Reviewerly/exHarmony huggingface dataset:
Contents Analysis of Integrated Thematic Book Elementary School Curriculum 2013 in Class V Theme 3

An analysis of a textbook determines how far the book is by the curriculum and competencies to be achieved. The textbook analysis aims to understand the effectiveness of learning schemes and approaches to understand what is needed in the learning process and curriculum development. This study uses qualitative methods with content analysis design. The data source used is an integrated thematic book for class V theme 3 Curriculum2013. The technique of collecting data is by carefully listening to the book and documentation. Data analysis used content analysis and semiotics to help a matrix that includes the variables to be analyzed. Data validity technique with repeated reading and scrutiny. The study results show that the revised 2017 edition of the Thematic Integrated Thematic textbooks published by the Ministry of Education and Culture in terms of content feasibility is categorized in the very feasible category. The book's contents already contain material that is by the competencies that students must achieve. The depth of the book includes spiritual attitudes, social knowledge, skills in each discourse, and examples that have been presented. The accuracy of the material in this book is very high. Nevertheless, the supporters of learning materials still give too few discourses, examples, and questions that can develop insight into diversity, national wisdom, and national integrity that can invite students to love Indonesian cultural diversity and practice it as
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many hyperparameters. Nevertheless, existing approaches are relatively expensive as they need to compute second-order derivatives and store a longer computational graph. This cost prevents scaling them to larger network architectures. We present EvoGrad, a new approach to meta-learning that draws upon evolutionary techniques to more efficiently compute hypergradients. EvoGrad estimates hypergradient with respect to hyperparameters without calculating second-order gradients, or storing a longer computational graph, leading to significant improvements in efficiency. We evaluate EvoGrad on two substantial recent meta-learning applications, namely cross-domain few-shot learning with feature-wise transformations and noisy label learning with MetaWeightNet. The results show that EvoGrad significantly improves efficiency and enables scaling meta-learning to bigger CNN architectures such as from ResNet18 to ResNet34.
Evaluasi Implementasi Tata Kelola Teknologi Informasi Berdasarkan Framework CobIT

Information technology support can improve the company's capabilities in added value creation, the best quality of service and efficient and effective implementation of the company's operations. Therefore, structured information technology governance ranging from planning to supervision so that the existence of information technology is able to support the company's success in achieving its business goals and ensure efficiency, cost reduction and increased control over Information Technology investment. PT. Krakatau Steel (Persero), Tbk has implemented information technology governance and has conducted an audit using the CobIT framework. This research aims to evaluate the implementation of Technology Governance in PT. Krakatau Steel (Persero), Tbk is seen in the Framework of CobIT with qualitative descriptive data analysis methods. This research was conducted by observation and by conducting open interviews to related parties in the BE, IT / IS and MS. Secondary data in the form of CobIT mapping results ranging from business objectives to IT processes from the BE, IT / IS and MS divisions then continued by the author by conducting an AUDIT of IT processing to obtain primary data. The results showed that there were still 6 (six) weaknesses that had not been done by the BE, IT / IS and MS divisions in IT Process PO2, PO5, PO8, PO9, PO10 and DS6 and given several recommendations for improvement
LibGuides: Musical Instrument Selection Guide: Which Instrument?

There are five main families of musical instruments. These pages will provide information about each instrument group and provide corresponding examples.
Numerical Composition of Differential Privacy

We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of privacy loss random variables to quantify the privacy loss of DP algorithms. The running time and memory needed for our algorithm to approximate the privacy curve of a DP algorithm composed with itself $k$ times is $\tilde{O}(\sqrt{k})$. This improves over the best prior method by Koskela et al. (2020) which requires $\tilde{\Omega}(k^{1.5})$ running time. We demonstrate the utility of our algorithm by accurately computing the privacy loss of DP-SGD algorithm of Abadi et al. (2016) and showing that our algorithm speeds up the privacy computations by a few orders of magnitude compared to prior work, while maintaining similar accuracy.
Módulo de inferencia difuso con base de conocimientos variable sobre hardware reconfigurable

