Reviewerly/exHarmony huggingface dataset:
Compositional Generalization in Multilingual Semantic Parsing over Wikidata

Semantic parsing (SP) allows humans to leverage vast knowledge resources through natural interaction. However, parsers are mostly designed for and evaluated on English resources, such as CFQ (Keysers et al., 2020), the current standard benchmark based on English data generated from grammar rules and oriented towards Freebase, an outdated knowledge base. We propose a method for creating a multilingual, parallel dataset of question-query pairs, grounded in Wikidata. We introduce such a dataset, which we call Multilingual Compositional Wikidata Questions (MCWQ), and use it to analyze the compositional generalization of semantic parsers in Hebrew, Kannada, Chinese and English. While within-language generalization is comparable across languages, experiments on zero-shot cross-lingual transfer demonstrate that cross-lingual compositional generalization fails, even with state-of-the-art pretrained multilingual encoders. Furthermore, our methodology, dataset and results will facilitate future research on SP in more realistic and diverse settings than has been possible with existing resources.
ASSOCIATION OF GLYCOSYLLATED HEMOGLOBIN (HbA1C) LEVELS TO THE SEVERITY OF DIABETIC FOOT ULCER IN TYPE 2 DIABETES MELLITUS PATIENTS IN RSUP. H. ADAM MALIK MEDAN

Abstrak&#x0D; Introduction: Diabetic ulcers with neuropathy require amputation or time to fully heal. The worse blood sugar control, it will worsen the condition of complications such as diabetic ulcers, diabetic neuropathy, and other complications. Wound healing in diabetic ulcers is influenced by controlled HbA1c levels.&#x0D; Methods: This study is an observational analytic study with a retrospective design used in this study with secondary data. This study is analytical because it aims to obtain the correlation of HbA1c with the severity of diabetic ulcers in patients with diabetes mellitus at H. Adam Malik Hospital.&#x0D; Results: The average age of the patients was 62 with the majority of respondents being male. The dominance of respondents came with Grade IV as many as 17 people, with uncontrolled HbA1c levels as many as 39 people. Using Spearman Corelation test, the value of r = 0.735, 0.736, and 0.785, respecttively for the corelation between HbA1c, Hb and ESR levels with the severity of diabetic foot ulcers ,with a value (p = 0.0001).&#x0D; Conclusion: We found a strong correlation and a significant relationship between HbA1c, Hb, and ESR levels with the severity of diabetic foot ulcers.
Peran taman bacaan hendra sebagai fungsi pendidikan untuk masyarakat

This study discusses the role of Community Reading Gardens as an educational function for the community. The purpose of this study is to find out how the role of Hendra Reading Gardens is in providing learning resources, information sources and educational recreation for the community. The method used is a qualitative research method with a case study approach, data collection techniques are carried out by conducting interviews, library research, observation and documentation. This study uses the Guidebook for Norms, Standards, Procedures, and Criteria for Community Reading Gardens in 2013, in which Community Reading Gardens have an important role as an educational function. The informants in this study consisted of 8 (eight) people who were interviewed, two people were managers and six people were members of the Hendra Reading Gardens. The results of this study indicate that the role of the Hendra Reading Gardens in Cihapit District, Bandung City is very instrumental in the community by providing learning resources, information sources, and recreation, and the community can develop learning potential through the provision of a collection of reading materials held by Hendra Reading Park. The conclusion that can be drawn from this research is that Hendra Reading Park is very helpful in empowering the community to like reading, giving a positive impact on the surrounding environment in advancing the function of education for the community.
Real world Gender and Age Prediction using CNN

The real world gender and age prediction is opined to develop to deal multitude of real world problems and scenarios where in there is an exceedingly great usage for the validation of a person and to authenticate the identity of a person. This project would be developed using a renowned deep learning algorithm – CNN ( Convolutional Neural Network ) to enhance the precision rate. To this end, we design a simple convolutional network architecture that can be used and deployed using adequate number of pooling layers so as to get sterling precision to the extent possible, training the model using publicly available dataset. Keywords deep learning, convolutional neural network, computer vision, prediction.
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors

In this paper, we explore the capacity of a language model-based method for grammatical error detection in detail. We first show that 5 to 10% of training data are enough for a BERT-based error detection method to achieve performance equivalent to a non-language model-based method can achieve with the full training data; recall improves much faster with respect to training data size in the BERT-based method than in the non-language model method while precision behaves similarly. These suggest that (i) the BERT-based method should have a good knowledge of grammar required to recognize certain types of error and that (ii) it can transform the knowledge into error detection rules by fine-tuning with a few training samples, which explains its high generalization ability in grammatical error detection. We further show with pseudo error data that it actually exhibits such nice properties in learning rules for recognizing various types of error. Finally, based on these findings, we explore a cost-effective method for detecting grammatical errors with feedback comments explaining relevant grammatical rules to learners.
Error Diagnosis of Deep Monocular Depth Estimation Models

