Reviewerly/exHarmony huggingface dataset:
Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation

Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, over-parameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). The ability of a DNN to fully interpolate a training dataset can render a DNN, evaluated purely on the training set, ineffective in distinguishing a cost-sensitive solution from its overall accuracy maximization counterpart. This necessitates rethinking cost-sensitive classification in DNNs. To address this challenge, this paper proposes a cost-sensitive adversarial data augmentation (CSADA) framework to make over-parameterized models cost-sensitive. The overarching idea is to generate targeted adversarial examples that push the decision boundary in cost-aware directions. These targeted adversarial samples are generated by maximizing the probability of critical misclassifications and used to train a model with more conservative decisions on costly pairs. Experiments on well-known datasets and a pharmacy medication image (PMI) dataset made publicly available show that our method can effectively minimize the overall cost and reduce critical errors, while achieving comparable performance in terms of overall accuracy.
A SAT Encoding for Optimal Clifford Circuit Synthesis

Executing quantum algorithms on a quantum computer requires compilation to representations that conform to all restrictions imposed by the device. Due to device's limited coherence times and gate fidelities, the compilation process has to be optimized as much as possible. To this end, an algorithm's description first has to be synthesized using the device's gate library. In this paper, we consider the optimal synthesis of Clifford circuits -- an important subclass of quantum circuits, with various applications. Such techniques are essential to establish lower bounds for (heuristic) synthesis methods and gauging their performance. Due to the huge search space, existing optimal techniques are limited to a maximum of six qubits. The contribution of this work is twofold: First, we propose an optimal synthesis method for Clifford circuits based on encoding the task as a satisfiability (SAT) problem and solving it using a SAT solver in conjunction with a binary search scheme. The resulting tool is demonstrated to synthesize optimal circuits for up to $26$ qubits -- more than four times as many as the current state of the art. Second, we experimentally show that the overhead introduced by state-of-the-art heuristics exceeds the lower bound by $27\%$ on average. The resulting tool is publicly available at https://github.com/cda-tum/qmap.
Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation

Research on Automatic Story Generation (ASG) relies heavily on human and automatic evaluation. However, there is no consensus on which human evaluation criteria to use, and no analysis of how well automatic criteria correlate with them. In this paper, we propose to re-evaluate ASG evaluation. We introduce a set of 6 orthogonal and comprehensive human criteria, carefully motivated by the social sciences literature. We also present HANNA, an annotated dataset of 1,056 stories produced by 10 different ASG systems. HANNA allows us to quantitatively evaluate the correlations of 72 automatic metrics with human criteria. Our analysis highlights the weaknesses of current metrics for ASG and allows us to formulate practical recommendations for ASG evaluation.
Demonstrating Software Reliability using Possibly Correlated Tests: Insights from a Conservative Bayesian Approach

This paper presents Bayesian techniques for conservative claims about software reliability, particularly when evidence suggests the software's executions are not statistically independent. We formalise informal notions of "doubting" that the executions are independent, and incorporate such doubts into reliability assessments. We develop techniques that reveal the extent to which independence assumptions can undermine conservatism in assessments, and identify conditions under which this impact is not significant. These techniques - novel extensions of conservative Bayesian inference (CBI) approaches - give conservative confidence bounds on the software's failure probability per execution. With illustrations in two application areas - nuclear power-plant safety and autonomous vehicle (AV) safety - our analyses reveals: 1) the confidence an assessor should possess before subjecting a system to operational testing. Otherwise, such testing is futile - favourable operational testing evidence will eventually decrease one's confidence in the system being sufficiently reliable; 2) the independence assumption supports conservative claims sometimes; 3) in some scenarios, observing a system operate without failure gives less confidence in the system than if some failures had been observed; 4) building confidence in a system is very sensitive to failures - each additional failure means significantly more operational testing is required, in order to support a reliability claim.
Analisis Kepuasan Mahasiswa UIN Semarang Terhadap E-Learning Menggunakan Algoritma C.45

