardness is provided and some heuristics are proposed for it. The applicability of both problems here introduced is attested by their good results when modeling the Open image in new window .
Adaptive E-Learning Using Soft Computing Techniques

E-Learning is gaining more importance in the present education system and methodology of learning are moving from instructor-orientation to learner-orientation thereby providing learner with flexible, efficient and personalized learning environment. In this paper, adaptive e-learning using various soft computing techniques needed for achieving adaptation in learning path in e-learning is discussed. Adaptive e-learning becomes necessary for slow learners and challenged people who take their own time for learning due to their impairment.
Research on Communication Strategy based on Mobile Information Technology

Abstract Under the background of the enhancement of modern information technology, the dissemination of information tends to be diversified. The further development of mobile Internet and its communication technology has changed the way and speed of information transmission. With the advent of 5G era, smart phones, smart terminals and other devices play an important role as media due to their convenience and become the main channel for people to obtain information. The purpose of this paper is to study the emerging mobile intelligent terminals in the background of the information age and examine these new products of The Times and the propagation rules and methods behind them from the perspective of media. Based on the questionnaire survey on the usage habits of users of these intelligent terminals, this paper makes a bold prediction on the mode of communication in the information age. The research results of this paper show that in the information age of media convergence, various communication subjects are faced with the challenge of adapting to the requirements of The Times and changing the mechanism. Only by actively innovating and improving the communication mechanism from the perspective of the audience can the relevant subjects better meet the requirements of the information Age.
Application of Blockchain Technology in Civil Registration Systems

Smart conurbation treatments are surfacing the route for urbanization, which indicates that government or administrative bodies, citizenries, and corporations need to incorporate smart solutions to expound systems that can implement a specific task in a disciplined way supplanting outmoded systems. Civil registration systems are one of the outmoded systems that can be made straightforward through the application of blockchain. Transparency, immutability, protection, and confidentiality—being some of the conspicuous blockchain characteristics—make it a podium of choice for digitizing civil registration systems. This study aims to design a decentralized application (DApp), i.e., a government or an administrative gateway, to perform various activities of civil registration systems using blockchain. The designed system generates birth as well as death certificates by way of smart contracts on a permission less network using Ethereum—a blockchain-based distributed processing platform. Moreover, the paper presents DApp implementation accompanied by several frameworks such as Truffle, Ganache, MetaMask, Remix Editor, Solidity, and Web3.js. This designed system guarantees a simplified registration process and offers increased data transparency and effective record maintenance.
Task Allocation Strategy for Heterogeneous Robot Teams in Offshore Missions

Heterogeneous robot fleets are capable of supporting dynamic and resource-constrained missions. While current temporal AI planners are able to deal with multi-robot planning problems by producing plans that take into account the individual robot capabilities and task requirements, these approaches deal with the high-dimensionality of the state space inefficiently, leading to multi-robot plans with poor plan quality. This paper proposes a novel task allocation strategy called Multi-Role Goal Assignment (MRGA) which enables for more efficient computation of plans using temporal planners. The approach allocates a mission's goals based on robot capabilities, the redundancy of the sensor system, the spatial distribution of the goals and task implementation time, avoiding the need to compute a large number of possible assignments. We demonstrate the applicability of the strategy with multiple robots operating jointly in an offshore platform. Experiments demonstrate that our approach allows for more robust solutions and improved plan quality while significantly reducing planning time.
Exploring Neural Architectures And Techniques For Typologically Diverse Morphological Inflection

Morphological inflection in low resource languages is critical to augment existing corpora in Low Resource Languages, which can help develop several applications in these languages with very good social impact. We describe our attention-based encoder-decoder approach that we implement using LSTMs and Transformers as the base units. We also describe the ancillary techniques that we experimented with, such as hallucination, language vector injection, sparsemax loss and adversarial language network alongside our approach to select the related language(s) for training. We present the results we generated on the constrained as well as unconstrained SIGMORPHON 2020 dataset (CITATION). One of the primary goals of our paper was to study the contribution varied components described above towards the performance of our system and perform an analysis on the same.
Sorry (Not Sorry): Decoding #MeToo Defenses

This Article examines the text of over two hundred public statements issued by people accused of work-related sexual harassment and misconduct as a part of the #MeToo movement. Using both computational and manual text analytics approaches, the project constructs a typology of the statements' substantive content, including admissions, denials, defenses, and apologies; their emotional content, including anger, anxiety, and sadness; and their cognitive content, including authenticity and certainty. The project also tracks specific themes throughout the statements, including attacks on the accusers, references to changing workplace norms, addiction and mental health stories, and concerns about due process. Building on this descriptive picture, the Article uses the statements to assess the #MeToo movement's progress in holding individual perpetrators to account, and in achieving structural change.
Swag: A Wrapper Method for Sparse Learning

Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test error for supervised classification and regression problems. While the general approach for these algorithms is to consider all possible attributes in a dataset to best predict the response of interest, an important branch of research is focused on sparse learning in order to avoid overfitting which can greatly affect the accuracy of out-of-sample prediction. However, in many practical settings we believe that only an extremely small combination of different attributes affect the response whereas even sparse-learning methods can still preserve a high number of attributes in high-dimensional settings and possibly deliver inconsistent prediction performance. As a consequence, the latter methods can also be hard to interpret for researchers and practitioners, a problem which is even more relevant for the “black-box”-type mechanisms of many learning approaches. Finally, aside from needing to quantify prediction uncertainty, there is often a problem of replicability since not all data-collection procedures measure (or observe) the same attributes and therefore cannot make use of proposed learners for testing purposes. To address all the previous issues, we propose to study a procedure that combines screening and wrapper methods and aims to find a library of extremely low-dimensional attribute combinations (with consequent low data collection and storage costs) in order to (i) match or improve the predictive performance of any particular learning method which uses all attributes as an input (including sparse learners); (ii) provide a low-dimensional network of attributes easily interpretable by researchers and practitioners; and (iii) increase the potential replicability of results due to a diversity of attribute combinations defining strong learners with equivalent predictive power. We call this algorithm “Sparse Wrapper AlGorithm” (SWAG).
Multi-Robot Planning Under Uncertainty with Congestion-Aware Models

When planning for multi-robot navigation tasks under uncertainty, plans should prevent robots from colliding while still reaching their goal. Solutions achieving this fall on a spectrum. At one end are solutions which prevent robots from being in the same part of the environment simultaneously at planning time, ignoring the robots' capabilities to manoeuvre around each other, whilst at the other end are solutions that solve the problem at execution time, relying solely on online conflict resolution. Both approaches can lead to inefficient behaviour. In this paper, we present a novel framework in the middle of this spectrum that explicitly reasons over the effect the presence of multiple robots has on navigation performance. We refer to this effect as congestion. We present a structure, called the probabilistic reservation table, which summarises the plans of robots, allowing us to probabilistically model congestion. We show how this structure can be used for planning by proposing an approach that, for each robot, sequentially builds and solves a Markov decision process where the transition probabilities are obtained by querying the probabilistic reservation table. We carry out experiments on synthetic data and in simulation to show the effectiveness of our framework.
Reinforcement Learning for Zone Based Multiagent Pathfinding under Uncertainty

We address the problem of multiple agents finding their paths from respective sources to destination nodes in a graph (also called MAPF). Most existing approaches assume that all agents move at fixed speed, and that a single node accommodates only a single agent. Motivated by the emerging applications of autonomous vehicles such as drone traffic management, we present zone-based path finding (or ZBPF) where agents move among zones, and agents' movements require uncertain travel time. Furthermore, each zone can accommodate multiple agents (as per its capacity). We also develop a simulator for ZBPF which provides a clean interface from the simulation environment to learning algorithms. We develop a novel formulation of the ZBPF problem using difference-of-convex functions (DC) programming. The resulting approach can be used for policy learning using samples from the simulator. We also present a multiagent credit assignment scheme that helps our learning approach converge faster. Empirical results in a number of 2D and 3D instances show that our approach can effectively minimize congestion in zones, while ensuring agents reach their final destinations.
HCC-Learn Framework for Hybrid Learning in Recommender Systems

In e-business, recommender systems have been instrumental in guiding users through their online experiences. However, these systems are often limited by the lack of labels data and data sparsity. Increasingly, data-mining techniques are utilized to address this issue. In most research, recommendations to be made are achieved via supervised learning that typically employs the k-nearest neighbor learner. However, supervised learning relies on labeled data, which may not be available at the time of learning. Data sparsity, which refers to situations where the number of items that have been recommended represents only a small subset of all available items, further affects model performance. One suggested solution is to apply cluster analysis as a preprocessing step and thus guide the learning process from natural grouping, typically using similar customer profiles, to improve predictive accuracy. In this paper, we study the benefits of applying cluster analysis as a preprocessing step prior to constructing classification models. Our HCC-Learn framework combines content-based analysis in the preprocessing stage and collaborative filtering in the final prediction stage. Our results show the value of our HCC-Learn framework applied to real-world data sets, especially when combining soft clustering and ensembles based on feature subspaces.
The value of the national language of science (part no. 1)

