Reviewerly/exHarmony huggingface dataset:
The creation background of sijo and poetic orientation of Yong-A, Park Yong-cheol

Yong-A Park Yong-cheol is an important figure who exerted a substantial influence on the formation and activities of the Poet and Literature School.&#x0D; Therefore, the discussion of Park Yong-cheol has been focused on the realm of modern poets or pure poetry. Previous studies with this trend played a certain role in revealing and deepening the aspect of Park Yong-cheol as a modern poet.&#x0D; However, on the other hand, they also had limitations that inhibit the scalability of the study on Park Yong-cheol's and narrow the scope of discussion.&#x0D; In this study, the author paid attention to the fact that Park Yong-cheol was a modern poet as well as a sijo poet, and looked into the background of sijo creation and his poetic orientation. To discuss this, the author first looked at the overall status of Park Yong-cheol’s sijo. Although the previous studies recognized that Park Yong-cheol created sijo, the total volume of the work and their recording location were not organized. Due to this, the prejudice that Park Yong-cheol created sijo as a study was formed. As a result of the discussion in this paper, Park Yong-cheol created about 95 sijos, but considering that the number of his creative free poems is around 70, it can be inferred that Park Yong-cheol's sijo creations were intentional.&#x0D; Park Yong-cheol's background in creating sijo can be attributed to his relationship with Widang Jeong In-bo when he was studying at Yeonhui University. In addition to this, it is worth paying attention to the letters sent and received while dating her wife, Lim Jeong-hee. Referring to the contents of these letters, it can be seen that the direct factor in Park Yong-cheol's creation of sijo was that they were created to express his subjective feelings. This was basically what Park Yong-cheol had when he wrote poetry, and it was also applied when he created free poetry. Through this, it can be seen that Park Yong-cheol recognized sijo not as a song, but as a style of poetry expressing his emotions.&#x0D; Park Yong-cheol focused on expressing individual subjective feelings when creating sijo and free poetry. Then, what is the biggest reference point that distinguishes Park Yong-cheol's sijo from free poetry? The reference point is that creation is based on personal experience. Park Yong-cheol's sijo presents an anecdote that he experienced by himself. This experience is closely related to specific poetic subjects such as his wife, Lim Jeong-hee, his close friends, Yeom Hyeong-woo and Yoon Shim-deok, his uncle, himself, and the baby to be born.&#x0D; This is a rare point to find in Park Yong-cheol's free poetry. In other words, it is conceivable that Park Yong-cheol preferred sijo as a form of expressing the emotions he felt in his experiences.&#x0D; Through the discussion of this study, the author thinks that this study made a certain contribution to studying Park Yong-cheol’s sijo, in that it expanded the field of Park Yong-cheol study and laid a stepping stone for studying Park Yong-cheol’s sijo
Kinerja Inert Gas Generator Untuk Mendapatkan Oksigen Konten 3% Pada Gas Lembam

Inert Gas System is a machine that functions as a security system to prevent explosions on tankers. Explosion prevention is carried out by introducing inert gas into the cargo tank with the aim of reducing oxygen levels in the cargo tank atmosphere so as to prevent the formation of a flammable gas mixture. Inert gas contains very little oxygen, namely 1-2% by volume. The device that produces the inert gas is called the Inert Gas Generator (IGG). The purpose of this study was to determine the factors that cause high oxygen content contained in the inert gas and the efforts made related to the factors mentioned so that the inert gas generator can work optimally. The research method used by the author is descriptive qualitative with Fishbone data analysis techniques and SWOT analysis. The results of the study stated that the factors causing oxygen content of more than 3% were dirty fuel filter, too much combustion air supply, oxygen analyzer that was rarely calibrated, flow sampling was too large, fuel quality was not good, lack of crew knowledge. to PMS (Plan Maintenance System) IGG. Efforts have been made to clean the fuel pump filter, close the air capacity valve a little, calibrate the oxygen analyzer according to the PMS schedule, reduce the sampling valve flow into the oxygen analyzer, add fuel oil treatment (additives) to the fuel, pay attention to the IGG maintenance schedule according to PMS, restore the maintenance implementation system based on the PMS inert gas generator schedule.
Design of Supervision-Scalable Learning Systems: Methodology and Performance Benchmarking