Student satisfaction on the use of e-learning is an important role for the continuity of an educational institution, especially in the current era of the covid 19 pandemic, the process of teaching and learning activities that are usually carried out in offline classes is abolished and replaced by utilizing the e-learning system. Because of the importance of the role of e-learning as a medium for student learning, knowledge is needed to determine the level of student satisfaction of e-learning as an evaluation material for managers to improve the quality of e-learning so that it can provide optimal services to students. Therefore, a classification technique with data mining is needed using the c.45 algorithm method on the WEKA (Waikato Environment for Knowledge Analysis) software. The results show that 73% of students are satisfied with the e-learning of the State Islamic University of Semarang and 23% of students are not satisfied with an accuracy of 84.76%.
Approximate Analytical Solution of Unstable Ordinary Differential Equation Using Differential Evolution Algorithm

The application of evolutionary optimization algorithms in problem solving is currently gaining wide popularity. Use of Differential Evolution (DE) algorithm in obtaining analytically approximate solution of unstable second–order initial value Ordinary Differential Equation (ODE) is presented in this work. The methodology involves solving an associated problem of optimization with constrains to get an analytically approximate solution for the ODE under consideration. Three test cases were used to demonstrate the efficiency of our method. In comparison with other methods discussed in the literature, our method gave significant improvement on the accuracy of the obtained results.
Optimasi Metode K-Nearest Neighbor Dengan Particle Swarm Optimization Untuk Pengenalan Citra Batik Dengan Ragam Hias Geometris

Batik is an intangible cultural heritage that has been approved by UNESCO as an Indonesian cultural heritage. Batik is the art of drawing on cloth for clothing. This art of drawing is in the form of a pattern and has a philosophical meaning which is historically very closely related to the philosophy of Javanese culture. The various patterns of this batik need to be preserved, and one of the efforts to preserve this diversity is to maintain the special characteristics of the batik motif and continue to introduce it to future generations. Efforts to facilitate the introduction of batik motifs can be done through technology that is able to automatically recognize batik patterns according to the motif being tested, one of which is pattern recognition technology. This study aims to optimize the KNN method with PSO to increase the accuracy of batik pattern recognition, the research was carried out in several stages starting from data collection, preprocessing, feature extraction, and classification. The research was conducted using 310 data in the form of batik images in 7 different motifs and divided into 240 compositions for training data and 70 for testing data. At the feature extraction stage, the discrete wavelet transform method is used up to level 3, then at the classification stage, the PSO and KNN algorithms are used. The PSO algorithm is used to obtain the most optimal number of k which is used as the input parameter k in the KNN algorithm. The results of the research that have been carried out have proven that with the addition of the PSO algorithm, the accuracy of the KNN method can be increased by 6% compared to the standard KNN method.
Apk Absensi Dengan Kolaborasi Fingerprint Dan SMS Gateway Menggunakan Framework Codeignitier Pada SMKN 1 Lobalain, Rote Ndao

Vocational High School (SMK) Negeri 1 Lobalain is one of the formal education channels that provides vocational education at the secondary education level located in Rote Ndao Regency, East Nusa Tenggara Province. In improving education, cooperation from parents is needed because educating students to be disciplined is not only the responsibility of the school, but parents must also be actively involved in monitoring student attendance. One of the things that must be improved in improving the quality of education is attendance. The problem at SMK Negeri 1 Lobalain is the student attendance system which is done manually so that it has an impact on attendance manipulation. As technology develops rapidly, fingerprint technology provides attendance and avoids the manipulation of attendance data. Based on the problems that have been defined, the author aims to make an attendance Apk with fingerprints and an SMS gateway using the Codeigniter Framework, it is hoped that by making this Apk it can help schools and parents in improving the quality of education at SMKN 1 Lobalain.
Decision Support System For Employee Performance Assessment To Determine The Status Of Reward Level Operator And Foreman Using Adaptive Neuro Fuzzy Inference System (Anfis)

Employees are required to have a good work ethic in order to advance their company. This causes many companies to motivate their employees in various ways. The general goal is for better and more stable employee performance so that it benefits the company. Rewards are given to employees who excel and are able to achieve certain targets, this is more effective in motivating employees than punishment so that it can be a source of motivation for employees to work optimally. In giving rewards, sometimes employees do not match the results of their performance and without applying good calculations. For that we need a recommendation system to support employee performance appraisal to get rewards. One of the methods used is the Adaptive Neuro Fuzzy Inference System (ANFIS) method. This method was chosen because it is able to complete employee performance appraisals based on predetermined criteria and is used as a reference in giving rewards. The amount of data obtained and will be used is a number of 537 employee data which will be divided into two data, namely training data which functions as a model of 524 data and test data which functions to test the system of 13 data. The training set uses a regression algorithm to form an employee performance appraisal model. This model is a representation of knowledge that will be used to predict the reward status of operator and foreman level employees
Sistem Pendukung Keputusan Untuk Menentukan Customer Segment Pasar Salah Satu UMKM Di Bandung ( Foodendez ) Dengan Metode Decision Tree

