Reviewerly/exHarmony huggingface dataset:
Structured Design Solves Multiple Tables of NL2SQL

NL2SQL (NLP Language To SQL) is a cutting-edge research direction of natural language processing, which converts natural query statements input by users into executable SQL statements. CCKS2022 proposes a multi-database multi-table NL2SQL task for the financial domain. For this task, this paper proposes a SQL generation method based on semantic parsing. The method adopts the multi-stage iterative generation mode of “question-database name-table name-column name-SQL statement”, uses semantic parsing and semantic similarity learning methods in acquiring table names, and generates and selects SQL statements based on the same query Multi-input statement for integrated filtering. At the end of the competition, the label replacement method was adopted, that is, the tables and columns in the SQL statement were replaced with tags to reduce the difficulty of generation.
Survey: Emotion Recognition from Text Using Different Approaches

Text Processing is a method for comprehending, analyzing, and cleaning text as well as performing actions on the same data. The technique is used to extract meaningful data from text. It is a written form of communication to express emotions through text. Happy, neutral, fear, sadness, surprise, disgust, and anger are the most common emotional expressions. As a result, in the social media era, identifying emotions from text is especially important. A survey of operational methods and approaches for identifying emotion from textual data is discussed in this paper. This research primarily focuses on existing datasets and methodologies that incorporate a Lexical keyword, Machine Learning and Hybrid-based approach.
Designing of Integration System for IOT Urban Farming: Mobile and Web Application

Technology is developing rapidly coupled with globalization and the industrial era 4.0 makes everything turn digital, one of which is in the field of urban agriculture (urban farming). In this study, we collect data using interview and observation. We used UML approach for analyze and design the system. Then, we proposed system architecture. After that, we create user interface and develop prototype also. We create the system that can be integrate between mobile application and web application. The results of this study are system architecture, web application and mobile application. It can to make easier for the user to monitor their plants. This research will focus on designing cloud databases and connections between microcontrollers, databases, websites and mobile applications with the aim of monitoring plants in real time. The conclusion in this study is the IoT Urban Farming system can be implemented and integrated between mobile and web application. IoT Urban Farming system can be supported by Cloud database.
Application Of Learning Vector Quantization Algorithm In Educational Games Learning To Write Aksara Bali

Almost every region in the nusantara has its own regional script, one of which is in Bali the people know the Balinese script as a script commonly used by their ancestors in writing. In order to keep this Balinese script sustainable and not extinct, learning about this script is applied in schools in Balinese language subjects. In addition to applying learning about script in schools, it would be better if learning about Balinese script could be done in more interesting ways and concepts. One of them is by making an educational game for the introduction of Balinese characters. Which in this application the user will provide input in the form of Balinese script characters written on the application, which then the input will go through a preprocessing process then proceed with diagonal feature extraction, then the results of the feature extraction will go through a classification process using the Learning Vector Quantization method. . The result is a web-based application that can recognize Balinese script writing using the Learning Vector Quantization classification method with an accuracy rate of 58.6% and get a good response from each respondent who has tested the application.
Unsupervised AI Workflow to Effectively Compress the Timeline from Discovery to Development Plan and Execution.

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
First-Order Logic: Languages and Structures

This textbook gives a complete and modern introduction to mathematical logic. The author uses contemporary notation, conventions, and perspectives throughout, and emphasizes interactions with the rest of mathematics. In addition to covering the basic concepts of mathematical logic and the fundamental material on completeness, compactness, and incompleteness, it devotes significant space to thorough introductions to the pillars of the modern subject: model theory, set theory, and computability. Requiring only a modest background of undergraduate mathematics, the text can be readily adapted for a variety of one- or two-semester courses at the upper-undergraduate or beginning-graduate level. Numerous examples reinforce the key ideas and illustrate their applications, and a wealth of classroom-tested exercises serve to consolidate readers' understanding. Comprehensive and engaging, this book offers a fresh approach to this enduringly fascinating and important subject.
MASS AND ELITE CULTURES AS FUNCTIONAL SYSTEMS: ON THE PROBLEM OF DEFINING CONCEPTS

The article is devoted to the problem of defining the concepts of mass and elite culture; the purpose of the study is to substantiate the productivity of using a functional approach; the result of the work is the presentation of definitions of these types of culture, the result is the proof of the productivity of this methodology, based not on the analysis of individual phenomena, but their groups, which necessitates the identification of types of culture, largely absorbing the signs all those included in this group of phenomens.
Rancang Bangun Website E-Learning Menggunakan Metode User-Centered Design dan Evaluasi Heuristik