Estimating depth from a monocular image is an ill-posed problem: when the camera projects a 3D scene onto a 2D plane, depth information is inherently and permanently lost. Nevertheless, recent work has shown impressive results in estimating 3D structure from 2D images using deep learning. In this paper, we put on an introspective hat and analyze state-of-the-art monocular depth estimation models in indoor scenes to understand these models' limitations and error patterns. To address errors in depth estimation, we introduce a novel Depth Error Detection Network (DEDN) that spatially identifies erroneous depth predictions in the monocular depth estimation models. By experimenting with multiple state-of-the-art monocular indoor depth estimation models on multiple datasets, we show that our proposed depth error detection network can identify a significant number of errors in the predicted depth maps. Our module is flexible and can be readily plugged into any monocular depth prediction network to help diagnose its results. Additionally, we propose a simple yet effective Depth Error Correction Network (DECN) that iteratively corrects errors based on our initial error diagnosis.
Towards Full-to-Empty Room Generation with Structure-Aware Feature Encoding and Soft Semantic Region-Adaptive Normalization

The task of transforming a furnished room image into a background-only is extremely challenging since it requires making large changes regarding the scene context while still preserving the overall layout and style. In order to acquire photo-realistic and structural consistent background, existing deep learning methods either employ image inpainting approaches or incorporate the learning of the scene layout as an individual task and leverage it later in a not fully differentiable semantic region-adaptive normalization module. To tackle these drawbacks, we treat scene layout generation as a feature linear transformation problem and propose a simple yet effective adjusted fully differentiable soft semantic region-adaptive normalization module (softSEAN) block. We showcase the applicability in diminished reality and depth estimation tasks, where our approach besides the advantages of mitigating training complexity and non-differentiability issues, surpasses the compared methods both quantitatively and qualitatively. Our softSEAN block can be used as a drop-in module for existing discriminative and generative models. Implementation is available on vcl3d.github.io/PanoDR/.
Semantic Cut Elimination for the Logic of Bunched Implications, Formalized in Coq

The logic of bunched implications (BI) is a substructural logic that forms the backbone of separation logic, the much studied logic for reasoning about heap-manipulating programs. Although the proof theory and metatheory of BI are mathematically involved, the formalization of important metatheoretical results is still incipient. In this paper we present a self-contained formalized, in the Coq proof assistant, proof of a central metatheoretical property of BI: cut elimination for its sequent calculus. The presented proof is *semantic*, in the sense that is obtained by interpreting sequents in a particular "universal" model. This results in a more modular and elegant proof than a standard Gentzen-style cut elimination argument, which can be subtle and error-prone in manual proofs for BI. In particular, our semantic approach avoids unnecessary inversions on proof derivations, or the uses of cut reductions and the multi-cut rule. Besides modular, our approach is also robust: we demonstrate how our method scales, with minor modifications, to (i) an extension of BI with an arbitrary set of \emph{simple structural rules}, and (ii) an extension with an S4-like $\Box$ modality.
Image-to-image Translation as a Unique Source of Knowledge

Image-to-image (I2I) translation is an established way of translating data from one domain to another but the usability of the translated images in the target domain when working with such dissimilar domains as the SAR/optical satellite imagery ones and how much of the origin domain is translated to the target domain is still not clear enough. This article address this by performing translations of labelled datasets from the optical domain to the SAR domain with different I2I algorithms from the state-of-the-art, learning from transferred features in the destination domain and evaluating later how much from the original dataset was transferred. Added to this, stacking is proposed as a way of combining the knowledge learned from the different I2I translations and evaluated against single models.
Quantifying Blockchain Extractable Value: How dark is the forest?

Permissionless blockchains such as Bitcoin have excelled at financial services. Yet, opportunistic traders extract monetary value from the mesh of decentralized finance (DeFi) smart contracts through so-called blockchain extractable value (BEV). The recent emergence of centralized BEV relayer portrays BEV as a positive additional revenue source. Because BEV was quantitatively shown to deteriorate the blockchain's consensus security, BEV relayers endanger the ledger security by incentivizing rational miners to fork the chain. For example, a rational miner with a 10% hashrate will fork Ethereum if a BEV opportunity exceeds 4x the block reward. However, related work is currently missing quantitative insights on past BEV extraction to assess the practical risks of BEV objectively. In this work, we allow to quantify the BEV danger by deriving the USD extracted from sandwich attacks, liquidations, and decentralized exchange arbitrage. We estimate that over 32 months, BEV yielded 540.54M USD in profit, divided among 11,289 addresses when capturing 49,691 cryptocurrencies and 60,830 on-chain markets. The highest BEV instance we find amounts to 4.1M USD, 616.6x the Ethereum block reward. Moreover, while the practitioner's community has discussed the existence of generalized trading bots, we are, to our knowledge, the first to provide a concrete algorithm. Our algorithm can replace unconfirmed transactions without the need to understand the victim transactions' underlying logic, which we estimate to have yielded a profit of 57,037.32 ETH (35.37M USD) over 32 months of past blockchain data. Finally, we formalize and analyze emerging BEV relay systems, where miners accept BEV transactions from a centralized relay server instead of the peer-to-peer (P2P) network. We find that such relay systems aggravate the consensus layer attacks and therefore further endanger blockchain security.
Computing zeta functions of algebraic curves using Harvey’s trace formula

