Reviewerly/exHarmony huggingface dataset:
A Study of the Metatheory of Assertoric Syllogistic

We show how a semantics based on Aristotle’s texts and ecthetic proofs can be reconstructed. All truth conditions are given by means of set inclusion. Perfect syllogisms reveal to be valid arguments that deserve a validity proof. It turns out of these proofs that transitivity of set inclusion is the necessary and sufficient condition for the validity and perfection of a syllogism. The proofs of validity for imperfect syllogisms are direct proofs without conversion in a calculus of natural deduction. Transitivity of set inclusion turns out to be a necessary condition for the validity of imperfect syllogisms. As a consequence, it can be established what the main metalogical difference between a perfect and an imperfect syllogism is. The validity of the laws of conversion is also obtained by direct proofs. Finally, it is shown that and explained why some imperfect syllogisms satisfy the definition of a perfect syllogism.
Modulation recognition with frequency offset and phase offset over multipath channels

Modulation recognition becomes unreliable at low signal-to-noise ratio (SNR) over fading channel. A novel method is proposed to recognize the digital modulated signals with frequency and phase offsets over multi-path fading channels in this paper. This method can overcome the effects of phase offset, Gaussian noise and multi-path fading. To achieve this, firstly, the characteristic parameters search is constructed based on the cyclostationarity of received signals, to overcome the phase offset, Gaussian white noise, and influence caused by multi-path fading. Then, the carrier frequency of the received signal is estimated, and the maximum characteristic parameter is searched around the integer multiple carriers and their vicinities. Finally, the modulation types of the received signal with frequency and phase offsets are classified using decision thresholds. Simulation results demonstrate that the performance of the proposed method is better than the traditional methods when SNR is over 5dB, and that the proposed method is robust to frequency and phase offsets over multi-path channels.
Contract lenses: Reasoning about bidirectional programs via calculation

Abstract Bidirectional transformations (BXs) are a mechanism for maintaining consistency between multiple representations of related data. The lens framework, which usually constructs BXs from lens combinators, has become the mainstream approach to BX programming because of its modularity and correctness by construction. However, the involved bidirectional behaviors of lenses make the equational reasoning and optimization of them much harder than unidirectional programs. We propose a novel approach to deriving efficient lenses from clear specifications via program calculation, a correct-by-construction approach to reasoning about functional programs by algebraic laws. To support bidirectional program calculation, we propose contract lenses , which extend conventional lenses with a pair of predicates to enable safe and modular composition of partial lenses. We define several contract-lens combinators capturing common computation patterns including $\textit{fold}, \textit{filter},\textit{map}$ , and $\textit{scan}$ , and develop several bidirectional calculation laws to reason about and optimize contract lenses. We demonstrate the effectiveness of our new calculation framework based on contract lenses with nontrivial examples.
Tracking Control Design for Takagi-Sugeno Fuzzy Systems Based on Membership Function-Dependent <i>L∞</i> Performance

The design of tracking control based on membership function-dependent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance for the systems, which are described by the Takagi-Sugeno fuzzy models, is considered. In practice, most Takagi-Sugeno fuzzy systems have such a characteristic that they work on some local subsystems most of the time and on others less time. Taking advantage of this feature, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance index dependent on membership function is proposed, through which better performance may be guaranteed for those local subsystems that work most of the time. By means of the newly defined <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance index, the measurable premise variables, the estimation of immeasurable premise variables, and the estimation of nonlinear function, an observer-based tracking controller is designed in the form of linear matrix inequalities, which makes full use of the information of measurable premise variables. Finally, an example is provided to verify the effectiveness of the proposed approach. In the simulation, compared with the traditional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control method, the novel method has better robust tracking performance.
PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data

Although many deep-learning-based super-resolution approaches have been proposed in recent years, because no ground truth is available in the inference stage, few can quantify the errors and uncertainties of the super-resolved results. For scientific visualization applications, however, conveying uncertainties of the results to scientists is crucial to avoid generating misleading or incorrect information. In this paper, we propose PSRFlow, a novel normalizing flow-based generative model for scientific data superresolution that incorporates uncertainty quantification into the super-resolution process. PSRFlow learns the conditional distribution of the high-resolution data based on the low-resolution counterpart. By sampling from a Gaussian latent space that captures the missing information in the high-resolution data, one can generate different plausible super-resolution outputs. The efficient sampling in the Gaussian latent space allows our model to perform uncertainty quantification for the super-resolved results. During model training, we augment the training data with samples across various scales to make the model adaptable to data of different scales, achieving flexible super-resolution for a given input. Our results demonstrate superior performance and robust uncertainty quantification compared with existing methods such as interpolation and GAN-based super-resolution networks
Towards designing systems to preserve peace by putting a cost on war

