Reviewerly/exHarmony huggingface dataset:
Weapon Detection System

Abstract: Security is always a main concern in every sphere, due to a rise in crime rate in a crowded event or suspicious lonely areas. Anomalydetection and observance have major applications of computer vision to gear various problems. Due to demand in the protection of safety, security of private properties placement of surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence observance. Detection of weapon and militant using convolution neural network (CNN). Proposed implementation uses two types of datasets. One dataset contains pre-labelled images. And the other one labelled manually contains a set of images. Results are tabulated, both algorithms achieve good efficiency, but their operation in real situations can be based on the trade-off between speed and efficiency. Crime is defined as an act dangerous not only to the person involved, but also to the community as a whole. It is to predict the crime using image dataset and finally calculate accurate performance of the detector. The propose algorithms that are able to alert the human operator when a weapon and militant is visible in the image. It is mainly focusedon limiting the number of false alarms in order to allow for real life application of the system. For future work, it is planned to use in live applicationand to improve the detection and reduce the crime.
Phishing URL Detection using Machine Learning

Abstract: Phishing attacks continue to pose a major threat for computer system defenders, often forming the first step in a multistage attack. There have been great strides made in phishing detection; however,some phishing emails appear to pass through filters by making simple structural and semantic changes to the messages. We tackle this problem through the use of a machine learning classifier operating on a large corpus of phishing and legitimate emails. We design a system to extract features, elevating some to higherlevel feature, that are meant to defeat common phishing email detection strategies. This paper presents an approach to detect phishing URLs in an efficient way based on URL features only. For detecting the phishing URLs SVM classifier is used. The performances are evaluated for different size of datasets using different number of features. The results are compared with other machine learning classification techniques. The proposed system is able to detect phishing websites using URL features only.
Fake Product Detection Using Blockchain Technology

Abstract: There are several fake products in the existing supply chain markets. It is necessary to have a system for end users to check all details about the product they are buying so that the customer can check if the product is genuine. In recent years, faking products play an important role in product manufacturing industries. Faking products affects the company name, sales, and profit of the organizations. Blockchain technology is used for the identification of actual merchandise and the detecting of faux products. Blockchain technology is generally a tally system that stores all the data of the deals that take place in it. The precise aspect approximately this era is that the tally we referred to is a allotted tally in a peer-to- peer community. Blockchain generation is relaxed as the facts saved once within the chain is immutable consequently any block cannot be modified or hacked. with the aid of using Blockchain era, clients or customers do no longer want to rely on users for confirmation of product authenticity and protection. Our device affords the rising generation of web use instances, and brief reaction (QR) codes offer a unique but affectable method to triumph over these faking of products. Counterfeited merchandise may be detected the use of a QR code scanner, wherein the QR code of the product is linked to the Blockchain. So, this system can be used to store product info and generate particular code of that product as blocks inside the database. It collects the precise code from the consumer and compares the code against entities within the Blockchain database. If the code matches, it will give all the information about the product otherwise no data can be outputted to the purchaser which shows that the product is fake or counterfeited.
Generalized Multiple Intent Conditioned Slot Filling

Natural language understanding includes the tasks of intent detection (identifying a user's objectives) and slot filling (extracting the entities relevant to those objectives). Prior slot filling methods assume that each intent type cannot occur more than once within a message, however this is often not a valid assumption for real-world settings. In this work, we generalize slot filling by removing the constraint of unique intents in a message. We cast this as a JSON generation task and approach it using a language model. We create a pre-training dataset by combining DBpedia and existing slot filling datasets that we convert for JSON generation. We also generate an in-domain dataset using GPT-3. We train T5 models for this task (with and without exemplars in the prompt) and find that both training datasets improve performance, and that the model is able to generalize to intent types not seen during training.
Silver Syntax Pre-training for Cross-Domain Relation Extraction

Relation Extraction (RE) remains a challenging task, especially when considering realistic out-of-domain evaluations. One of the main reasons for this is the limited training size of current RE datasets: obtaining high-quality (manually annotated) data is extremely expensive and cannot realistically be repeated for each new domain. An intermediate training step on data from related tasks has shown to be beneficial across many NLP tasks.However, this setup still requires supplementary annotated data, which is often not available. In this paper, we investigate intermediate pre-training specifically for RE. We exploit the affinity between syntactic structure and semantic RE, and identify the syntactic relations which are closely related to RE by being on the shortest dependency path between two entities. We then take advantage of the high accuracy of current syntactic parsers in order to automatically obtain large amounts of low-cost pre-training data. By pre-training our RE model on the relevant syntactic relations, we are able to outperform the baseline in five out of six cross-domain setups, without any additional annotated data.
Simulation of a Variational Quantum Perceptron using Grover's Algorithm