The article examines the history of the formation of several languages of science – Ancient Greek, Sanskrit, Arabic and Latin - relating to the material of four languages and corresponding cultures. Several considerations are given in favor of the need to preserve the national languages of science. The stages of formation of languages of science in the system of culture are traced. There are two types of languages that are used by scientific communities: 1) languages that are rooted in the national culture and remain firmly linked with the natural language community; 2) languages that are reserved for performing a certain function, while in parallel, national languages are fully functioning in society. The first type includes the Greek and Arabic, the scientific languages of the second type are Sanskrit and Latin. The key role of the humanitarian, in particular poetic, philological and philosophical culture for the formation of the language of science is shown. Based on the material of the Ancient Greek language, the stages of its development over several centuries are traced, which resulted in such linguistic tools that allowed not only to use abstract conceptual concepts, but also to organize the vocabulary hierarchically, and this as a result allowed to form any needed generic chains. The importance of the appearance of impersonal texts that comes with collections of written documents alienated from a particular teacher is emphasized.
A Fast Decision Procedure For Uniqueness of Normal Forms w.r.t. Conversion of Shallow Term Rewriting Systems

Uniqueness of normal forms w.r.t. conversion (UNC) of term rewriting systems (TRSs) guarantees that there are no distinct convertible normal forms. It was recently shown that the UNC property of TRSs is decidable for shallow TRSs (Radcliffe et al., 2010). The existing procedure mainly consists of testing whether there exists a counterexample in a finite set of candidates; however, the procedure suffers a bottleneck of having a sheer number of such candidates. In this paper, we propose a new procedure which consists of checking a smaller number of such candidates and enumerating such candidates more efficiently. Correctness of the proposed procedure is proved and its complexity is analyzed. Furthermore, these two procedures have been implemented and it is experimentally confirmed that the proposed procedure runs much faster than the existing procedure.
Do sophisticated evolutionary algorithms perform better than simple ones?

Evolutionary algorithms (EAs) come in all shapes and sizes. Theoretical investigations focus on simple, bare-bones EAs while applications often use more sophisticated EAs that perform well on the problem at hand. What is often unclear is whether a large degree of algorithm sophistication is necessary, and if so, how much performance is gained by adding complexity to an EA. We address this question by comparing the performance of a wide range of theory-driven EAs, from bare-bones algorithms like the (1+1) EA, a (2+1) GA and simple population-based algorithms to more sophisticated ones like the (1+(λ,λ)) GA and algorithms using fast (heavy-tailed) mutation operators, against sophisticated and highly effective EAs from specific applications. This includes a famous and highly cited Genetic Algorithm for the Multidimensional Knapsack Problem and the Parameterless Population Pyramid for Ising Spin Glasses and MaxSat. While for the Multidimensional Knapsack Problem the sophisticated algorithm performs best, surprisingly, for large Ising and MaxSat instances the simplest algorithm performs best. We also derive conclusions about the usefulness of populations, crossover and fast mutation operators. Empirical results are supported by statistical tests and contrasted against theoretical work in an attempt to link theoretical and empirical results on EAs.
Enhancing Webpage Navigation with a Novel Scrollbar Design

In this paper, we introduce a novel type of navigation bar especially designed to provide users with additional information as they navigate long web pages that consist of several different sections and a large amount of content. In addition to facilitating scrolling, the proposed navigation bar enables users to quickly traverse the page without needing to manually scroll. Moreover, we present the results of a comparative study that analyzed the effectiveness of our proposed system with respect to traditional approaches. From the findings of our research, which includes an evaluation of individuals’ likelihood to use the proposed navigation bar, we can conclude that users have a strong preference for our system; also we found that it increases accuracy in information finding tasks. Consequently, we believe the proposed navigation bar design could enhance web usability and improve user satisfaction.
Parsing for Natural Language in Odia: A Novel Study

In this paper a simple parsing for Odia Language using Context-Free Grammar (CFG) is shown. Parsing is a technique used for building a sentence automatically in the phrases of grammar as well as in lexicon using syntactic analysis. It also includes semantic analysis and syntactic analysis that basically attention on parsing for Odia Language based on Panini method, Context Free Grammar along with top down approach. All the things are being represented as simple tree primarily formalism in context free grammar. Noun Phrase Chunking, Tokenization, Morphological Analysis and Part of Speech Tagging are being described briefly for Odia Language.
Governing Cloud computing services

An increasing shift towards agile enterprises results into an increasing importance of IT outsourcing, amongst which Cloud computing services are used to obtain scalability and increased availability. As the use of Cloud services incurs related risks, proper risk management and Cloud governance need to be performed to mitigate these associated technical risks, operational risks, organizational risks and compliance risks. To achieve effective Cloud governance, the required structures need to be in place, governance and management processes need to be performed and relational mechanisms need to be embedded.