Reviewerly/exHarmony huggingface dataset:
Feature Selection Using Genetic Algorithm for Cancer Prediction System

In healthcare sector, cancer is one of the most threatening and fast-growing diseases. The early diagnosis of this disease is very important as the success rate of its treatment depends upon how early and accurately it is diagnosed. The machine learning algorithms are helpful in detection and prediction of diseases. To improve efficiency of these algorithms, optimal features need to be selected. So, this research work uses genetic algorithm to select optimal features before applying k-nearest neighbor (KNN) and weighted k-nearest neighbor (WKNN) on Wisconsin Breast Cancer Prognosis dataset extracted from UCI repository. This approach helps in early prediction and the results show that WKNN performed better with 86.44% accuracy than KNN which gives 83.05% accuracy.
Supporting Development of Online Task Guidance for Software System Users: Lessons From the WITS Project

This chapter describes WITS, a research and development effort to build a production-quality authoring system to support rapid development of intelligent tutoring systems (ITS) for software applications. It discusses the lessons we learned from building ITS authoring systems for use by a corporate training department and building ITSs for deployment in work centers. The WITS project evolved from a single-person summer project into a full-scale development project. One of the major goals of the WITS project was to produce an authorable ITS for software applications. The authoring process for an ITS usually mirrors the parts of the tutoring system: the simulation and/or interface, the student model, the teaching model, and the expert or domain model. In 1988, an online training system for Plug-in Inventory and Control System / Mechanized Order Acknowledgment was developed as a proof-of-concept prototype.
Assessment of global PAR empirical models in the Mexican Northern Plateau

Photosynthetically active radiation (PAR) is a vital parameter for various applications such as the study of plant physiology or greenhouse lighting. Since instruments for direct PAR measuring are very specific and have less accessibility compared to radiometric or photometric instruments, PAR is often estimated through empirical models that use meteorological parameters that describe the sky conditions as input data. Although it can be estimated through empirical models, these estimations are carried out only in a few areas of interest, for example, some areas of China and the Mediterranean basin, while in some agricultural areas of interest of North America the estimation of such radiation is scarce, being an example of this the Mexican Northern Plateau, which is a region with large agricultural production. In this work, the information obtained in different meteorological stations distributed within the Mexican Northern Plateau is used to assess several empirical models reported in the literature in order to know the behavior of PAR in this area. The performance validation of the models is done using statistical analysis of the <i>Q<sub>p</sub>/R<sub>s</sub> </i>ratios at noon and the monthly mean values of <i>Q<sub>p</sub></i> obtained from each of the meteorological stations. The models used for its implementation were developed in various geographic areas along the planet, and based on the results obtained they are classified according to their performance to predict PAR within the study area.
MRS-DP: Improving Performance and Resource Utilization of Big Data Applications with Deadlines and Priorities

This article proposes the MapReduce scheduler with deadline and priorities (MRS-DP) scheduler capable of handling jobs with deadlines and priorities. Big data have emerged as a key concept and revolutionized data analytics in the present era. Big data are characterized by multiple dimensions or Vs, namely volume, velocity, variety, veracity, and valence. Recently, a new and important dimension (another V) is added, known as value. Value has emerged as an important characteristic and it can be understood in terms of delay in acquiring information, leading to late decisions that may result in missed opportunities. To gain optimal benefits, this article introduces a scheduler based on jobs with deadlines and priorities intending to improve resource utilization, with efficient job progress monitoring and backup launching mechanism. The proposed scheduler is capable of accommodating multiple jobs to maximize the number of jobs processed successfully and avoid starvation of lower priority jobs while improving the resource utilization and ensuring the assured quality of service (QoS). To evaluate our proposed scheduler, we ran multiple workloads consisting of the WordCount jobs and DataSort jobs. The performance of the proposed MRS-DP scheduler is compared with minimal earliest deadline first-work conserving scheduler and MapReduce Constraint Programming based Resource Management algorithm in terms of the percentage of successful jobs, priority-wise jobs, and resource utilization of the cluster. The result of the proposed scheduler depicts an improvement of around 10%-20% in terms of the percentage of successful jobs, 20%-25% concerning effective resource utilization offered, and the ability to ensure the offered QoS.
Analysis of fixed tilt and sun tracking photovoltaic micro wind based hybrid power systems [ECM 115 (2016) 265–275]: Response to comments on Letter to Editor