The quantum perceptron, the variational circuit, and the Grover algorithm have been proposed as promising components for quantum machine learning. This paper presents a new quantum perceptron that combines the quantum variational circuit and the Grover algorithm. However, this does not guarantee that this quantum variational perceptron with Grover's algorithm (QVPG) will have any advantage over its quantum variational (QVP) and classical counterparts. Here, we examine the performance of QVP and QVP-G by computing their loss function and analyzing their accuracy on the classification task, then comparing these two quantum models to the classical perceptron (CP). The results show that our two quantum models are more efficient than CP, and our novel suggested model QVP-G outperforms the QVP, demonstrating that the Grover can be applied to the classification task and even makes the model more accurate, besides the unstructured search problems.
Generating coherent comic with rich story using ChatGPT and Stable Diffusion

Past work demonstrated that using neural networks, we can extend unfinished music pieces while maintaining the music style of the musician. With recent advancements in large language models and diffusion models, we are now capable of generating comics with an interesting storyline while maintaining the art style of the artist. In this paper, we used ChatGPT to generate storylines and dialogue and then generated the comic using stable diffusion. We introduced a novel way to evaluate AI-generated stories, and we achieved SOTA performance on character fidelity and art style by fine-tuning stable diffusion using LoRA, ControlNet, etc.
Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering

Self-supervised speech representation models have succeeded in various tasks, but improving them for content-related problems using unlabeled data is challenging. We propose speaker-invariant clustering (Spin), a novel self-supervised learning method that clusters speech representations and performs swapped prediction between the original and speaker-perturbed utterances. Spin disentangles speaker information and preserves content representations with just 45 minutes of fine-tuning on a single GPU. Spin improves pre-trained networks and outperforms prior methods in speech recognition and acoustic unit discovery.
Lexical verbs of hedging in English research articles by native and non-native speakers

This study aims to compare verb variation and the functions of hedges used in English scientific research articles written by English native speakers (NS) and non-native speakers from Indonesia (NNS). The data were collected from a corpus of 30 international journal articles. The analysis was conducted quantitatively using a normalization system and statistical tests to determine the frequency and distribution of lexical verbs and qualitatively using Palmer’s (2001) and Hyland’s (1996) hedging model to identify the forms and specific functions of the hedges. The findings demonstrate that both NS and NNS used speculative, deductive, quotative, and sensorial verbs as hedges. However, NS used speculative and sensorial verbs more frequently, while NNS used deductive and quotative verbs more frequently. Statistically, the frequency and distribution of hedges in the two categories of writers were not significantly different. Both NS and NNS tend to use lexical verbs to protect themselves as writers by limiting personal commitments and to protect their readers by anticipating rejection based on subjectivity. These findings contribute to the understanding of hedging use in academic writing by NNS from Indonesia and provide implications for English language teaching and learning.
PENINGKATAN HASIL BELAJAR SISWA KELAS IV SD PADA MATERI INDONESIAKU KAYA BUDAYA MELALUI MEDIA PETA KERAGAMAN BUDAYA

Learning media is a means used so that the interaction in the implementation of learning takes place realistically so that students can receive information well. The purpose of this study was to describe process and improvement of learning outcomes of fourth grade students on the material of Indonesia's rich culture through cultural diversity maps. The research method used is collaborative classroom action research through the stages of 3 cycles. The research subjects were fourth grade students of one of the elementary schools in Bandung City in the 2022/2023 school year. The results showed that students could master the learning material presented through 3 cycles. The completeness of student learning outcomes showed as much as 30% in cycle 1, 75% in cycle 2, and 92% in cycle 3. Thus, that the improvement in the learning outcomes of grade IV students on the material of Indonesia's rich in culture through a map of cultural diversity there is a significant increase in learning outcomes, and the material of Indonesia's rich in culture can be delivered through the media of cultural diversity maps. Recommendations for further research to use cultural diversity maps on other materials.
The discursive nature of modern science: philosophical understanding

