Reviewerly/exHarmony huggingface dataset:
Studying Proton–Proton Interaction at Large Hadrons Collider Using Genetic Programming

Abstract This paper describes how to use Genetic Programming (GP) as an evolutionary computational that is a family of algorithms for global optimization. GP, as a global optimization technique used by discovery of a new function for modeling physical phenomena. The p-p interactions are modeled at Large Hadron Collider (LHC) experiments, the number of charged particles multiplicity &lt;n&gt; and the total cross-section, σT, as functions of the total center of mass energy (from low to ultra-high energy), <?CDATA $\sqrt{s}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="block" overflow="scroll"> <mml:mrow> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> </mml:mrow> </mml:math> are discovered by using GP. In view of the discovered function for <?CDATA $\langle n\rangle (\sqrt{s})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="block" overflow="scroll"> <mml:mrow> <mml:mo>〈</mml:mo> <mml:mi>n</mml:mi> <mml:mo>〉</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , the overall trend of the values predicted is consistent with LHC data [predicted values are 34.8638 and 35.3520 at <?CDATA $\sqrt{s}=13TeV$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="block" overflow="scroll"> <mml:mrow> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>13</mml:mn> <mml:mi>T</mml:mi> <mml:mi>e</mml:mi> <mml:mi>V</mml:mi> </mml:mrow> </mml:math> and <?CDATA $\sqrt{s}=14TeV$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="block" overflow="scroll"> <mml:mrow> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>14</mml:mn> <mml:mi>T</mml:mi> <mml:mi>e</mml:mi> <mml:mi>V</mml:mi> </mml:mrow> </mml:math> respectively]. The new function <?CDATA ${\sigma }_{T}(\sqrt{s})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="block" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>σ</mml:mi> <mml:mi>T</mml:mi> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , trained on experimental data of Particle Data Group (PDG) demonstrates a nice match to the other models. The predicted values of the total cross section at <?CDATA $\sqrt{s}=13TeV$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="block" overflow="scroll"> <mml:mrow> <mml:msqrt> <mml:mi>s</mml:mi> </mml:msqrt> <mml:mo>=</mml:mo> <mml:mn>13</mml:mn> <mml:mi>T</mml:mi> <mml:mi>e</mml:mi> <mml:mi>V</mml:mi> </mml:mrow> </mml:math> , and 14 TeV are found to be 109.0381 mb and 111.8329 mb respectively. Furthermore, the values predicted are agreed with other models like Block
Assessing and improving the replication of chaotic attractors by means of reservoir computing

The prediction of complex nonlinear dynamical systems with the help of machine learning techniques has become increasingly popular. In particular, the so-called computing method turned out to be a very promising approach especially for the reproduction of the long-term properties of the system [1]. Yet, a thorough statistical analysis of the forecast results is missing. So far the standard approach is to use purely random Erdos-Renyi networks for the reservoir in the model. It is obvious that there is a variety of conceivable network topologies that may have an influence on the results. Using the Lorenz System we statistically analyze the quality of predicition for different parametrizations - both the exact short term prediction as well as the reproduction of the long-term properties of the system as estimated by the correlation dimension and largest Lyapunov exponent. We find that both short and longterm predictions vary significantly. Thus special care must be taken in selecting the good predictions. We investigate the benefit of using different network topologies such as Small World or Scale Free networks and show which effect they have on the prediction quality. Our results suggest that the overall performance is best for small world networks. [1] J. Pathak et al., Chaos, 27, 121102 (2017)
Study of Intermittent-Irrigation Technique to Cropping Patterns and Water Availability at Pemali Kanan Irrigation Area in Brebes Regency

Abstract Paddy farming is closely related to irrigation water supply since it is a kind of plant that has a high water requirement. Therefore, in major areas in Indonesia, paddy is not recommended to be grown in dry season. In Brebes Regency, many farmers still pick paddy for all three planting seasons that they suffer from insufficient of irrigation water supply. The objectives of this study is to find out how water availability compared with water requirement at Pemali Kanan Irrigation Area in both periods (present time and 20 years to come) and is intermittent irrigation technique required to solve this water supply problem. This research is conducted by calculating water availabilty using FJ Mock Method, generating water availability for the upcoming 2 decades using Thomas-Fiering Method and analysing water requirements for all 13 cropping patterns. Output of the analysis shows that water availability of Pemali River in both periods are statistically identical. Meanwhile, the average water requirements are mostly negative. It means that water requirements for all 13 cropping patterns could be fulfilled by rainfall. The conclusion is that in most 2-weekly periods, rainfall could provide adequate water supply for all areas. Intermittent irrigation technique will only be required in a certain period that is the 3rd planting season which lasts from Juni to October.
Dubbing with deepfakes