The practice of using the internet in everyday life makes online learning more flexible than face-to-face learning. The online learning process is implemented through e-learning websites at various universities. The e-learning website used in online learning is still minimal attention in evaluating and does not apply elements of user experience and usability in it. We can find out whether a user is satisfied by testing the e-learning website to get feedback from the user. The research was conducted using a user-centered design (UCD) method which focuses on solving problems experienced by a user. Questionnaires will be distributed to users who need a new way of learning through Google Forms to get information about the actual problems experienced by users. This study found that the LearningKu website satisfies users with an average heuristic evaluation score of 1.04 or a scale of 1 included in the cosmetic problem category, which means that the problem does not need to be fixed unless there is time left in project work.
Speech Emotion Classification Using Semi-Supervised LSTM

One of the most challenging topics today is analyzing speech mood. Specifically, because it is unclear which features are most suitable for this task. It is imperative to consider the nature of the dataset, whether it comes from an infant or from an adult.In this study, we describe the characteristics of the type of speech. The Mel-frequency cepstral coefficients (MFCC) and Spectrogram are tools for analyzing audio files. We also examined the CREMA-D dataset to identify different mood states from a sample of the speech files contained within the dataset, which included six moods: normal, angry, happy, sad, scared, and irritated. We developed a mood classification using Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) and used SVMs to increase the number of labeled data in small datasets in order to solve the problem of sample volume. This study contributed to the development of Semi-Supervised Model for testing the classification results developed from a model consisting of SVM and LSTM. The developed semi-supervised model demonstrated improved accuracy when compared with the original number of samples in both the SVM and the LSTM datasets.Using the semi-supervised model, the results of the model development proved that the accuracy of the classification can be improved even further than using the LSTM alone in the technique. Semi-supervised models have a validation accuracy of 89.72 %, while LSTM has a validation accuracy of 54.77%, and the training of the model is also faster using the semi-supervised method.
Anomaly Detection Based on Denoising Autoencoder and Variational Autoencoder with Obrid-Sensor

Aging society is a serious problem around the world. The number of elderly people living alone is increasing. If they have an accident or gets ill at home, there is a risk that they may die because of lack early detection. Some existing systems use cameras to detect dangerous states of the people. Using this system is difficult in places where privacy should be protected like bathrooms. A system has been proposed to detect abnormal states using a sensor called Obrid-Sensor. This sensor can observe subject's state without invading privacy of because brightness information to be acquired is one dimension. A previous study is proposed a fall detection method with an autoencoder by estimating subject's state from standing data. Various noises cause a loss of performance in the autoencoder. Therefore, the method was proposed by using a denoising autoencoder which is more robust to noise, a variational autoencoder which is more expressive. As a result, recall of all methods was 100 %, and fall detection could be performed without false negative. The denoising autoencoder was the most suitable for anomaly detection.
Knowledge-aware Graph Convolutional Network for Collaborative Recommendation

Recommender systems(RS) that introduce the knowledge graph(KG) overcome cold-start and data sparsity problems through mining the abundant side information. Recent popular KG-based recommendation methods are mainly propagation-based methods, however, such methods often fail to fully exploit the rich semantic associations between entities and ignore the potential key signals in user’ actions. Therefore, we use a knowledge-aware graph convolutional network (GCN) for collaborative recommendation (KACR), which first explicitly encodes collaborative signals by collaboratively propagating user-item interaction data. Secondly, we use a graph convolutional network to aggregate semantic information of the local neighborhood structure and propagate users' interest in the KG's high-order connected structure to mine the users' potential preference. Finally, users' and items' final representation is obtained through the aggregator to make predictions. According to the experimental findings on three open datasets, KACR has a stronger recommendation effect than the baselines.
Subjective Probability and Utility

In order to make decisionsProbabilitysubjective, we need to be able to make predictions about the possible outcomes of each decision. Usually, we have uncertainty about what those outcomes are. This can be due to stochasticity, which is frequently used to model games of chance and inherently unpredictable physical phenomena. It can also be due to partial information, a characteristic of many natural problems. For example, it might be hard to know at any single moment how much change you have in your wallet, whether you will be able to catch the next bus, or to remember where you left your keys.
Remote Sensing Object Detection Method Based on Attention Mechanism and Multi-scale Feature Fusion