Abstract We present a new method for computing the zeta function of an algebraic curve over a finite field. The algorithm relies on a trace formula of Harvey to count points on a plane model of the curve. The zeta function of the curve is then obtained by making corrections at singular points. We report on an implementation and provide some examples in MAGMA which demonstrate an improvement over Tuitman’s algorithm.
Learning in the Presence of Multiple Agents

Abstract Reinforcement Learning (RL) has emerged as a powerful tool to solve sequential decision-making problems, where a learning agent interacts with an unknown environment in order to maximize its rewards. Although most RL real-world applications involve multiple agents, the Multi-Agent Reinforcement Learning (MARL) framework is still poorly understood from a theoretical point of view. In this manuscript, we take a step toward solving this problem, providing theoretically sound algorithms for three RL sub-problems with multiple agents: Inverse Reinforcement Learning (IRL), online learning in MARL, and policy optimization in MARL. We start by considering the IRL problem, providing novel algorithms in two different settings: the first considers how to recover and cluster the intentions of a set of agents given demonstrations of near-optimal behavior; the second aims at inferring the reward function optimized by an agent while observing its actual learning process. Then, we consider online learning in MARL. We showed how the presence of other agents can increase the hardness of the problem while proposing statistically efficient algorithms in two settings: Non-cooperative Configurable Markov Decision Processes and Turn-based Markov Games. As the third sub-problem, we study MARL from an optimization viewpoint, showing the difficulties that arise from multiple function optimization problems and providing a novel algorithm for this scenario.
Explainable Artificial Intelligence (XAI): Conception, Visualization and Assessment Approaches Towards Amenable XAI

For last decade, due to the accessibility of huge databases and recent advancements in deep learning methodology, machine learning systems have arrived at or transcended tremendous performance in a spacious variety of tasks. One can see this speedy development in speech analysis, image recognition, sentiment analysis, strategic game planning and many more, for e.g. in medical field, it's used for diagnosing different diseases, like breast cancer etc., based on their symptoms. But many state-of- the-art models is facing lack of transparency and interpretability which is a major hindrance in many applications, e.g. finance and healthcare where visualization, interpretation and explanation for model's decision is an obligation for trust. This is an implicit problem of the current techniques carried by sub-symbolism (e.g. Deep Neural Networks) that were not shown in the last hype of AI (specifically, rule based models and expert systems). Models underlying this problem come within the so-called Explainable AI (XAI) field, which is extensively acknowledged as a racial feature for the practical deployment of AI models. As a result, explainable artificial intelligence (XAI) has turned into scientific interest in last recent years. So, this chapter epitomizes contemporary developments in Explainable AI that describes explainability in Machine Learning, constituting a fiction definition of explainable Machine Learning that envelopes such prior conceptual propositions with a considerable focus on the audience for which the explainability is needed. Except of this definition, this chapter starts a confabulation on its various techniques that are essentials for analysing interpretability and explainability of Artificial Intelligence, and also gives a comparison between two medical experiments, that are based on predicting heart disease using disparate Explainable Artificial Intelligence techniques, which can give a lead for researchers as well as practitioners or newcomers in the field of Artificial Intelligence for selecting suitable methods with Explainable AI to grasp the advances of AI in their action sectors, without any previous bias for its dearth of interpretability.
Principal Component Regression Modelling with Variational Bayesian Approach to Overcome Multicollinearity at Various Levels of Missing Data Proportion