The article attempts to find out the patterns of adequate discursive interpreta-tion of science in conditions of social turbulence. For this, based on the socio-communicative methodology, the author makes sense of the relationship between science and philosophy, and also characterizes the state of the modern scientific debate about science in domestic and foreign scientific discourse in such fields as understanding the tasks of post-nonclassical science (O. Polishchuk, I. Do-bronravova, G. Haken), possibilities of reality transformation by means of techno-science (H. Bashlyar, B. Latour, V. Maryniuk, V. Melnyk), risks of post-academic science development (J. Ziman, O. Kuz, V. Cheshko), modes/types of science (M. Gibbons, H. Novotny, S. Limoges, L. Ryzhko). The main result of the study is the author's definition of the strengths and weaknesses of science in a socially turbu-lent space. Among its strengths, the author attributes such properties as increased scientific attention to applied problems, a higher degree of scientific self-organization, multitasking at different levels and within different types of relation-ships, communicativeness. Among the weaknesses are the risks of science losing fundamentality, disciplinary boundaries, which is accompanied by a loss of clarity in the methodology and criteria of scientific success, as well as the risk of replac-ing scientific truth with non-academic success criteria, such as the economic suc-cess of science, political correctness, etc. It is concluded that in the situation of global transformations and uncertainty which humanity is currently experiencing, new characteristics of science are developing, which help to understand the risks and dangers of moving away from traditional academicism.
Automatic Mist Sprayer for Oyster Mushroom Cultivation Using Thingspeak IoT Platform and R Analytical Tool

In mushroom greenhouses at IKM Simbang Maros, watering is currently done manually using a hose and scheduled twice daily, without considering the optimal temperature and humidity for mushroom growth. This study aims to develop an automatic watering system for oyster mushroom cultivation using a DHT22 sensor to monitor temperature and humidity. The system is equipped with a NodeMCU ESP32 microcontroller and a mist sprayer, triggered by a relay to activate the water pump. An IoT platform using Thingspeak allows farmers to monitor temperature and humidity data in real-time on their smartphones. In a real case study, the device effectively maintained optimal humidity levels for mushroom growth, with the pump activating when humidity was below 85%. Data from 10 observations showed that the pump was active for 7 times. Data processing using R Studio showed the system's ability to stabilize humidity levels. The proposed system could help farmers optimize mushroom growth and reduce the labor required for manual watering.
Exploring parental language ideology and language enrichment in Indonesian children’s translingual practices

There is an increased urgency of acquiring a lingua franca in this era of industrial revolution 4.0 and society 5.0 as language mastery plays a crucial role in meeting the 21st-century’s demand to compete globally. To this end, family literacy practices have become the keys for the family to socialize their children into the languages they think will benefit their children’s future. Despite the increasingly abundant research on bilingual/multilingualism, research on family English bilingual practice in Indonesian contexts is still limited. This paper is aimed to discuss family literacy practices and language enrichment used to acquire English in two Indonesian families in South Sumatra. A case study design was used to examine translingual practice. Interviews and observations were conducted to collect the data from the two families. Categorizing strategies (coding and thematic analysis) were accomplished to analyze the data. The findings of this study highlight translanguaging, code-switching, and code-mixing as the families’ common literacy practices at home. Another finding reveals the critical role of digital media, such as TV programs, YouTube videos, online games, and books, in supporting children’s English acquisition. Implications of this study include the need to pay attention to the family literacy practice as important component of understanding how second language socialization occurs outside of formal schooling and the role of digital media in mediating this socialization process.
KOMPARASI METODE SMOTE DAN ADASYN UNTUK PENANGANAN DATA TIDAK SEIMBANG MULTICLASS

Data Mining is an activity that combines various branches of science into one, consisting of database systems, statistics, machine learning, and visualization, to analyze a large dataset in order to obtain useful data characteristics. To address the problem of imbalanced datasets, the distribution of non-uniform classes among classes is balanced by using a comparison of the SMOTE and ADASYN methods to ensure that the number is balanced between majority (negative) and minority (positive) classes. Based on the results of experiments conducted in this study, testing the SMOTE method with a classification method can handle the number of majority (negative) and minority (positive) classes in imbalanced data by producing MCC and Gmean values that achieve better predictive performance than using a classification method alone or using the ADASYN method. Furthermore, for the MultiClass dataset, the highest MCC and Gmean values were achieved using SMOTE + KNN with the highest MCC value of 0.64 and Gmean value of 0.74. This indicates that the handling process of imbalanced class distribution in the data preprocessing stage has an influence on the accuracy values of MCC and Gmean in the SMOTE + KNN method.
The main directions of the study of the psychological specifics of distance forms of professional activity realization