Aiming at the low target detection accuracy due to the complex imaging background, small size, large number and dense arrangement of targets in optical remote sensing images, this paper was proposed an improved Faster R-CNN algorithm. Firstly, the attention mechanism module is reasonably embedded in the backbone network to emphasize the target information and suppress the background information; Secondly, a feature fusion method is designed to fuse the information of each feature layer in the backbone network to improve the small target detection ability; In addition, the loss function and pooling function are improved and the Anchor parameters are optimized. It can be seen from the experimental data that the detection accuracy of the method proposed in this paper is significantly improved compared with the original algorithm, and the detection accuracy is more dominant than other mainstream algorithms.
Arbitrageurs in the Bitcoin Ecosystem: Evidence from User-Level Trading Patterns in the Mt. Gox Exchange Platform

We mine the leaked history of trades on Mt. Gox, the dominant Bitcoin exchange from 2011 to early 2014, to detect the triangular arbitrage conducted within the platform. We exploit user identifiers per trade to identify and describe the individual trading patterns of 440 arbitrageurs. We introduce proxies for expertise and show that the non sophisticated users conduct on average non-profitable arbitrage when transaction costs are accounted for, while the expert ones obtain a positive and statistically significant premium. Most importantly, by including user fixed effects, we document that expert users make profits on arbitrage by reacting quickly to plausible exogenous variations on the official exchange rates. A small number of arbitrageurs conduct the vast majority of the arbitrage actions and systematically yield higher profits: our results provide empirical evidence that arbitrageurs are few and sophisticated users, characterized by the ability to incorporate information and quick reaction to exogenous shocks within tiny time scale intervals.
Multi-Classification of Electric Power Metadata based on Prompt-tuning

In the era of big data, massive amounts of data are generated every day, especially in industrial fields, such as electricity, communications, finance, meteorology, satellites, etc. These industrial data are rich in value, and will be the foundation of digital economy and information management. Due to the particularity of the industry, the exploitation of big data mainly faces the following challenges that degrade the performance of mainstream general models: 1. The text classification model needs to be universal and rapidly deployed; 2. The short text in the metadata contains large amounts of jargons that are difficult to be semantically vectorized. To this end, this paper proposes a pre-training model based on prompt-tuning, targeting on the classification of metadata for the power industry. The model consists of three modules: 1. Continual pre-training module conducts the mask and prediction task on the unlabeled text dataset of the power industry to enable the pre-training model to acquire new vocabulary and knowledge of the industry; 2. The prompt-tuning model uses the continuous depth prompt technology as the backbone, which helps to bring the pre-training model closer to the downstream tasks. 3. The exterior knowledge-based verbalizer model extends the tags to make the text generation model perform better. The model was tested on a real dataset from the power industry, and was able to improve the F1-score by 13.86% and 6.02%, compared to the classification model based on deep learning and the p-tuning model without knowledgeable verbalizer, respectively.
HeHe: Balancing the Privacy and Efficiency in Training CNNs over the Semi-honest Cloud

Convolutional Neural Networks (CNNs) have been widely used in various areas. As the training of CNNs requires powerful computing resources, data owners are now employing clouds to accomplish the task. However, this inevitably introduces serious privacy issues against the data owners, as the training images are now outsourced to the clouds, who may illegally spy on the content of the images for potential benefit. In this work, we propose HeHe, a CNN training framework over encrypted images with practical efficiency via additively homomorphic encryption and a delicate interaction scheme in CryptoHeader, which are shallow layers of the network. To evaluate whether the image content is preserved through a processing system, we propose $$(\alpha ,\beta )$$ -recoverable, a novel image privacy model, and theoretically prove HeHe is robust against it. We test HeHe on several datasets in the aspects of accuracy, efficiency, and privacy. The empirical study justifies that HeHe is practical for the CNN training over encrypted images while preserving the accuracy with acceptable training cost and content leakage.
Using Uncertainty as a Defense Against Adversarial Attacks for Tabular Datasets

Adversarial examples are a threat to systems that use machine learning models. Considerable research has focused on adversarial exploits using homogeneous datasets (vision, sound, and text) while primarily attacking deep learning models. However, many industries such as healthcare, business analytics, finance, and cybersecurity rely upon heterogeneous (tabular) datasets. The attacks which perform well on homogeneous datasets do not extend to heterogeneous datasets due to feature constraints. Therefore, tabular datasets require different forms of attack and defense mechanisms. In this work, we propose a novel defense against adversarial examples built from tabular datasets. We use an uncertainty metric, the Minimum Prediction Deviation (MPD), to detect adversarial examples generated by a tabular evasion attack algorithm, the Feature Importance Guided Attack (FIGA). Using MPD as a defense we are able to detect 98% of the adversarial samples with a 7.8% false positive rate on average.
Towards developing speaker diarization for parent-child interactions

