Reviewerly/exHarmony huggingface dataset:
Domestic Violence Detection System Using Natural Language Processing

Domestic violence is a widespread problem that leaves victims feeling helpless and trapped. It refers to a pattern of abusive behavior in an intimate relationship, where one partner seeks to exert power and control over the other. To address this issue, we proposed a system that incorporates several advanced technological aspects to address domestic violence and support victims. It includes a speech recognition module that identifies abusive behavior patterns through specific words and phrases associated with domestic violence. Natural language processing techniques analyze the context and tone of the language to understand the emotional state of the victim and detect instances of emotional abuse. A camera system with computer vision capabilities detects physical violence and abuse signs through object detection and tracking in video footage. The system is accessible through a mobile app, enabling victims to collect evidence of abuse and access necessary resources and support services. Machine learning algorithms continuously improve the system's accuracy and performance by learning from a large dataset of abusive language patterns and visual cues. When abusive behavior is detected, the system can automatically send alerts to law enforcement and emergency contacts, providing immediate assistance. Recorded voice and video footage, along with associated analysis results, are securely stored as evidence for legal proceedings. By integrating these technological aspects, the system aims to empower victims, improve evidence collection, and support efforts to combat domestic violence.
Multivariate Analysis

Data Science for the Geosciences provides students and instructors with the statistical and machine learning foundations to address Earth science questions using real-world case studies in natural hazards, climate change, environmental contamination and Earth resources. It focuses on techniques that address common characteristics of geoscientific data, including extremes, multivariate, compositional, geospatial and space-time methods. Step-by-step instructions are provided, enabling readers to easily follow the protocols for each method, solve their geoscientific problems and make interpretations. With an emphasis on intuitive reasoning throughout, students are encouraged to develop their understanding without the need for complex mathematics, making this the perfect text for those with limited mathematical or coding experience. Students can test their skills with homework exercises that focus on data scientific analysis, modeling, and prediction problems, and through the use of supplemental Python notebooks that can be applied to real datasets worldwide.
Adapting for Mobile

If you took a straw poll of 100 people and asked them if they use their cell phones to purchase goods - how many would say yes? In today’s world, I bet a sizeable proportion would say yes: more and more people are using a mobile device to purchase products, and that’s not just limited to cell phones! In this chapter, we’ll explore the steps we need to take to modify our site so it supports mobile devices, from choosing appropriate examples, through to effecting changes on our code so the site looks presentable on a cell phone or tablet.
THE EFFECT OF TEACHER’S IMPLEMENTED STRATEGIES ON STUDENTS’ TEXT COMPREHENSION

Text comprehension is the key to successful participation in society, and it should be mastered at a sufficient level by the end of basic school.Various strategies should be used to support students' vocabulary and text comprehension at different cognitive levels.The teacher's role is crucial in this process.In order to identify how teachers' strategies affected students' vocabulary and text comprehension at different levels, basic school students were pre-and post-tested, and their language teachers were questioned.In the strategic intervention, the effects of six comprehension strategies were examined.Students' better comprehension was influenced by teaching them to form and answer questions.Comprehension was positively affected by the monitoring strategy.In contrast, the use of summarising the text and improving vocabulary negatively affected students' inferential comprehension.Teacher's proper usage of strategies is essential for promoting students' comprehension skills.
MAXIMIZING THE EFFICIENCY OF PRESENCE-DISTANCE EDUCATION MICROCOMPUTERS SYSTEMS

Embedded systems have been one of the most widely used areas of electrotechnology in recent decades.The common question is how to maximize the teaching efficiency of the technology area, where the IT technology and Electrotechnology areas are intersected.The departments of universities and future employer of absolvents are focused on different platforms of microcontrollers.That fact leads to finding an optimal teaching system.This article examines and proposes solutions for improving the teaching efficiency of the presence and distance education methods.The main challenge is to motivate students and increase their understanding of the microcomputer system.The article's secondary challenge is to offer a solution to the distance learning method, which has had a negative impact on students' knowledge levels.The article describes how to increase the effectiveness of distance learning using current software tools.The distance education methods are divided into two categories with a different way of education reaching.
VISUALISASI DATA STOK BARANG PADA PT BECEK GRUP INDONESIA