The article presents materials that reveal the psychological specifics of distance forms of professional activity. It is shown that it can be characterized from the standpoint of the study of a new subject-information class. The provisions on transformations in the subsystems of professionally important qualities (PIQ) of the different types of activity included in this class, as well as provisions on the specifics of distance forms of educational activity, including in the context of organizational learning and in the context of professiogenesis and the problem of learning in labor psychology, are formulated.
Echoes of Biases: How Stigmatizing Language Affects AI Performance

Electronic health records (EHRs) serve as an essential data source for the envisioned artificial intelligence (AI)-driven transformation in healthcare. However, clinician biases reflected in EHR notes can lead to AI models inheriting and amplifying these biases, perpetuating health disparities. This study investigates the impact of stigmatizing language (SL) in EHR notes on mortality prediction using a Transformer-based deep learning model and explainable AI (XAI) techniques. Our findings demonstrate that SL written by clinicians adversely affects AI performance, particularly so for black patients, highlighting SL as a source of racial disparity in AI model development. To explore an operationally efficient way to mitigate SL's impact, we investigate patterns in the generation of SL through a clinicians' collaborative network, identifying central clinicians as having a stronger impact on racial disparity in the AI model. We find that removing SL written by central clinicians is a more efficient bias reduction strategy than eliminating all SL in the entire corpus of data. This study provides actionable insights for responsible AI development and contributes to understanding clinician behavior and EHR note writing in healthcare.
rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition

Human Activity Recognition (HAR) has become a spotlight in recent scientific research because of its applications in various domains such as healthcare, athletic competitions, smart cities, and smart home. While researchers focus on the methodology of processing data, users wonder if the Artificial Intelligence (AI) methods used for HAR can be trusted. Trust depends mainly on the reliability or robustness of the system. To investigate the robustness of HAR systems, we analyzed several suitable current public datasets and selected WISDM for our investigation of Deep Learning approaches. While the published specification of WISDM matched our fundamental requirements (e.g., large, balanced, multi-hardware), several hidden issues were found in the course of our analysis. These issues reduce the performance and the overall trust of the classifier. By identifying the problems and repairing the dataset, the performance of the classifier was increased. This paper presents the methods by which other researchers may identify and correct similar problems in public datasets. By fixing the issues dataset veracity is improved, which increases the overall trust in the trained HAR system.
A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning

Works in quantum machine learning (QML) over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in some cases. Among the corpus of recent work, many current QML models take advantage of variational quantum algorithm (VQA) circuits, given that their scale is typically small enough to be compatible with NISQ devices and the method of automatic differentiation for optimizing circuit parameters is familiar to machine learning (ML). While the results bear interesting promise for an era when quantum machines are more readily accessible, if one can achieve similar results through non-quantum methods then there may be a more near-term advantage available to practitioners. To this end, the nature of this work is to investigate the utilization of stochastic methods inspired by a variational quantum version of the long short-term memory (LSTM) model in an attempt to approach the reported successes in performance and rapid convergence. By analyzing the performance of classical, stochastic, and quantum methods, this work aims to elucidate if it is possible to achieve some of QML's major reported benefits on classical machines by incorporating aspects of its stochasticity.
Can Deep Network Balance Copy-Move Forgery Detection and Distinguishment?

Copy-move forgery detection is a crucial research area within digital image forensics, as it focuses on identifying instances where objects in an image are duplicated and placed in different locations. The detection of such forgeries is particularly important in contexts where they can be exploited for malicious purposes. Recent years have witnessed an increased interest in distinguishing between the original and duplicated objects in copy-move forgeries, accompanied by the development of larger-scale datasets to facilitate this task. However, existing approaches to copy-move forgery detection and source/target differentiation often involve two separate steps or the design of individual end-to-end networks for each task. In this paper, we propose an innovative method that employs the transformer architecture in an end-to-end deep neural network. Our method aims to detect instances of copy-move forgery while simultaneously localizing the source and target regions. By utilizing this approach, we address the challenges posed by multi-object copy-move scenarios and report if there is a balance between the detection and differentiation tasks. To evaluate the performance of our proposed network, we conducted experiments on two publicly available copy-move datasets. The results and analysis aims to show the potential significance of our focus in balancing detection and distinguishment result and transferring the trained model in different datasets in the field.