This paper presents the development of a fuzzy inference module (FIM) implemented on reconfigurable hardware with the ability to dynamically modify its knowledge base. The original FIM is designed using the fuzzy systems development environment Xfuzzy, which allows the generation of VHDL hardware description language code for the FIM architecture. Subsequently, the VHDL code is modified to add ports with the address, data and read / write control signals to the antecedents and rule memories that contain the knowledge base. The modified FIM is encapsulated in an intellectual property module following two possible options, making the corresponding interconnections in each case, so that these memories can be accessed through the added port from a processing system embedded in the same device, enabling the modification of its contents in operation time. The implementations were carried out on two types of reconfigurable devices: a Spartan-3E1600 FPGA, using a processing system based on the Microblaze softcore and the ISE/EDK development environment; as well as on a SoC-FPGA Zynq-7Z010, using its hardcore processing system based on ARM and the Vivado development environment, checking the dynamic modification of the FIM knowledge base. The carried out modifications make it easy to fine-tune the knowledge base during the development stage of a hybrid hardware/software fuzzy controller as well as the implementation of an adaptive fuzzy controller.
An End-to-End Mutual Enhancement Network Toward Image Compression and Semantic Segmentation

Image compression is to compress image data without compromising human vision feeling. However, the information loss through the image compression process may influence the following machine vision tasks, such as object detection and semantic segmentation. How to jointly consider the human vision and the machine vision to compress images for human and machine vision tasks is still an open problem. In this paper, we provide a multi-task framework for image compression and semantic segmentation. More specifically, an end-to-end mutual enhancement network is designed to efficiently compress the given image, and simultaneously segment the semantic information. Firstly, a uniform feature learning strategy is adopted to jointly learn the features for image compression and semantic segmentation in the encoder. Moreover, a multi-scale aggregation module in the encoder is employed to enhance the semantic features. Then, by transmitting the quantified features, both the decompressed image features and the learned semantic features can be reconstructed. Finally, we decode this information for the image compression task and the semantic segmentation task. On one hand, we can utilize the decompressed semantic features to implement semantic segmentation in the decoder. On the other hand, the quality of the decompressed image can be further improved depending on the obtained semantic segmentation map. Experimental results prove that our framework is effective to simultaneously support image compression and semantic segmentation, both in the subjective and objective evaluation.
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations

Distinguishing between cause and effect using time series observational data is a major challenge in many scientific fields. A new perspective has been provided based on the principle of Independence of Causal Mechanisms (ICM), leading to the Spectral Independence Criterion (SIC), postulating that the power spectral density (PSD) of the cause time series is uncorrelated with the squared modulus of the frequency response of the filter generating the effect. Since SIC rests on methods and assumptions in stark contrast with most causal discovery methods for time series, it raises questions regarding what theoretical grounds justify its use. In this paper, we provide answers covering several key aspects. After providing an information theoretic interpretation of SIC, we present an identifiability result that sheds light on the context for which this approach is expected to perform well. We further demonstrate the robustness of SIC to downsampling - an obstacle that can spoil Granger-based inference. Finally, an invariance perspective allows to explore the limitations of the spectral independence assumption and how to generalize it. Overall, these results support the postulate of Spectral Independence is a well grounded leading principle for causal inference based on empirical time series.
SAT-Based Approach for Learning Optimal Decision Trees with Non-Binary Features

Decision trees are a popular classification model in machine learning due to their interpretability and performance. Traditionally, decision-tree classifiers are constructed using greedy heuristic algorithms, however these algorithms do not provide guarantees on the quality of the resultant trees. Instead, a recent line of work has studied the use of exact optimization approaches for constructing optimal decision trees. Most of the recent approaches that employ exact optimization are designed for datasets with binary features. While numeric and categorical features can be transformed to binary features, this transformation can introduce a large number of binary features and may not be efficient in practice. In this work, we present a novel SAT-based encoding for decision trees that supports non-binary features and demonstrate how it can be used to solve two well-studied variants of the optimal decision tree problem. We perform an extensive empirical analysis that shows our approach obtains superior performance and is often an order of magnitude faster than the current state-of-the-art exact techniques on non-binary datasets.
Visualizing The Implicit Model Selection Tradeoff

The recent rise of Machine Learning (ML) has been leveraged by operations management researchers to provide new solutions to operational problems. As other ML applications, these solutions rely on model selection, which is typically done by evaluating certain metrics on models separately and selecting the model whose evaluations (accuracy-related loss and/or some interpretability measures) are optimal. However, empirical evidence suggests that often in practice multiple models attain competitive results. Therefore, while models' overall performance could be similar, the models could operate quite differently. This results in an implicit tradeoff in models' performance throughout the feature space whom resolving requires new model selection tools.This paper aims to study methods for comparing predictive models in an interpretable manner in order to expose this tradeoff, which characterizes model selection problems. To this end, we propose various methods that synthesize ideas from supervised learning, unsupervised learning, dimensionality reduction, and visualization, and demonstrate how they can be used to inform the model selection process. Using various datasets and a simple Python interface that we developed, we demonstrate how practitioners and researchers could benefit from applying these approaches to better understand the broader impact of their model selection choices.
A MASTERY STUDY OF THE EXPOSITORY WRITING OF THE THIRD YEAR STUDENTS OF SMP NEGERI NAIOLA