The detailed response is presented here to the comments in the letter to Editor on our published paper “Analysis of fixed tilt and sun tracking photovoltaic micro wind based hybrid power systems [ECM 115 (2016) 265–275]” [1]. The main objective of the study was to identify the potential of different PV configurations for a roof mounted 6 kWp PV-wind hybrid system at Hamirpur in Indian Western Himalayan location. In this paper optimum configurations for this hybrid system were studied, using fixed and tracking photovoltaic systems. A comparative power generation analysis of different configurations of the hybrid system with fixed tilt, monthly optimum tilt, yearly optimum tilt and different PV tracking, was carried out using Hybrid Optimization Model for Electric Renewables (HOMER) legacy version based on the measured data for this location. The queries raised by a reader in a letter to Editor, has been addressed. The study results presented were based on the measured climatic data of the location for a 6 kWp educational and research hybrid system using HOMER (legacy version) and such cannot be generalized for any other location without further analysis. The follow up research is also discussed.
Nearly Optimal Robust Secret Sharing against Rushing Adversaries.

Robust secret sharing is a strengthening of standard secret sharing that allows the shared secret to be recovered even if some of the shares being used in the reconstruction have been adversarially modified. In this work, we study the setting where out of all the n shares, the adversary is allowed to adaptively corrupt and modify up to t shares, where \(n = 2t+1\) (Note that if the adversary is allowed to modify any more shares, then correct reconstruction would be impossible.). Further, we deal with rushing adversaries, meaning that the adversary is allowed to see the honest parties’ shares before modifying its own shares.
Association rules mining in parallel conditional tree based on grid computing inspired partition algorithm

Association rules have important applications in many fields, however, with the explosive growth of information technology in recent years, the mining efficiency of association rules has become a very serious problem. The parallel multi-swarm PSO frequent pattern (PMSPF) algorithm creatively combines the particle swarm optimisation (PSO) algorithm with the frequent pattern-growth (FP-growth) algorithm to greatly improve the mining efficiency of association rules. However, under the computing environment of the Spark cluster, the calculation load is not balanced. Therefore, large amount of data may lead to problems like memory overflow. In this paper, parallel conditional frequent pattern (PCFP) tree algorithm is proposed on the basis of PMSPF. First of all, through data grouping, the problem of too large a data volume to construct FP-tree is solved. Then, through parallel strategy of the condition tree, parallel computing is implemented. The experimental results show that although PCFP algorithm generates certain data redundancy in the process of data grouping, the efficiency of the algorithm is significantly higher than that of the PMSPF algorithm and traditional parallel frequent pattern (PFP) algorithm.
Canonical Solutions to Recursive Equations and Completeness of Equational Axiomatisations.

In this paper we prove completeness of four axiomatisations for finite-state behaviours with respect to behavioural equivalences at various τ-abstract levels: branching congruence, delay congruence, η-congruence, and weak congruence. Instead of merging guarded recursive equations, which was the approach originally used by Robin Milner and has since become the standard strategy for proving completeness results of this kind, in this work we take a new approach by solving guarded recursive equations with canonical solutions which are those with the fewest reachable states. The new strategy allows uniform treatment of the axiomatisations with respect to different behavioural equivalences.
Analisis Matthew Correlation Coefficient pada K-Nearest Neighbor dalam Klasifikasi Ikan Hias

&#x0D; &#x0D; &#x0D; K-Nearest Neighbor (K-NN) is a machine learning algorithm that functions to classify data. This study aims to measure the performance of K-NN algorithm by using Matthew Correlation Coefficient (MCC). The data that used in this study are the ornamental fish which consisting of 3 classes named Premium, Medium, and Low. The analysis results of the Matthew Correlation Coefficient on K-NN using Euclidean Distance obtained the highest MCC value in Medium class which is 0.786542. The second highest MCC value is in Premium class which is 0.567434. The lowest MCC value is in Low class which is 0.435269.&#x0D; Overall, the MCC values is statistically which is 0,596415.&#x0D; &#x0D; &#x0D;
Economic neoliberalism and African development

This chapter analyses the possible impacts of the neoliberalism policies pursued in sub-Saharan Africa (SSA) on the economic development in the region during post-independence. It first presents a brief account of the socioeconomic situation in the immediate post-independence era until the adoption of the stabilisation and structural adjustment programmes by SSA countries. The chapter then discusses the implementation of ‘neoliberal’ reforms in the region. It also examines SSA’s growth and development performance during the pre- and post-reforms periods, and provides some insights into the driving forces behind the region’s economic outcomes. The chapter uncovers notable differences in economic policy across SSA countries, with policy orientation in most countries reflecting ‘partial’ rather than ‘pure’ neoliberalism.Nevertheless, at least at the regional level, there is an apparent reversal from dismal performance to impressive growth accompanied by major improvements in development indicators following the reforms. These economic gains are observed to have been bolstered by relative political stability and reasonably market-friendly policies, supported by improved democratic institutions. Thus, maintaining such an institutional framework appears critical for continued economic development in Africa.
Further Optimizations of CSIDH: A Systematic Approach to Efficient Strategies, Permutations, and Bound Vectors.