We are observing constant progress in many technical domains that influence the world on many socio-economic and socio-technical levels. Recent rapid development of decentralized solutions redefining the digital economy, or the current boom in AI are already restructuring our ways of thinking. Despite technological prosperity we have not progressed socially to the point where war would become obsolete. We are not day dreamers, and we do not think that we can eradicate war with technology. However, we do think that decentralized technology has reached a level where it can be used to enforce proper disincentives. In this paper we are presenting a Cost of War architecture that combines an array of recent technological advancements and literally puts a cost on war. We are proposing a system that proactively and retroactively calculates economic losses of an invaded country, provides a privacy-preserving and decentralized way of identifying citizens, and enables processing of their restitution and compensation claims. We believe that it is the first system that tries to address this unsettling global problem.
Mortar-FP8: Morphing the Existing FP32 Infrastructure for High Performance Deep Learning Acceleration

Vanilla Deep Neural Networks (DNN) after training are represented with native floating-point 32 (fp32) weights. We observe that the bit-level sparsity of these weights is very abundant in the mantissa and the distribution of exponent is aggregated, which can all be directly exploited to speed up model inference. In this paper, we propose Mortar and Mortar-FP8, the off-line/on-line software and hardware collaborative approaches for fp32 DNN acceleration. The proposed methods include the software algorithms to morph the mantissa and convert fp32 weights to fp8 format, as well as associated hardware accelerator architecture to accelerate general-purpose deep learning through optimized algorithm and specialized hardware. We highlight the following results by evaluating various deep learning tasks, including image classification, object detection, video understanding, video and image super-resolution: (1) Mortar increase mantissa sparsity up to 1.58x~2.09x with only a negligible ~0.2% accuracy loss; (2) Mortar-FP8 morph the fp32 weights to fp8 format with a minimal accuracy loss of ~0.3 %; and (3) the corresponding hardware accelerator significantly outperforms baselines, achieving up to 6.032x and 6.99x performance improvements. The area and power of Mortar are 0.031 mm2 and 68.58 mW. Those metrics are 0.0505 mm2 and 25.16 mW for Mortar-FP8.
Reading Comprehension of Analytical and Hortatory Exposition: A Comparative Study

Reading has become one of the compulsory skills taught in schools and it is one skill which is tested in National Examinations in Indonesia. The needs to comprehend texts, particularly expositional ones, are indispensable. Therefore, this study has an objective to find out the significant differences in students’ evaluation of reading skills and generic structure in analytical and hortatory exposition. After calculating the data using a statistical application, SPSS, we obtained a significant difference in the second-grade students’ evaluation of MAN Darussalam Aceh Besar in analytical and hortatory exposition based on reading comprehension skills. It can be shown that Tcount is higher than Ttable (3.490 &gt; 2.101). There is also a significant difference in students’ evaluation of detailed information and vocabulary skills. Most students had difficulties in answering inference questions. The calculation result of the generic structure of analytical and hortatory exposition shows no significant difference in the second-grade students’ evaluation of Analytical and Hortatory exposition. It can be demonstrated that Tcount (-1.500) is lower than Ttable (2.101). The generic structure comparison table between analytical and hortatory exposition showed no significant difference in students’ evaluation of the second-grade students in identifying the generic structure of the texts. Still, the students scored the lowest in identifying analytical exposition conclusions and hortatory exposition suggestions.
Using Games to Learn English for Young Learners in Darussalam Elementary school

There are lots of schools in Indonesia still use conventional learning method in learning English. That is still using the book as a medium of learning. Learning English using games is still minimal and rarely uses technology as a learning medium. Using games can be used as an alternative learning media that can make the learning process more effective, engaging, and fun. This research aims to develop learning English by using games for young learners. The author uses a case study method to describe and analyze using games at a private school in Darussalam elementary school. The findings of this study are: (1) Students can improve their English skills by using games that have several values that are by student learning which make the game more suitable to be used as a medium for learning English in the classroom for students. With special needs; (2) There are some visible results through the game. First, learning styles suit students so that they feel interested in learning. Second, motivation and background affect each student's learning progress, especially in English.
Pengaruh Model ARCS dan Motivasi Belajar terhadap Keterampilan Menulis Teks Pidato Persuasif (The Effect of the ARCS Model and Learning Motivation on Persuasive Speech Text Writing Skills)