Artificial intelligence is so good at manipulating videos, companies are using it to dub adverts and messages, reports Donna Lu
Constructing new k-uniform and absolutely maximally entangled states

Pure multipartite quantum states of n parties and local dimension q are called k-uniform if all reductions to k parties are maximally mixed. These states are relevant for our understanding of multipartite entanglement, quantum information protocols and the construction of quantum error correcting codes. To our knowledge, the only known systematic construction of these states is based on a class of classical error correction codes known as maximum distance separable. We present a systematic method to construct other examples of k-uniform states and show that the states derived through our construction are not equivalent to any k-uniform state constructed from maximum distance separable codes. Furthermore, we used our method to construct several examples of absolutely maximally entangled states whose existence was open so far.
A class number calculation of the $5^{\text{th}}$ layer of the cyclotomic $\mathbf{Z}_2$-extension of $\mathbf{Q}(\sqrt{5})$

For a positive integer $n$, let $K_n$ be the $n$-th layer of the the cyclotomic $\mathbf{Z}_2$-extension of $\mathbf{Q}(\sqrt{5})$, which is the real quadratic field with the minimal discriminant. We prove that the class number of $K_5$ is 1.
Auto Generation of Gold Standard, Class Labeled Data Set and Ontology Extension Tool [QuadW]

Automatic Knowledge Extraction (AKE) from domain independent, unstructured text sources is a challenging task in Natural Language Processing and Text analytics. Though, supervised learning mechanisms are very much result promising, application is painful due to the mandatory requirement of a class labeled training data set, as it involves expensive manual effort which is more time consuming. As a solution for this problem, this paper introduces a novel mechanism to build a self-learned classifier model that can automatically generate class labeled training data set for Knowledge/Information Extraction from domain independent unstructured text. Sri Lankan English newspapers (which comprise unstructured text in unconstrained domains) are the main data source for this study and a prototype was built to Professional Information Extraction with the semantic pattern Who holds/held What position, Where and When (Four words start with `W', hence named `QuadW'). Methodology uses advanced machine learning techniques such as, a Random Forest with Adaboost ensemble algorithm to build a composite classification model. This classifier is called as self-learned since, it generates its own training data set automatically. This composite model has improved accuracy and avoided over fitting to data as well. The rule-based feature extraction algorithm and the hand-craft ontology developed, can also be considered as novel components of this study. Self-learned classifier has been extensively improved and tested to show higher accuracy with precision and recall close to one. Therefore, the classified output from the self-learned classifier can be used as a gold-standard data set for future research in Professional Information Extraction. The constructed ontology with approximately 400 facts, also can be effectively used in future researches. Further, introduced classifier can be used as a tool to extend the existing ontology as well. A novel usage of machine learning algorithms to text classification demonstrates that, this study goes with the state-of-the-art technologies.
SPK PENENTUAN PEMBERIAN PINJAMAN KEPADA ANGGOTA BUMDES DENGAN METODE SIMPLE ADDITIVE WEIGHTING

Information technology as a tool in the quality of manpower one of the world of government in the village, especially in the field of village-owned enterprises which are still doing work with the manual system. In addition, where it really needs a decision support system to help human resources in utilizing technology to run a process quickly and efficiently. The approach to find the value of attribute weights, namely approaching the existence of subjective, objective approaches and approaches of integration between subjective and objective. Decision Support System Implementation with Simple Additive Weighting Method can be used to determine the granting of loans to members of BUMDes Maju Bersama by entering in the form of alternative data and then entering the weight value seen in the existing criteria, so that it can produce the calculation value and the ranking of the prospective loan recipient. This draft will show the final results of each member who will borrow funds, and the highest rating can be said of the members who will receive loan assistance, the lowest rating will be given the opportunity to make decisions later to receive loan assistance.
Describing Pictures in Improving the Students’ Ability in Writing

This Research was designed to improve the students’ ability in writing by Describing pictures.The problem of the research was “can describing pictures improve students’ ability in writing?”. There were many kinds of strategies that could be used in teaching writing, but in this research, the researcher applied only describing pictures, and writing activities focusing on the kinds of activities. The design of this research was Collaborative Classroom Action Research (CAR). Based on the analytical scoring rubric showed that from 63 in the first cycle and got increased to 73 in the second cycle. In the first cycle there were 13 Students of 30 students could reach the minimum score or only 43 % students were success. While in the last cycle there were 27 Students of 30 students could reach even greater that the minimum score or 90% students were success.Based on the conclusion of this research the researcher suggests some points. Firstly, describing pictures could be employed to improve the students’ ability in writing. Secondly, it was suggested that the English teacher could apply describing pictures as one of the alternative techniques that could be used in the teaching of English to all skills especially writing.
A Study on Application of the Problem-based Learning to Reading Education Using Literary Text