The design of robust learning systems that offer stable performance under a wide range of supervision degrees is investigated in this work. We choose the image classification problem as an illustrative example and focus on the design of modularized systems that consist of three learning modules: representation learning, feature learning and decision learning. We discuss ways to adjust each module so that the design is robust with respect to different training sample numbers. Based on these ideas, we propose two families of learning systems. One adopts the classical histogram of oriented gradients (HOG) features while the other uses successive-subspace-learning (SSL) features. We test their performance against LeNet-5, which is an end-to-end optimized neural network, for MNIST and Fashion-MNIST datasets. The number of training samples per image class goes from the extremely weak supervision condition (i.e., 1 labeled sample per class) to the strong supervision condition (i.e., 4096 labeled sample per class) with gradual transition in between (i.e., $2^n$, $n=0, 1, \cdots, 12$). Experimental results show that the two families of modularized learning systems have more robust performance than LeNet-5. They both outperform LeNet-5 by a large margin for small $n$ and have performance comparable with that of LeNet-5 for large $n$.
Is my transaction done yet? An empirical study of transaction processing times in the Ethereum Blockchain Platform

Ethereum is one of the most popular platforms for the development of blockchain-powered applications. These applications are known as Dapps. When engineering Dapps, developers need to translate requests captured in the front-end of their application into one or more smart contract transactions. Developers need to pay for these transactions and, the more they pay (i.e., the higher the gas price), the faster the transaction is likely to be processed. Therefore developers need to optimize the balance between cost (transaction fees) and user experience (transaction processing times). Online services have been developed to provide transaction issuers (e.g., Dapp developers) with an estimate of how long transactions will take to be processed given a certain gas price. These estimation services are crucial in the Ethereum domain and several popular wallets such as Metamask rely on them. However, their accuracy has not been empirically investigated so far. In this paper, we quantify the transaction processing times in Ethereum, investigate the relationship between processing times and gas prices, and determine the accuracy of state-of-the-practice estimation services. We find that transactions are processed in a median of 57s and that 90% of the transactions are processed within 8m. The higher gas prices result in faster transaction processing times with diminishing returns. In particular, we observe no practical difference in processing time between expensive and very expensive transactions. In terms of accuracy of processing time estimation services, we note that they are equivalent. However, when stratifying transactions by gas prices, Etherscan's Gas Tracker is the most accurate estimation service for very cheap and cheap transaction. EthGasStation's Gas Price API, in turn, is the most accurate estimation service for regular, expensive, and very expensive transactions.
Improving Localization for Semi-Supervised Object Detection

Nowadays, Semi-Supervised Object Detection (SSOD) is a hot topic, since, while it is rather easy to collect images for creating a new dataset, labeling them is still an expensive and time-consuming task. One of the successful methods to take advantage of raw images on a Semi-Supervised Learning (SSL) setting is the Mean Teacher technique, where the operations of pseudo-labeling by the Teacher and the Knowledge Transfer from the Student to the Teacher take place simultaneously. However, the pseudo-labeling by thresholding is not the best solution since the confidence value is not strictly related to the prediction uncertainty, not permitting to safely filter predictions. In this paper, we introduce an additional classification task for bounding box localization to improve the filtering of the predicted bounding boxes and obtain higher quality on Student training. Furthermore, we empirically prove that bounding box regression on the unsupervised part can equally contribute to the training as much as category classification. Our experiments show that our IL-net (Improving Localization net) increases SSOD performance by 1.14% AP on COCO dataset in limited-annotation regime. The code is available at https://github.com/IMPLabUniPr/unbiased-teacher/tree/ilnet
New family of CV states of definite parity and their metrological power

Abstract We introduce a family of new continuous variable (CV) states of definite parity originating from even single-mode squeezed vacuum (SMSV) state by subtracting an arbitrary number of photons from it. A beam splitter (BS) with arbitrary transmittance and reflectance parameters serves as a hub for redirecting input photons in an indistinguishable manner to the output and measuring modes followed by probabilistic measurement, thereby converting the initial SMSV photon distribution into new one after we know the number of registered photons in auхilliary mode. Depending on the parity of the subtracted photons, the family of the generated states is divided into two subfamilies, that is, into even and odd according to the parity of the Fock state subtracted. The family of the CV states is determined solely by one SMSV parameter, which inevitably decreases during their implementation. The algorithm of the quantum engineering can generate macroscopic CV states with a larger average number of photons (say, 4000 and even more) and quantum Fisher information of observable measuring the number of photons many times more (say, from tens of times for small practical values of the squeezing ratio to ≈4 for extremely high squeezing amplitude) than one of the original SMSV. The potential of the new family of the CV states of a certain parity, to which original SMSV, no doubt, belongs, can become decisive for a new push to implementation of optical quantum metrology protocols.
Language Politeness Patterns in Information Services at Zion High School Makassar