This study aims to (1) explain the effect of the ARCS and conventional learning models on writing skills; (2) explain the effect of the ARCS model Attention, Relevance, Confidence, Satisfaction ) on writing skills for students who are highly motivated to learn; (3) explain the effect of the ARCS model on writing skills for students with low learning motivation; and (4) explain the interaction between the ARCS learning model with learning motivation towards writing skills. This type of research is a quasi-experiment. Sampling was done by purposive sampling technique. The research samples are class IXB and IXE. The research findings showed (1) the persuasive speech text writing skills of students taught by the ARCS model were higher than students taught by the conventional model for both students with high learning motivation and low learning motivation; and (2) there is an interaction between the ARCS model and learning motivation in influencing persuasive speech text writing skills. Penelitian ini bertujuan untuk (1) menjelaskan pengaruh model ARCS Attention, Relevance, Confidence, Satisfaction ) dan model pembelajaran konvensional terhadap keterampilan menulis; (2) menjelaskan pengaruh model ARCS terhadap keterampilan menulis untuk siswa yang bermotivasi belajar tinggi; (3) menjelaskan pengaruh model ARCS terhadap keterampilan menulis untuk siswa yang bermotivasi belajar rendah; dan (4) menjelaskan interaksi antara model pembelajaran ARCS dengan motivasi belajar terhadap keterampilan menulis. Jenis penelitian ini adalah quasi experiment. Pengambilan sampel dilakukan dengan teknik purposive sampling. Sampel penelitian, yaitu kelas IXB dan IXE. Temuan penelitian menunjukkan (1) keterampilan menulis teks pidato persuasif siswa yang diajar dengan model ARCS lebih tinggi daripada siswa yang diajar dengan model konvensional baik pada siswa bermotivasi belajar tinggi maupun bermotivasi belajar rendah; dan (2) terdapat interaksi antara model ARCS dan motivasi belajar dalam memengaruhi keterampilan menulis teks pidato persuasif.
The Effect of Using the Simpatik Application on Performance of Pump Operator at Perumda Tirta Kanjuruhan Malang Regency Using Technological Approach Acceptance Model (TAM)

Productivity is directly related to human resources and one of the factors that influence employee performance is the use of technology (Handoko, 2010). Lamb, et al (2014) stated that one of the most important actors is the user (user). Researchers took research subjects at Perumda Tirta Kanjuruhan, as a Regional Owned Enterprise in Malang Regency for the provision of quality drinking water services to meet the community's drinking water needs. In 2022 an application called SIMPATIK (Tirta Kanjuruhan Pump Monitoring System) based on an app sheet has been built which is used to access data related to water pump performance more quickly and in real time. To determine the effect of the SIMPATIK application on the performance of pump operator, researchers used the Technology Acceptance Model (TAM) approach (Davis, 1985). The variables in this study are perceived ease of use (X), perceived usefulness (M1), behavioral intention to use (M2) and pump operator performance (Y). The results showed that perceived ease of use has a positive effect on perceived usefulness, perceived usefulness has a positive effect on behavioral intention to use, perceived usefulness has a positive and significant effect on pump operator performance, behavioral intention to use has a positive and significant effect on pump operator performance, perceived ease to use has a positive and significant effect on the performance of pump operators with perceived usefulness mediating partially, perceived ease to use has a positive and significant effect on pump operator performance with perceived usefulness and behavioral intention to use partially mediating. Keywords: Simpatik Application, perceived ease of use, perceived usefulness, behavioral intention to use, pump operator performance DOI: 10.7176/JESD/14-16-07 Publication date : October 2023
IMPLEMENTATION OF DOUBLE MOVING AVERAGE METHOD IN PREDICTION OF OMEGA 3 CHICKEN EGG HARVEST RESULT of PAK DARMO WEB-BASED