Abstract Foodendez is a UMKM in Bandung which is well known for its products, namely Uthi &amp; Mies Krispi Patin Fish Skin which is a best seller product at Foodendez. This business has been running for about 5 years.In these 5 years Foodendez already has its own customer / market segment for the sale of this Foodendez product, however, this Foodendez has a problem predicting the customer / market segment for product sales from Foodendez, this is not suitable. expected expectations, because in analyzing / predicting customers / market segments still using a simple method or it can only be said to only use initial predictions by guessing roughly what estimates are known from the business plan or by other methods that make this Foodendez product get the wrong customer / segment that market It is expected that before hand or can be said to change and have an effect which makes Foodendez's turn over change insignificantly, and until recently Foodendez has not had a customer / market segment that is truly significant or as expected, for example is the sale of Krispi Patin Fish Skin that is predicted by the customer. the segment is more people &gt; 18 years old but it turns out that &lt; 18 years old people also like Krispi Patin Fish Skin products and offline and online sales where it is predicted that online sales will get a significant gain, which turns out to be more income in offline sales. So the authors provide a solution with the design / scheme of Decision Support Systems that will determine the customer segment and market segment which will later find out which market segment and customer segment are more significant and know which targets are suitable for products from Foodendez so that Foodendez UMKM can know what steps must be taken to set clear and significant targets by looking at the results of the percentage of enthusiasts and the turnover given by the Decision Tree method. The method of activities carried out is taken by collecting data with the interview method. The result of this activity is a Decision Support System design / scheme where the design / scheme is the result of determining a more detailed and significant customer segment / market segment and according to what the owner of Foodendez UMKM wants. Keywords: Decision Tree , Customer Segment , Market Segment , Foodendez , Uthi &amp; Mies
The Effect of In-Service Training Programs on the Professional Development of Public Elementary School Teachers

This study aimed to determine the effect of in-service training programs on the professional development of public elementary school teachers in Candelaria West District, Division of Quezon.The study used quantitative and qualitative research designs and applied a descriptive-correlational method.The results revealed that professional development in terms of content knowledge and pedagogy, learning environment, diversity of learners, curriculum and planning, assessment and reporting, community linkages and professional engagement, professional growth, and professional development are positively, highly, and significantly related to the in-service training programs' professionalism dimensions and pedagogical components.Likewise, the findings showed that teachers had significant improvements in content knowledge and its application within and across curriculum areas; strategies for promoting literacy and numeracy; strategies for developing critical and creative thinking as well as higher-order thinking skills; teaching and learning resources, including ICT; and learners' gender, needs, strengths, interests, and experiences after attending a number of INSETs.The result suggests teachers should attend INSET programs with a positive attitude and active participation to improve their knowledge, develop their skills, change their behavior and attitudes, and have the ability to perform tasks more efficiently and effectively.
Sistem Pakar Menentukan Gangguan Layanan Indihome Menggunakan Algoritma Best First Search Berbasis Web

Indihome users are growing rapidly. However, PT Telkom's indihome services are still experiencing various disturbances so that the quality of service is not optimal, with the large number of Indihome users, it is undeniable that there are technicians who understand one type of disturbance better than others and sometimes there are new disturbances that are not understood by many technicians. , technicians who do not understand even those who are familiar with certain disorders may make mistakes in making decisions in dealing with disturbances. This is what prompted the construction of an expert system that aims as a solution to help make it easier for technicians when an interruption occurs in the indihome service, the expert system allows technicians to make consistent decisions to avoid mistakes based on the knowledge of an expert, so that with the solutions provided, technicians are able to take action. what to do when a disturbance occurs and how to deal with the disturbance. The expert system used is the Best First Search algorithm method.
Learning Relational Causal Models with Cycles through Relational Acyclification