This study analyzed the mastery level of students in the third year students of SMP Negeri Naiola. The aim of the study is to find out the mastery level of students about Expository Writing. The method used descriptive qualitative method. Therefore, this research used writing test. After collecting the data, the writer analyzed them based on the three generic structure of Expository Writing text namely: Thesis, Argument, and Recommendation/Reiteration. The result of data analysis shows that the students have mastered expository text. It is based on the data that their average score is 83 and categorized as Excellent. This means that the students have ability in Expository Writing. However, the students still found difficulties in in mastering argument of the components of personal expository text. The data shows that their total number of wrong answers is 48. To overcome the students’ difficulties in expository writing, English teacher have to guide and motivate the students to master expository writing based on the generic structure of the text.
Trigonometrical Verification and Authentication of Handwritten Signatures: A new Forensic Approach

Handwritten Signatures fall under the behavioral biometric parameters of an individual. It is proof of identity and intent practiced since many years. Signature verification is routine practice in forensic questioned document examination and hence, it is important to consider the real-world picture of handwritten signature verification. Manual signature verification by skilled forensic questioned document experts is the most practiced method for signature comparison and verification. The highest error rates in signature verification are found with laypeople, higher than for computers, which in turn make more errors than experts. Identification of an individual signature using behavioural biometrics is becoming prevalent and it includes both online and offline signature verification. Offline signature verification focuses to deal with static features of signature which are length, appearance or pictorial effect, height, and other measures or specific features of different strokes in signature.This paper is attempting to suggest an innovative approach for handwritten signature comparison, verification and authentication by using apex angle value calculated with simple trigonometric rule. It is observed that the value of apex angle has better outcome in comparison and verification than that of measuring lengths of signature or types of strokes. As a signature is pure outcome of neuromuscular activity, the hypothesis was suggestive of the fact that there must be a ready mechanism or learned program in individuals’ brain about the signature they have practiced for years to establish their presence or to depict their identity marker. The study suggests that value of apex angle of individual’s signature can be considered as parameter for examination of signature exhibit in handwritten signature authentication to establish common or different authorship. Considering this fact, we have innovated the approach of using the calculated value of apex angle as one of the parameters and characteristic features of signature comparison and verification. We further plan to experiment whether it can be useful with random and disguised forgeries or simulated signatures. This paper focuses on introducing more reliable techniques and supporting parameters for signature verification which can be easily introduced in current signature verification practices by questioned document examiners or subject experts.
Pechini Sol-Gel Synthesis and Characterization of NiWO4/W5O14/WO3 Nanocomposite for Photocatalytic Desulfurization of Thiophene

One of the most important problems in the oil industry is the presence of sulfur and sulfur compounds in crude oil. Sulfur compounds in crude oil can have detrimental effects on the environment, equipment, catalysts, and end products. One of the most important goals of researchers in recent years is to sweeten petroleum products from these compounds. This study aims to use NiWO4/W5O14/WO3 composite nanostructure to solve this problem using the photocatalytic oxidative desulfurization method. This composite nanostructure has been synthesized by Pechini sol-gel method with high purity at low temperature and examined by XRD, EDS, FESEM, and FT-IR analysis. Due to the presence of WO3 in the composition, the bandgap was greatly reduced and the efficiency was increased. As a result, the nanostructure could degrade more than 73% of the sulfur in thiophene in 180 min under visible light. By optimizing the amount of photocatalyst and irradiation time, the efficiency can be improved.
Standards for Knowledge Graphs in the Financial Sector

Standards are a necessary adjunct to many kinds of new technology development. This paper explores an emerging new kind of standard in which business meaning is standardized using formal ontology. Some issues are identified in terms of the characteristics of different ontologies and how these may apply to different intended uses. By way of illustration, an account of the origins, history and intended uses of an industry standard called the Financial Industry Business Ontology (FIBO) is given, along with a summary of other financial industry standards. It is suggested that an understanding of the ontology characterizations described here may be instructive in developing industry standards at the level of semantics and in making use of these standards.
An Ensemble Denoiser Based on Generative Adversarial Networks to Eliminate Adversarial Perturbations