Eggs are one of the livestock products that help people to get adequate nutrition. In Indonesia, chicken eggs are in great demand, besides being easy to obtain and the price is affordable, Omega 3 chicken eggs are one of the chicken eggs that have very many benefits. However, chicken eggs themselves become a rare staple because the yield of chicken eggs is too little. The main purpose of this application was to forecast or predict the yield of chicken eggs accurately so that it became one of the options for egg supply in the future. Making the application used the Double Moving Average method as a forecasting method. The Double Moving Average was used as an analysis of data on the yield of chicken eggs Omega 3 with the right calculation produced an accurate forecast. Implementation of the application is intended for users starting from filling in data, filling in forecasting movements, and additional features such as recaps. The results of the application are explained from the forecasting results which have a high level of accuracy and a small error rate using the Double Moving Average method.
PENGEMBANGAN E-MODUL BERBASIS FLIPBOOK TEMA 8 SUBTEMA 3 USAHA PELESTARIAN LINGKUNGAN PEMBELAJARAN 1

This study aims to determine the development of e-modules based on flipbook theme 8 subtheme 3 efforts to preserve the learning environment 1 in class V SDN Cilangkap 3 Depok City and to determine the feasibility of flipbook-based e-modules for the learning process. This research has conducted validation tests on several experts in the media, language, and material fields to develop and perfect e-module products using flipbooks for students. The results of the data from the validation of media experts are 100%, which means that the e-module is very feasible to try out. Language experts obtained 100% results, which means they were declared very feasible and the results from material experts were 100% which were stated to be very feasible for testing. And based on trials that have been carried out on fifth grade students as many as 34 students obtained a proper score for use in the learning process of 97% and trials that have been carried out on teachers of 98% which means that the E-Module product using this flipbook is very feasible for used in the learning process
Using folk theories of recommender systems to inform human-centered explainable AI (HCXAI)

This study uses folk theories of the Spotify music recommender system to inform the principles of human-centered explainable AI (HCXAI). The results show that folk theories can reinforce, challenge, and augment these principles facilitating the development of more transparent and explainable recommender systems for the non-expert, lay public.
Malicious Player Card-Based Cryptographic Protocols with a Standard Deck of Cards Using Private Operations

This paper shows new card-based cryptographic protocols to compute Boolean functions using a standard deck of cards when the players are malicious. Card-based cryptographic protocols use physical cards instead of computers. They can be used when the software on computers is not reliable. We discuss protocols that use a standard deck of cards because it is easy to prepare. Though protocols that use private operations tend to be efficient in the number of cards used in the protocols, malicious actions are possible during private operations. This paper shows three-player protocols to prevent malicious actions by watching another player’s actions. We show logical AND, XOR, and copy protocols since any Boolean functions can be realized by a combination of the protocols. The numbers of cards used by the protocols are the minimum.
A New Gadget Decomposition Algorithm with Less Noise Growth in HE Schemes

A gadget decomposition algorithm can invert a specific gadget matrix and produce an output with specific statistical properties. Such algorithms are commonly used in GSW-type homomorphic encryption schemes, like TFHE, to enable homomorphic multiplication on ciphertexts while controlling noise growth. In this paper, we propose a new decomposition algorithm that has lower noise growth compared to existing algorithms. Our work is inspired by Genise et al.’s algorithm [EUROCRYPT 2018] and can be considered an improved version of their algorithm. Our decomposition procedure is designed using the idea of Babai’s nearest plane algorithm. Our experimental result show that both the noise growth and efficiency are superior to Genise et al.’s algorithm, and Zhang-Yu’s algorithm [PKC 2022].
Flexible hidden Markov models using hmmTMB and applications in ecology

I will present hidden Markov models, a general class of stochastic processes widely used in many areas (including ecology, financial analysis, medical statistics, etc.). In particular, I will explain how the parameters of interest can be specified as flexible functions of covariates, possibly using non-parametric relationships and random effects, to answer complex scientific questions. I will present the R package hmmTMB, which implements this approach, and I will demonstrate its use for the analysis of animal movement data.
Academic English Word Counting and Classifying System with Web Application and LINE OA

In writing an academic paper, it is crucial to use appropriate academic vocabulary and an adequate amount of academic terms. The initial way to assess the level of academic writing in a document is by analyzing the frequency and usage of academic terminology. This preliminary examination can provide valuable insights into the academic vocabulary employed in the paper. In this work, the development of an academic word counting and classifying system in English is done utilizing the Academic Word List (AWL) sublist families as the database. The system was created using Python language and libraries such as spaCy, Bootstrap, and Heroku. The proposed system is accessible through web and LINE applications and has been proven to be effective in counting and classifying words, checking message similarity, and matching the words with the academic word list. It is found that this system could serve as a valuable tool for enhancing the quality of academic writing and reviewing academic documents.