CSIDH is a recent post-quantum key establishment protocol based on constructing isogenies between supersingular elliptic curves. Several recent works give constant-time implementations of CSIDH along with some optimizations of the ideal class group action evaluation algorithm, including the SIMBA technique of Meyer et al. and the “two-point method” of Onuki et al. A recent work of Cervantes-Vazquez et al. details a number of improvements to the works of Meyer et al. and Onuki et al. Several of these optimizations—in particular, the choice of ordering of the primes, the choice of SIMBA partition and strategies, and the choice of bound vector which defines the secret keyspace—have been made in an ad hoc fashion, and so while they yield performance improvements it has not been clear whether these choices could be improved upon, or how to do so. In this work we present a framework for improving these optimizations using (respectively) linear programming, dynamic programming, and convex programming techniques. Our framework is applicable to any CSIDH security level, to all currently-proposed paradigms for computing the class group action, and to any choice of model for the underlying curves. Using our framework we find improved parameter sets for the two major methods of computing the group action: in the case of the implementation of Meyer et al. we obtain a 13.04% speedup without applying the further optimizations proposed by Cervantes-Vazquez et al., while for that of Cervantes-Vazquez et al. under the two-point method we obtain a speedup of 5.23%, giving the fastest constant-time implementation of CSIDH to date.
Time-Dependent Mission Planning of Hybrid Multirotors In Surveillance Operations

The maneuverability of multirotors is one of the principal reasons for the great attention they are receiving from researchers and manufacturers in the market. Multirotors can achieve several tasks and missions with high efficiency and durability. One of the most important missions for the multirotors is the surveillance of some areas of interest. This paper presents four novel optimal time-dependent mission planning formulations for a given number of hybrid multirotors in a 3D environment. The first two formulations are concerned with minimizing the overall time of a planned mission. On the other hand, the other two formulations minimize the total mission latencies in-between tasks. Typically, planning a mission initially starts with assigning a starting point as a ground control station (GCS), from which a given number of homogeneous hybrid multirotors should start their missions. Each mission is composed of a set of way-points to be visited once by one of the multirotors while minimizing the overall time or the total in-between latencies needed to accomplish the mission. The first novel formulation optimizes the overall mission time. The second formulation is then introduced with a given threshold mission time, and the number of sufficient multirotors to accomplish this mission is required to be optimized. This given threshold mission time is precisely considered depending on the multirotors endurance. These two formulations are based on Travelling Salesman Problem (TSP). However, the third and fourth formulations are introduced to tackle the problem from the minimum latency point of view. The third formulation is a novel formulation to introduce and clarify the overall minimum latency problem with only one multirotor. Whereas the fourth formulation tackles the same problem but with more than one multirotor. The third and fourth formulations are based on the travelling repairman problem (TRP), or what is known by the delivery man problem (DMP). The results of all the aforementioned formulations results are presented in a comparative form.
A preliminary study to apply the Quine McCluskey algorithm for fuzzy rule base minimization

The Fuzzy Rule-Based Classification Systems (FR-BCS) are classification models that use fuzzy rules to represent knowledge. FBRCS are popular today, with numerous applications and studies of their behavior and efficiency. This work is dedicated to studying a method that allows the minimization of FBRCS generated by the Chi Algorithm, using the Quine-McCluskey method so that the number of generated rules can be reduced, without greatly altering the accuracy, thus improving the simplicity of the model.
APE-QUEST: an MT Quality Gate

The APE-QUEST project (2018--2020) sets up a quality gate and crowdsourcing workflow for the eTranslation system of EC’s Connecting Europe Facility to improve translation quality in specific domains. It packages these services as a translation portal for machine-to-machine and machine-to-human scenarios.
Calculation of Distance Protection Setting of Regional Power Grid Considering Traction Power Supply

Due to the asymmetry of the traction transformer, there is a problem in the calculation of the measured impedance when the line distance protection on the grid side cooperates with the traction transformer, which causes problems for the setting calculation personnel whether the distance protection can avoid the faults on the other side of the traction transformer. Firstly, the influence of traction substation on distance protection is studied and analyzed. Secondly, the analysis models of single-phase Ii traction transformer, V/v wiring traction transformer and V/x wiring traction transformer are built. Finally, the measurement impedance correction scheme of the protection on the grid side distance after the other side faults of traction transformer is analyzed, and the setting calculation proposal of distance protection is given.
A Detailed Survey of Fully Homomorphic Encryption Standards to Preserve Privacy over Cloud Communications