This study aims to model Principal Component Regression (PCR) using Variational Bayesian Principal Component Analysis (VBPCA) with Ordinary Least Square (OLS) as a method of estimating regression parameters to overcome multicollinearity at various levels of the proportion of missing data. The data used in this study are secondary data and simulation data contaminated with collinearity in the predictor variables with various missing data proportions of 1%, 5%, and 10%. The secondary data used is the Human Depth Index in Java in 2021, complete data without missing values. The results indicate that the multicollinearity in secondary and original data can be optimally overcome as indicated by the smaller standard error value of the regression parameter for the PCR using VBPCA method which is smaller and has a relative efficiency value of less than 1. VBPCA can handle the proportion of missing data to less than 10%. The proportion of missing data causes information from the original variable to decrease, as evidenced by immense MAPE value and the parameter estimation bias that gets bigger. Then the cross validation (Q^2 ) value and the coefficient of determination (adjusted R^2 ) are get smaller as the proportion of missing data increases.
Geometry Learning Through Batik Reconstruction

In this world, the shapes of objects, including Batik motifs in Indonesia, are regular and irregular. One of the regular Batik motifs is Surya Kawung Batik from Mojokerto. The purpose of this research is to observe the ability of the Electrical Engineering Department students in Maranatha Christian University to study and reconstruct the geometric shapes of Surya Kawung Batik. In the making of the Batik motifs, the research methods employed are survey, observation, exploration, testing, and improvement, while in the learning process, the method applied is descriptive qualitative, in which the researchers check the data credibility. Turtle graphics algorithm and mathematical calculations are used to form Batik geometric motifs. The result of this research shows an increase in the students' ability to learn the geometric shapes and to reconstruct digital Batik motifs which resemble the original Batik motifs and which can be stored using a smaller memory. If the memory for storing motifs is small, the required storage space will be more efficient.
Transformation of tent architecture hybrid into the raincoat

Tent architecture is used in disaster conditions and emergencies, but in this millennium era, it is not only a disaster condition but as a glamping tent or glamorous camping. This writing aims to produce a hybrid architecture, a prediction of the needs of Generation Z for temporary space. The research method is qualitative interpretive, shaping the architectural space interpreted as experimentation to build a wearable shelter which includes; 1). The temporal space of the Millennial generation; 2). Portable architecture; 3). Typology of portable tents; 4). The ideal tent. The result is a nomadic space intended to support the productivity and self-expression of a new generation, which heeds global concerns for health and the environment while engendering a new informality. The findings of this study are the transformation of architectural hybrids from tents to raincoats for the millennial generation.
Oyster Mussels (Crassostrea gigas) Transport System (Case Study in Kamashima Company) in Hyogo, Japan

Oyster culture is an aquaculture activity that is mostly carried out in Japanese waters. While in Indonesia, oyster shells are widely consumed, but they have not been produced through the culture process but are caught on the high seas. This study aims to determine how the application of technology and transportation in the culture and sale of oyster in Japan. Based on literature studies and activities at the Kamashima-Japan company, transportation is an important part of oyster farming activities. Oyster transportation technology is currently carried out in two areas, namely sea and land, using open and closed dry transportation methods. Sea transportation uses Hydraulic Cranes for Shipboard Use (Marine Cranes) technology which is applied to special vessels measuring 6-7 GT with the classification of Truck-mounted cranes, while land transportation uses Styrofoam boxes equipped with ice blocks to immobilize the temperature. The size of the wind that blows in the sea, the time of pick-up and delivery, and the number of oyster shells required are important factors in these transportation activities.
A study of risk-based authentication system in cyber security using machine learning

The optimum authentication method is determined by the user's risk profile, which is created using context- and behavior-based data from the user's device, finger print, one-time password, and other characteristics. Hacking and security breaches of online accounts, including social networking and web ac- counts, are very common in today's society. We suggest a Risk Based Authentication System utilizing Machine Learning to stop this. For the protection of data and money in this internet environment, security is a worry. Numerous parameters are researched and taken into consideration in the paper in order to solve the issue. These variables determine whether to grant the user permission or not. The gradients descent method is used to verify the user. Previous literature is re- viewed with technical details of the system before conclusion.
A Decentralized Medical Network for Maintaining Patient Records Using Blockchain Technology

Abstract Personal Medical Records (PMR) manage an individual’s medical information in digital form and allow patients to view their medical information and doctors to diagnose diseases. Today’s institution-dependent centralized storage, fails to give trustworthy, secure, reliable, and traceable patient controls. This leads to a serious disadvantage in diagnosing and preventing diseases. The proposed blockchain technique forms a secured network between doctors of the same specialization for gathering opinions on a particular diagnosis by sharing the PMR with consent to provide better care to patients. To finalize the disease prediction, members can approve the diagnosis. The smart contract access control allows doctors to view and access the PMR. The scalability issue is resolved by the Huffman code data compression technique, and security of the PMR is achieved by an advanced encryption standard. The proposed techniques’ requirements, latency time, compression ratio and security analysis have been compared with existing techniques.
ON MODAL -VALENT STEGANOGRAPHIC PROTOCOLS

In this note, we plan to define a notion of modal steganographic protocols and then give some examples.