Daily interactions of children with their parents are crucial for spoken language skills and overall development. Capturing such interactions can help to provide meaningful feedback to parents as well as practitioners. Naturalistic audio capture and developing further speech processing pipeline for parent-child interactions is a challenging problem. One of the first important steps in the speech processing pipeline is Speaker Diarization—to identify who spoke when. Speaker Diarization is the method of separating a captured audio stream into analogous segments that are differentiated by the speaker’s (child or parent’s) identity. Following ongoing COVID-19 restrictions and human subjects research IRB protocols, an unsupervised data collection approach was formulated to collect parent-child interactions (of consented families) using LENA device—a light weight audio recorder. Different interaction scenarios were explored: book reading activity at home and spontaneous interactions in a science museum. To identify child’s speech from a parent, we train the Diarization models on open-source adult speech data and children speech data acquired from LDC (Linguistic Data Consortium). Various speaker embeddings (e.g., x-vectors, i-vectors, resnets) will be explored. Results will be reported using Diarization Error Rate. [Work sponsored by NSF via Grant Nos. 1918032 and 1918012.]
Sentiment Analysis of Indians Due to Conflict Between India and China at the Actual Line of Control in Galwan Valley, Ladakh

Sentimental Analysis, the study of human emotion and opinion is one of the fields which keeps on progressing over the years, as a result of which, it can play a very important role in analyzing market trends and decision-making. Living in an era, when people freely express their viewpoints on social media, a lot of information can be gathered by identifying the emotions behind them. Twitter, a platform led by strong public opinion, has the power to affect the market and even the government. Following the tweets of one such powerful hashtag, #BoycottChineseProducts, we performed a sentimental analysis on this trending tweet in this paper as it showcases the response of Indians to China’s aggression in Galwan Valley. By identifying particular words and giving them weights, the percentage of positive sentiments of Indian consumers toward Chinese Products is calculated which can be used for further studies as it can have a large impact on the Chinese Market in India.
EXPLORING READING SPEED USED BY EFL STUDENTS

Reading speed is the solution to today's pressing challenge for students, who must complete more work or assignments in less time. Reading speed is becoming increasingly important as technology advances, causing readers to require a deeper understanding of the information provided. Furthermore, reading speed is combined with several techniques or strategies to help students read quickly with limited time. This study was conducted in Eight Semesters of the English Department. To find the result in this study, the researcher used interviews and documentation as instruments to collect the data. The subjects in this study are fourteen which are 13 students and one lecturer. The findings showed the students were had various types of reading strategies used in reading speed, and a student may use one or more of them. Skimming, scanning, and silent reading are the most common strategies they employ. However, in terms of reading speed, the students preferred silent reading. These techniques aid in their reading comprehension. In other words, the internal factors students’ equally contributed to the lecturers' problem during teaching reading lessons.
Pengembangan Booklet Pengolahan Limbah Industri Tahu Menggunakan SSF-Wetlands dengan Tanaman Eceng Gondok

Liquid waste generated from the tofu industry contains organic substances, namely 40%-60% protein, 25%-50% carbohydrates, 10% fat, and other suspended solids that can undergo physical, chemical, and biological changes to produce toxic substances. To overcome the pollution caused by the disposal of industrial liquid waste, research on the development of a tofu industrial waste treatment booklet using subsurface flow-wetlands was carried out with water hyacinth plants. This aims to improve the understanding and skills of tofu industry owners so that tofu industry owners can practice on their own about tofu industrial waste treatment after reading the booklet. This research method uses the Thiagarajan media development model, there are 4 stages, namely: definition, design, development, and disseminate. However, this research was carried out only until the development stage. The results of the validation assessment validation test from material experts obtained an assessment percentage of 86% and media experts obtained an assessment percentage of 82.85% with the achievement criteria being very suitable or very valid according to the BSNP criteria. In conclusion, the booklet developed has very interesting qualifications, is very appropriate, and is effective, and has few revisions. so that the booklet media is suitable to be used and disseminated as a media for outreach to the owners of the tofu industry.