Deep neural networks (DNNs) have been applied in various machine learning tasks with the success of deep learning technologies. However, they are surprisingly vulnerable to adversarial examples, which can easily fool deep neural networks. Due to this drawback of deep neural networks, numerous methods have been proposed to eliminate the effect of adversarial examples. Although they do play a significant role in protecting deep neural networks, most of them all have one flaw in common. They are only effective for certain types of adversarial examples. This paper proposes an ensemble denoiser based on generative adversarial networks (GANs) to protect deep neural networks. This proposed method aims to remove the effect of multiple types of adversarial examples before they are fed into deep neural networks. Therefore, it is model-independent and cannot modify deep neural networks&amp;rsquo; parameters. We employ a generative adversarial network for this proposed method to learn multiple mappings between adversarial examples and benign examples. Each mapping behaves differently for different types of adversarial examples. Therefore, we integrate these mappings as the ultimate method to defend against multiple types of adversarial examples. Experiments are conducted on the MNIST and CIFAR10 datasets. We compare this proposed method with several existing excellent methods. Results show that this proposed method achieves better performance than other methods when defending against multiple types of adversarial examples. The code is available at &lt;a href="https://github.com/Afreadyang/ensemble-ape-gan"&gt;https://github.com/Afreadyang/ensemble-ape-gan&lt;/a&gt;
Analysis of Flood Control Plan in Cibitung Kertagraha Housing, Bekasi District

The change in the function of land to housing can cause a decrease in the level of the water absorption zone that occurs in the area and can cause inundation or even flooding. Housing can be categorized as good, that is, it must have drainage facilities that can drain rainwater quickly and not cause stagnation of water even though the rainfall is very high. Therefore, the right solution to tackle standing water, drainage problems, and flooding in Kertagraha housing is to create a retention pond because in the kertagraha housing there is no final storage system that is suitable for dealing with flooding,The location of this research is located in Sukarukun Village, Sukatani District, Cibitung, Bekasi Regency with the category of housing area. In this study, a hydrological analysis was carried out with rainfall data from the Rain Post Station located in Sukatani and the TRMM Rain Post located in Cikarang within a 10-year return period starting from 2010-2019, and the planned flood discharge data were obtained. Furthermore, hydraulic analysis is carried out to determine the flood discharge capacity in the existing drainage channels and to plan the volume of the storage pool. After that, the retention pond design planning is carried out.In the calculation analysis, it is found that the planned rain discharge value in the next 10 years is 7.393 m³ / second using the Haspers method. The total capacity of the storage pool is 12,385 m³, with the dimensions of the retention pond, namely P = 70m, L = 20m, T = 10meter. With a concentration time of 1675 seconds. Based on the analysis carried out and there is sufficient land, this retention pond is the best solution to overcome the flood problem in Kertagraha housing.
Imagine Networks

In this paper, we introduce an Imagine Network that can simulate itselfthrough graph tree neural networks. Among the graph tree neural networksmodels, association, deduction, and memory networks are learned, and a networkis created by combining the discriminator and reinforcement learning models.This model can learn various datasets or data samples generated in environmentsand generate new data samples.
Selection and Participation of the Team of Bulgaria in the XXIV JBMO 2020

The article contained an overview of the selection and preparation of the team of Bulgaria in the Junior Balkan Mathematical Olympiad 2020, the contest itself and the presentation of the team. The included materials can be useful in the preparation for math competitions in the junior high school.
A Spatio-Temporal Identity Verification Method for Person-Action Instance Search in Movies

As one of the challenging problems in video search, Person-Action Instance Search (P-A INS) aims to retrieve shots with a specific person carrying out a specific action from massive amounts of video shots. Most existing methods conduct person INS and action INS separately to compute the initial person and action ranking scores, which will be directly fused to generate the final ranking list. However, direct aggregation of two individual INS scores ignores spatial relationships of person and action, thus cannot guarantee their identity consistency and cause identity inconsistency problem (IIP). To address IIP, we propose a simple spatio-temporal identity verification method. Specifically, in the spatial dimension, we propose an identity consistency verification (ICV) step to revise the direct fusion score of person INS and action INS. Moreover, in the temporal dimension, we propose a double-temporal extension (DTE) operation to further improve P-A INS results. The proposed method is evaluated on the large-scale NIST TRECVID INS 2019–2021 tasks, and the experimental results show that it can effectively mitigate the IIP, and its performance surpasses that of the champion team in 2019 INS task and the second place teams in both 2020 and 2021 INS tasks.