This study aims to reveal the politeness pattern of expressive and assertive speech acts in information services at Zion High School. This research is a qualitative research using speech act theory and language politeness theory. The data of this study consisted of speech data and field notes. Data was collected through recording, observation, and transcription techniques. Data analysis was carried out through four main procedures, namely: data collection, data reduction, data presentation, and inference/verification. Based on data analysis, speech act politeness patterns in information services at Zion High School include code mixing and pleasantries.
Rethinking Unsupervised Neural Superpixel Segmentation

Recently, the concept of unsupervised learning for superpixel segmentation via CNNs has been studied. Essentially, such methods generate superpixels by convolutional neural network (CNN) employed on a single image, and such CNNs are trained without any labels or further information. Thus, such approach relies on the incorporation of priors, typically by designing an objective function that guides the solution towards a meaningful superpixel segmentation. In this paper we propose three key elements to improve the efficacy of such networks: (i) the similarity of the \emph{soft} superpixelated image compared to the input image, (ii) the enhancement and consideration of object edges and boundaries and (iii) a modified architecture based on atrous convolution, which allow for a wider field of view, functioning as a multi-scale component in our network. By experimenting with the BSDS500 dataset, we find evidence to the significance of our proposal, both qualitatively and quantitatively.
Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency

Integrating multi-modal data to promote medical image analysis has recently gained great attention. This paper presents a novel scheme to learn the mutual benefits of different modalities to achieve better segmentation results for unpaired multi-modal medical images. Our approach tackles two critical issues of this task from a practical perspective: (1) how to effectively learn the semantic consistencies of various modalities (e.g., CT and MRI), and (2) how to leverage the above consistencies to regularize the network learning while preserving its simplicity. To address (1), we leverage a carefully designed External Attention Module (EAM) to align semantic class representations and their correlations of different modalities. To solve (2), the proposed EAM is designed as an external plug-and-play one, which can be discarded once the model is optimized. We have demonstrated the effectiveness of the proposed method on two medical image segmentation scenarios: (1) cardiac structure segmentation, and (2) abdominal multi-organ segmentation. Extensive results show that the proposed method outperforms its counterparts by a wide margin.
Revisiting lp-constrained Softmax Loss: A Comprehensive Study

Normalization is a vital process for any machine learning task as it controls the properties of data and affects model performance at large. The impact of particular forms of normalization, however, has so far been investigated in limited domain-specific classification tasks and not in a general fashion. Motivated by the lack of such a comprehensive study, in this paper we investigate the performance of lp-constrained softmax loss classifiers across different norm orders, magnitudes, and data dimensions in both proof-of-concept classification problems and real-world popular image classification tasks. Experimental results suggest collectively that lp-constrained softmax loss classifiers not only can achieve more accurate classification results but, at the same time, appear to be less prone to overfitting. The core findings hold across the three popular deep learning architectures tested and eight datasets examined, and suggest that lp normalization is a recommended data representation practice for image classification in terms of performance and convergence, and against overfitting.
Schedule Implementing the Modified Euclidean Distance Method in the Course Planning of the USN Kolaka Information Systems Curriculum

Scheduling is needed to anticipate the clash of lecture hours for lecturers in teaching. Some things that need to be considered when arranging a lecture schedule are lecturers, rooms, courses, hours, and days. In this study, the scheduling system uses the Euclidean Distance method by modifying the algorithm. The aim of this study is to prove the Euclidean distance method in the course planning system in universities. In general, to obtain data that will be used in the scheduling system, observations, interviews and bibliographic studies are carried out. From the research results, the lecture scheduling system using the Euclidean Distance method can be used in the scheduling system, especially lecture scheduling. This is proven, that the lecture scheduling system using the Euclidean Distance method does not have a schedule for lecturers who teach more than one course at the same time (collide).
Sensitivity of Average Precision to Bounding Box Perturbations

Object detection is a fundamental vision task. It has been highly researched in academia and has been widely adopted in industry. Average Precision (AP) is the standard score for evaluating object detectors. Our understanding of the subtleties of this score, however, is limited. Here, we quantify the sensitivity of AP to bounding box perturbations and show that AP is very sensitive to small translations. Only one pixel shift is enough to drop the mAP of a model by 8.4%. The mAP drop over small objects with only one pixel shift is 23.1%. The corresponding numbers when ground-truth (GT) boxes are used as predictions are 23% and 41.7%, respectively. These results explain why achieving higher mAP becomes increasingly harder as models get better. We also investigate the effect of box scaling on AP. Code and data is available at https://github.com/aliborji/AP_Box_Perturbation.
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving

\emph{Ex ante} correlation is becoming the mainstream approach for \emph{sequential adversarial team games}, where a team of players faces another team in a zero-sum game. It is known that team members' asymmetric information makes both equilibrium computation \textsf{APX}-hard and team's strategies not directly representable on the game tree. This latter issue prevents the adoption of successful tools for huge 2-player zero-sum games such as, \emph{e.g.}, abstractions, no-regret learning, and subgame solving. This work shows that we can recover from this weakness by bridging the gap between sequential adversarial team games and 2-player games. In particular, we propose a new, suitable game representation that we call \emph{team-public-information}, in which a team is represented as a single coordinator who only knows information common to the whole team and prescribes to each member an action for any possible private state. The resulting representation is highly \emph{explainable}, being a 2-player tree in which the team's strategies are behavioral with a direct interpretation and more expressive than the original extensive form when designing abstractions. Furthermore, we prove payoff equivalence of our representation, and we provide techniques that, starting directly from the extensive form, generate dramatically more compact representations without information loss. Finally, we experimentally evaluate our techniques when applied to a standard testbed, comparing their performance with the current state of the art.
Prediction of Academic Formulaic Language based on Multi-feature Fusion

&lt;p&gt;Academic formulaic language is multi-word combinations with specific functions and semantics, which are important to improve the idiomaticity, fluency and logic of machine translation, intelligent question answering, automatic summarization, etc. In order to narrow the search range of the corpus and extract academic formulaic language more efficiently, this paper proposes a prediction model of academic formulaic language based on multi-feature fusion. The semantic features and part-of-speech features of the academic formulaic language are extracted separately, and then the late fusion method is used to learn multiple features and predict whether formulaic language is included in the sentence. Experimental results show that the late fusion method based on part-of-speech features and semantic features has the best predictive effect among the four fusion methods, which lays the foundation for further efficient recognition of academic formulaic language.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
All you need is feedback: Communication with block attention feedback codes

Deep learning based channel code designs have recently gained interest as an alternative to conventional coding algorithms, particularly for channels for which existing codes do not provide effective solutions. Communication over a feedback channel is one such problem, for which promising results have recently been obtained by employing various deep learning architectures. In this paper, we introduce a novel learning-aided code design for feedback channels, called generalized block attention feedback (GBAF) codes, which i) employs a modular architecture that can be implemented using different neural network architectures; ii) provides order-of-magnitude improvements in the probability of error compared to existing designs; and iii) can transmit at desired code rates.
Smartformer: An Intelligent Model Compression Framework for Transformer

Abstract Transformer, as one of the latest popular deep neural networks (DNNs), has achieved outstanding performance in various areas. However, this model usually requires massive amounts of parameters to fit. Over-parameterization not only brings storage challenges in a resource-limited setting but also inevitably results in the model over-fitting. Even though literature works introduced several ways to reduce the parameter size of Transformers, none of them have focused on addressing the over-parameterization issue by concurrently considering the following three objectives: preserving the model architecture, maintaining the model performance, and reducing the model complexity (number of parameters) to improve its convergence property. In this study, we propose an intelligent model compression framework, Smartformer, by incorporating reinforcement learning and CP-decomposition techniques. This framework has two advantages: 1) from the perspective of convenience, researchers can directly exploit this framework to train their designed Transformer models as usual without fine-tuning the original model training hyperparameters; 2) from the perspective of effectiveness, researchers can simultaneously fulfill the aforementioned three objectives by using this framework. We conduct two basic experiments and three practical experiments to demonstrate the proposed framework using various datasets and existing Transformer models. The results demonstrate that the Smartformer is able to improve the existing Transformers regarding the model performance and model complexity by intelligently incorporating the model decomposition and compression in the training process.
TAPHSIR: Towards AnaPHoric Ambiguity Detection and ReSolution In Requirements

We introduce TAPHSIR, a tool for anaphoric ambiguity detection and anaphora resolution in requirements. TAPHSIR facilities reviewing the use of pronouns in a requirements specification and revising those pronouns that can lead to misunderstandings during the development process. To this end, TAPHSIR detects the requirements which have potential anaphoric ambiguity and further attempts interpreting anaphora occurrences automatically. TAPHSIR employs a hybrid solution composed of an ambiguity detection solution based on machine learning and an anaphora resolution solution based on a variant of the BERT language model. Given a requirements specification, TAPHSIR decides for each pronoun occurrence in the specification whether the pronoun is ambiguous or unambiguous, and further provides an automatic interpretation for the pronoun. The output generated by TAPHSIR can be easily reviewed and validated by requirements engineers. TAPHSIR is publicly available on Zenodo (DOI: 10.5281/zenodo.5902117).