In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models. An expressive class of graphical models, relational causal models, can represent and reason about complex dynamic systems exhibiting such cycles or feedback loops. Existing cyclic causal discovery algorithms for learning causal models from observational data assume that the data instances are independent and identically distributed which makes them unsuitable for relational causal models. At the same time, causal discovery algorithms for relational causal models assume acyclicity. In this work, we examine the necessary and sufficient conditions under which a constraint-based relational causal discovery algorithm is sound and complete for cyclic relational causal models. We introduce relational acyclification, an operation specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models. We show that under the assumptions of relational acyclification and $\sigma$-faithfulness, the relational causal discovery algorithm RCD (Maier et al. 2013) is sound and complete for cyclic models. We present experimental results to support our claim.
Automating UAV Flight Readiness Approval using Goal-Directed Answer Set Programming

We present a novel application of Goal-Directed Answer Set Programming that digitizes the model aircraft operator's compliance verification against the Academy of Model Aircrafts (AMA) safety code. The AMA safety code regulates how AMA flyers operate Unmanned Aerial Vehicles (UAVs) for limited recreational purposes. Flying drones and their operators are subject to various rules before and after the operation of the aircraft to ensure safe flights. In this paper, we leverage Answer Set Programming to encode the AMA safety code and automate compliance checks. To check compliance, we use the s(CASP) which is a goal-directed ASP engine. By using s(CASP) the operators can easily check for violations and obtain a justification tree explaining the cause of the violations in human-readable natural language. Further, we implement an algorithm to help the operators obtain the minimal set of conditions that need to be satisfied in order to pass the compliance check. We develop a front-end questionnaire interface that accepts various conditions and use the backend s(CASP) engine to evaluate whether the conditions adhere to the regulations. We also leverage s(CASP) implemented in SWI-Prolog, where SWI-Prolog exposes the reasoning capabilities of s(CASP) as a REST service. To the best of our knowledge, this is the first application of ASP in the AMA and Avionics Compliance and Certification space.
Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders

The joint optimization of the reconstruction and classification error is a hard non convex problem, especially when a non linear mapping is utilized. In order to overcome this obstacle, a novel optimization strategy is proposed, in which a Convolutional Autoencoder for dimensionality reduction and a classifier composed by a Fully Connected Network, are combined to simultaneously produce supervised dimensionality reduction and predictions. It turned out that this methodology can also be greatly beneficial in enforcing explainability of deep learning architectures. Additionally, the resulting Latent Space, optimized for the classification task, can be utilized to improve traditional, interpretable classification algorithms. The experimental results, showed that the proposed methodology achieved competitive results against the state of the art deep learning methods, while being much more efficient in terms of parameter count. Finally, it was empirically justified that the proposed methodology introduces advanced explainability regarding, not only the data structure through the produced latent space, but also about the classification behaviour.
A Quantitative Assessment of Package Freshness in Linux Distributions

Linux users expect fresh packages in the official repositories of their distributions. Yet, due to philosophical divergences, the packages available in various distributions do not all have the same degree of freshness. Users therefore need to be informed as to those differences. Through quantitative empirical analyses, we assess and compare the freshness of 890 common packages in six mainstream Linux distributions. We find that at least one out of ten packages is outdated, but the proportion of outdated packages varies greatly between these distributions. Using the metrics of update delay and time lag, we find that the majority of packages are using versions less than 3 months behind the upstream in 5 of those 6 distributions. We contrast the user perception of package freshness with our analyses and order the considered distributions in terms of package freshness to help Linux users in choosing a distribution that most fits their needs and expectations.
The Relationship between Text Formality and Writing Quality: An Evaluation Based on Coh-Metrix

This study seeks to investigate the relationship between text formality and writing quality by quantifying text formality in EFL English writing. In order to conduct the research, 548 English argumentation compositions were evaluated in terms of the five Coh-Metrix text formality dimensions: narrativity, syntactic simplicity, word concreteness, referential coherence, and deep cohesion. Based on the study, there is only a weak relationship between text formality and writing quality, and some facets of text formality, such as narrativity, word concreteness, syntactic simplicity, and deep cohesion, have varying degrees of influence on writing quality and, consequently, on English writing scores. As per statistics, narrativity has a detrimental effect on writing quality; word concreteness has a significant positive correlation with writing quality; syntactic simplicity shows a relatively small negative correlation with writing quality; deep cohesion has a modestly negative correlation with writing quality; and referential coherence has no correlation with writing quality. It is intended that the findings of this study will have some bearing on how English argumentative writing is taught and learned.