Skripsi dashboard to monitor incoming and outgoing stock, sales volume, and analyzing the best-selling products of the month at PT Becek Grup Indonesia. The dashboard is well designed to provide accurate and credible information about stock items, so that PT Becek Grup Indonesia can make the right and optimal decisions. The methodology used in this study was interview, observation, and data analysis through the prototyping method. The results of the research show that stocks go in and out and identify a product accurately and provide effective information. In addition, this dashboard has a visualization designed using Power BI so that users can easily use it and can be accessed via mobile devices. Then the visualized data is expected to improve service quality at PT Becek Grup Indonesia and provide significant benefits for warehouse management. Then the data that has been visualized can be used to make it easier for users to find out the details and information they want to know
Perancangan Dashboard Untuk Pemantauan Jumlah Siswa Pada PT XYZ Menggunakan Metode Prototyping

The development of technology has encouraged the growth of coding courses as a solution to prepare children for an increasingly digital future. PT XYZ is a course school that offers coding learning for children aged 5-18 years. Currently, PT XYZ already has several branches in major cities. In monitoring the number of students at each branch, PT XYZ still manages data manually which is vulnerable to inaccurate data. This research aims to design a dashboard that can be used to help companies monitor the number of students and other indicators related to company performance. This dashboard design uses the prototyping method and Microsoft Power BI. The result of this research is a dashboard system that can be used to monitor the number of students from the three main branches of PT XYZ, as well as display other information related to students that can help companies to set targets to be achieved.
Rancang Bangun Dashboard Penjualan pada PT. XYZ dengan Microsoft PowerBI

One of the most popular and most widely used technologies among people today is the internet. Starting from studying, shopping, working, doing business, communicating, everything can be accessed only by using the internet or in other words referred to as online activities. People are now trying to expand their business online, one of which is PT. XYZ. This company is a retail company that has an online and offline distribution system and sells power tools and household appliances. This company already has an application-based system. Therefore, the dashboard design for this company will be used to provide a summary of sales for three months from tokopedia, shoppe, lazada and tiktok shop to make it easier for company leaders to make decisions. The data collection method was carried out by interviewing and dashboard design using the prototyping method. This design will produce a dashboard using the Microsoft PowerBI Desktop application which can provide visual information about PT.XYZ's sales data.
A Systematic Pelican Optimization based Weight Extreme Learning Machine Algorithm for Face Emotion Recognition

Recognizing the facial expressions from the given input is one of the challenging and demanding tasks in recent days owing to the low-resolution images and different backgrounds.Also, the facial emotional/expression recognition system has gained a significant attention in the field of computer vision.The conventional works implemented a variety of machine learning algorithms for face emotion recognition, but higher computation costs, decreased reliability, redundant information, and increased computational time requirement are still only a few of the issues.In this case, an image face filtering method is employed in the beginning to produce a normalized output image with high contrast and quality, which is used to increase the classifier's overall rate of emotion recognition.Then, the novel pelican optimization algorithm (POA) is used to optimize the feature set in order to guarantee the success of classifier training and testing.Prior to emotion recognition, this technique is used to reduce the dimensionality of information.Based on the given face image's optimized attributes, the weight optimized extreme learning machine (WOELM) algorithm is used to reliably identify the emotions.By using well-known datasets like CK+ and MMI, a wide range of factors are taken into consideration for study in order to compare and evaluate the outcomes of the proposed POA-WOELM face emotion identification system.The obtained results reveal that the combination of POA-WOELM provides an increased 99% of emotion recognition accuracy to all kinds of emotions, with the sensitivity of 98.8% and specificity of 98.9%.
The Role and Impact of Social Media Influencers

In this paper, we explore the influence of influencers in the digital space and their impact on society.The research involved 375 intentional sampled respondents who are social media followers.The findings revealed that TikTok is the most popular platform for influencers, followed by Instagram, YouTube, Facebook, Snapchat, and Twitter.Influencers predominantly create entertainment and gaming content, with art and skills, social issues, education, and religion following suit.The study highlights that influencers are crucial in shaping societies by providing attractive and concise information and experiences, fostering development and support among their followers.The author suggests enhancing influencers' skills through workshops and training programs, focusing on positive interactions, humanitarian aspects, and support for affected sectors, particularly therapeutic institutions.
Penerapan Metode SVM Untuk Deteksi Dini Penyakit Stroke (Studi Kasus : RSUD Dr. H. Ishak Umarella Maluku Tengah dan RS Sumber Hidup-GPM)