This study was carried out in order to identify the applicability and effectiveness of Problem-based Learning (PBL) in university reading education using literary text. The primary purpose of presented research is to discover systematic and structural approach such as PBL to overcome traditional methodological problems in reading education. The study was applied to the university course 〈Reading a Bestseller〉 in liberal arts school. The first stage was introductory stage that presents the problem and the second stage was PBL application stage. In the third stage, each team experienced the presentation and feedback sequentially. The students read four selected books during the semester and solved four questions, one every three weeks. Review logs, peer reviews, group reviews, and task execution plans were used as assessment tools. The improvement effect on reading ability was measured using questions categorized in four areas: factual, inferential, critical and creative. As a result, the reading skill was significantly improved in the PBL application group. In addition, the results of the survey on the effectiveness of PBL application on reading education showed positive opinions on reading comprehension, team activity effectiveness, and problem resolution. Therefore, this study indicated the possibility of a new effective teaching model for university liberal arts reading education.
Data Abstraction and Relational Program Logic.

In a paper published in 1972 Hoare articulated the fundamental notions of hiding invariants and simulations. Hiding: invariants on encapsulated data representations need not be mentioned in specifications that comprise the API of a module. Simulation: correctness of a new data representation and implementation can be established by proving simulation between the old and new implementations using a coupling relation defined on the encapsulated state. These results were formalized semantically and for a simple model of state, though the paper claimed this could be extended to encompass dynamically allocated objects. In recent years, progress has been made towards formalizing the claim, for simulation, though mainly in semantic developments. In this paper, the ideas are combined with the idea in Hoare's 1969 paper: a logic of programs. For a language with dynamically allocated shared mutable objects, we introduce a relational Hoare logic that formalizes encapsulation, hiding of invariants, and relating two implementations via coupling relations. Relations and other assertions are expressed in first order logic. The proof rules are shown sound with respect to a conventional operational semantics. Applicability to representative examples of data abstraction is demonstrated using an SMT-based implementation.
Pattern representation using Neuroevolution of the augmenting topology (NEAT) on Keystroke dynamics features in Biometrics

Since several decades, the standard authentication method of username and password is still termed as vulnerable. Biometrics technologies are therefore being implemented to provide the solution for human authentication. Among the various biometrics' characteristics, Keystroke dynamics is widely used and easily accepted by users. It is the one which is smoothly deployed at the user end. Unlike other metrics adopted in other research works, the flight time, the distance between the keys and dwell time have been considered in this paper. With the rapid evolution of technology, many systems are becoming vulnerable due to different forms of attacks that can be interfered at different levels in the biometric system such as the attack on the matcher module and at the sensor level. Thus, to counteract these loopholes, the Neuroevolution of the Augmenting Topologies, which is a technique that can reduce the level of attack at the matcher module, have been applied to keystroke dynamics features and evaluated. Much effort has been placed in devising enhanced techniques to pre-process, represent and match features in the keystroke dynamics system. Firefly, yet another algorithm, which has achieved motivating results in feature subset selection has been enhanced and applied on the keystroke dynamics extracted features. Experiments carried on the proposed Neuroevolution of the Augmenting Topologies system have achieved a good recognition rate over other techniques like Chaotic Neural network and Neural network, which could produce only a recognition rate of about 86%.
Automated Segmentation of the Pectoral Muscle in Axial Breast MR Images

Pectoral muscle segmentation is a crucial step in various computer-aided applications of breast Magnetic Resonance Imaging (MRI). Due to imaging artifact and homogeneity between the pectoral and breast regions, the pectoral muscle boundary estimation is not a trivial task. In this paper, a fully automatic segmentation method based on deep learning is proposed for accurate delineation of the pectoral muscle boundary in axial breast MR images. The proposed method involves two main steps: pectoral muscle segmentation and boundary estimation. For pectoral muscle segmentation, a model based on the U-Net architecture is used to segment the pectoral muscle from the input image. Next, the pectoral muscle boundary is estimated through candidate points detection and contour segmentation. The proposed method was evaluated quantitatively with two real-world datasets, our own private dataset, and a publicly available dataset. The first dataset includes 12 patients breast MR images and the second dataset consists of 80 patients breast MR images. The proposed method achieved a Dice score of 95% in the first dataset and 89% in the second dataset. The high segmentation performance of the proposed method when evaluated on large scale quantitative breast MR images confirms its potential applicability in future breast cancer clinical applications.
An Investigation of Synthetic Aperture Radar Speckle Filtering and Image Segmentation Considering Wavelet Decomposition