The Homomorphic Encryption is a term that denotes a technique of encryption to enable additional computation on cipher texts, this additional computation helps to preserve privacy and provides confidentiality to the payloads those are getting processed between cloud server and clients. The term homomorphic is an indication to the homomorphism term used in Algebra. The term chiefly underlines the fact that homomorphisms between cipher text and plaintext spaces are comparable to the encryption and decryption functions. Homomorphic encryption underscores an additional assessment potential for computation over encrypted data, even without accessing the secret key or decrypting data sets. The results of such a computation continue to be encrypted. This paper gives an account of various homomorphic standards followed in cloud communications and summarizes the key facts and stats of these methods.
A Path Planning Method for Mobile Robots Based on Fuzzy Firefly Algorithms

Introducction: Mobile Robot is a kind of robot system consisting of sensors, remote control operators and automatic control mobile carriers. It is a product of integrated application of integrated disciplines developed in recent years. In the research of mobile robot related technology, navigation technology is its core, and path planning is an important link and subject of navigation research. Objective: An improved firefly algorithm is proposed for path planning of Mobile Robots in this paper. Methods: In this paper, an improved firefly algorithm is proposed. Compared with traditional firefly algorithm, this algorithm has three main improvements: (1) using Sobol sequence to initialize population; (2) adding dynamic disturbance coefficient to enhance the global search ability of the algorithm; (3) considering the uncertainty of search, the attraction between individuals is strong. Fuzzy control is carried out by setting membership function. Results: The new algorithm takes advantage of the uniformity of Sobol sequence sampling and starts to optimize in a wider range, which makes the initial path of the algorithm longer, but because the new algorithm introduces the dynamic disturbance coefficient and the fuzzy control strategy, the average running time is shorter. Conclusion: In the simulation experiment of mobile robot path planning problem, the improved firefly algorithm proposed in this paper is easier to jump out of local optimum than the traditional firefly algorithm, and has more robust search ability. Discussion: It is obvious from the graph that in 100 iterations, the FaFA algorithm takes advantage of the uniformity of Sobol sequence sampling and starts to optimize in a wider range, which makes the initial path of the algorithm longer, but because the FaFA algorithm introduces the dynamic disturbance coefficient and the fuzzy control strategy, it makes the algorithm able.
IMPROVING STUDENTS’ MASTERY OF SIMPLE PRESENT TENSE IN DESCRIPTIVE TEXT BY USING KAHOOT! GAME

The aim of this research was to improve students mastery of simple present tense in descriptive text by using Kahoot! game. This research was a classroom action research that was done in three cycles. The subject for this research was the tenth-grade students in class X IIS 2. The writer observed students improvement in mastering the simple present tense by collecting data through observation checklist, field notes and multiple choices test. The students attitude towards the learning process was gathered by observation checklist and field notes. The data of students mastery in simple present tense was obtained by the students test which was assessed through scoring rubric. The result showed that students problems in simple present tense in the form of verb (to be) and verb (-s,-es) of the third person singular had been solved by using Kahoot! game as media for teaching, by providing picture in each question of Kahoot! game, by setting time limit in the game to encourage students participation, a music in the Kahoot! game created positive attitude in the classroom and the questioning improved the students attention and motivation in remembering the rules of simple present tense. Furthermore, the students interest in learning the simple present tense also improved. In conclusion, the media was able to improve the students mastery of simple present tense in descriptive text. The writer recommends the teacher to use Kahoot! game as media in teaching and learning process, especially in teaching simple present tense with the similar problems.
Text Emotion Detection Based on Bi- LSTM Network

Emotion detection and opinion mining in social network is a hot research topic nowadays. Most existing studies and research, however, have been using typical binary (positive/negative) or ternary (positive / negative / neutral) classification. Classifying short sequences of text into multi subclasses is relatively rarely reported. Except Bouazizi and Ohtsuki (2017), the accuracy rate of detection in their two studies was only 56.9% and 60.2%. In our study, we proposed a Bi-directional Long Short-Term Memory with Language Model (BiLSTM-LM) to classify text sequences into seven distinct emotional classes. Results showed that the accuracy rate of detection based on our model can reach as high as 64.09% on multi-class classification, which is 4 percentage points higher than the most advanced model in the world to date.