Synthetic Aperture Radar (SAR) provides a capability to get a broad-area images at high resolution independently of weather conditions and daylight. The paper considers image segmentation of surface observation by SAR systems in microwave spectrum. Speckle noise is a typical problem for SAR data processing. We analyse Kuan, Lee, and Frost filters application for speckle reduction. Also, we propose a comparative study of image segmentation considering wavelet decomposition with Daubechies, Symlets, Coiflets, BiorSplines, and Fejer-Korovkin families. Otsu’s method was used for threshold determination in segmentation. Results of investigated SAR image segmentation indicate double decreasing of the number of Binary Large Objects for Kuan image filtering due to noise reduction.
A Self-Calibrating True Random Number Generator

True Random Number Generators (TRNGs) are essential in all security systems. Unfortunately, large design effort is required to ensure that a TRNG design on a Field-Programmable Gate Array (FPGA) generates a sufficient entropy density at its output. This design effort relates to the fact that for each FPGA family a manual placement and routing procedure has to be executed. On top of this often comes the additional effort of finding a suitable location inside the target FPGA. This searching procedure has to be repeated for every device separately. In this demo, we show the working of a novel entropy source for the Coherent Sampling Ring Oscillator (COSO) based TRNG. This entropy source eliminates the need for any manual intervention during the implementation process. It generates two oscillating signals that can be matched with a precision of a few picoseconds. A controller regulates this entropy source based on some predefined bounds on the period length difference of the two oscillating signals.
Clinical profile prediction by multiple instance learning from multi-sensorial data

The last years there is a great interest in developing unobtrusive health monitoring systems with a predictive component, aiming to recognize signs of illness in an attempt to assist clinicians in delivering early interventions. The objective of this work is to investigate whether the physiological and kinetic functioning and human activity of daily living monitored by multiple sensors can be used as surrogate of the standard clinical assessment. We focus on the older population and propose to utilize Multiple Instance Learning (MIL) to predict their clinical profile from the multi-sensorial data. ReliefF-MI is applied to achieve dimensionality reduction and to discover the most important features that are associated with each clinical metric, while the BagSMOTE algorithm is utilized to mitigate the class imbalance problem. The proposed methodology was evaluated on a multi-parametric dataset of 86 older adults containing clinical parameters from various domains (cognitive, physical, medical, psychological, social and showed high prognostic capacity for the person's functionality (Katz index) and social interaction (phone calls).
What Is SwiftUI

In this chapter, we'll review the principles of SwiftUI and why it came into being. You'll see the advantages that it offers over traditional methodologies of development and how easy it is to write UI without having to worry much about it in a much more declarative manner.
Two simple models of business interruption accumulation risk in cyber insurance

As modern society becomes ever more dependent on IT services, risk management of cyber incidents becomes more important. Cyber insurance is one tool, among others, for such risk management that has received much attention in the past few years. One obstacle to well-functioning cyber insurance, however, is the fact that cyber accumulation risk remains poorly understood, despite efforts from practitioners and scientists. In this article, we address the accumulation risk of business interruption incidents, an area that has received less attention than the accumulation risk of data breach incidents. Two simple models are introduced: First, a model that takes the insurer's perspective and explores the impact on aggregated claims cost from incidents that unintentionally propagate between firms. Second, a model that takes the insured's perspective, considering the impacts of limited incident management capacity and showing that there is sometimes an economic case for collectively funding additional incident managers. The paper is concluded with some reflections on the models and an outlook.
The Entity Recognition of Thai Poem Compose by Sunthorn Phu by Using the Bidirectional Long Short Term Memory Technique

The challenge of the Named Entity Recognition on the domain Thai Poem Klon-Suphap comprise of incomplete sentences, prosody, word transformation and art in language. In this article, we propose the Name Entity Recognition on the domain Thai Poem Klon-Suphap by using The Bidirectional Long Short Term network (BiLSTM) 2 models (1) BiLSTM with words embedding and (2) BiLSTM with words embedding and part of speech embedding. There were 6,216 sentences (waks) of Thai poem Phra-Aphai-Mani. The training data 4,972 sentences and testing data 1,244 sentences to recognize (1) Activity (2) Person (3) Location (4) Number (5) Body (6) Time (7) Animal and (8) Others. The experimental results of BiLSTM with words embedding and part of speech embedding showed the Precision equal 0.89, the Recall equal 0.80 and the F-measure equal 0.84. The accuracy of results is higher than BiLSTM with words embedding.