Stroke is a significant health problem in today's modern society. Early detection of stroke usually takes a long time. To prevent the risk of a significant disabling stroke, it is good to pay attention and recognize the symptoms of a stroke early on. In this study, the Support Vector Machine (SVM) method was used to detect stroke based on risk factors for stroke consisting of blood pressure, age, LDL, and blood sugar. Based on the results obtained, the nonlinear SVM method has a better level of accuracy than the linear SVM. This is because of the two data-sharing schemes, the linear SVM only has an accuracy rate of 81.25%, while the nonlinear SVM has an accuracy rate of 84.38%. Especially for the nonlinear SVM, the RBF kernel has a better level of accuracy than the polynomial kernel. This can be seen from the results of testing the two data sharing schemes, the RBF kernel has the best results, namely the highest accuracy rate of 84.38% and 84% respectively
Connecting Expressive Sound to Bow Technique

Subject Philosophy of Music Applied Music Collection: Oxford Scholarship Online
The Double ARA-Formable Transform with Applications

The main goal of this work is to introduce a new double transform named the double ARA-Formable transform (DARA- FT) we introduce the application of the new double transform to various fundamental functions and master properties. Additionally, we state and prove the convolution and existence theorems. Subsequently, by utilizing these findings, different kinds of partial differential equations (PDEs) are solved. We establish novel methods for resolving families of PDEs. With the latter, one can obtain precise answers to some well-known PDEs, including the telegraph equation, the advection-diffusion equation, the Klein-Gordon equation, and others. The results demonstrate the double ARA-Formable transform is effective and beneficial for handling these types of problems
Variable Importance Without Impossible Data

The most popular methods for measuring importance of the variables in a black-box prediction algorithm make use of synthetic inputs that combine predictor variables from multiple observations. These inputs can be unlikely, physically impossible, or even logically impossible. As a result, the predictions for such cases can be based on data very unlike any the black box was trained on. We think that users cannot trust an explanation of the decision of a prediction algorithm when the explanation uses such values. Instead, we advocate a method called cohort Shapley, which is grounded in economic game theory and uses only actually observed data to quantify variable importance. Cohort Shapley works by narrowing the cohort of observations judged to be similar to a target observation on one or more features. We illustrate it on an algorithmic fairness problem where it is essential to attribute importance to protected variables that the model was not trained on. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Self-pulsing and dual-mode lasing in a square microcavity semiconductor laser

Self-pulsing and dual-mode lasing in a square microcavity semiconductor laser are studied experimentally. Self-sustained pulses originating from undamped relaxation oscillation induced by a two-mode interaction are obtained, as the injection current is slightly above the laser threshold. A repetition frequency of 4.4 GHz and a pulse width of 30–40 ps are obtained at a current of 8 mA. The laser switches to continuous-wave operation when the injection current is higher than a certain value, and dual-mode lasing with 30.7 GHz at 16 mA and 10.7 GHz at 27 mA are observed in the lasing spectra. Furthermore, the relative intensity noise spectra are presented to reveal the relationship between the lasing states and the dynamics induced by relaxation oscillation and mode beating.
EXPLORING SOCIAL ENGINEERING EXPLOITATIONS WITH CHATGPT

As technology continues to advance, new opportunities and challenges emerge in the field of social engineering.Chatbots powered by artificial intelligence, such as ChatGPT, have gained prominence as versatile tools for human-computer interaction.This paper explores the intersection of social engineering and AI-powered chatbots, focusing on the risks, mitigation strategies, and ethical considerations associated with their use.By understanding the potential pitfalls and implementing responsible practices, we can harness the power of AI for positive outcomes while safeguarding against malicious intent.
PEMEROLEHAN KOSA KATA BAHASA INDONESIA ANAK USIA TIGA SAMPAI EMPAT TAHUN

Vocabulary acquisition in children aged three to four years begins with the ability to pronounce a word, and develops into two, three, four words and so on, thus forming a simple sentence. This writing aims to examine the acquisition of Indonesian vocabulary for children aged three to four years. This research is qualitative research to obtain accurate data on the acquisition of Indonesian vocabulary for children aged three to four years that living in Bima complex, Lasinrang street, Paleteang district, Pinrang regency. The data collection in this research is library research and field research. The results of this research indicate that children aged three to four years often use nouns rather than verbs, adjectives, and adverbs. Acquisition of vocabulary with affix is still rarely spoken, while confixes and infixes have not been found
Structure a Public-Key Cryptosystem Based on HSS Algebra

NTRU (Number theory research unit) is a public key cryptosystem that uses lattice-based cryptography. It is more resistant to assaults than other commonly used public-key cryptosystems, and its performance has been proven to be much better. This study introduces TRUHIB, a commutative variation of the NTRU, as a novel multidimensional public-key cryptosystem. It operates using a novel mathematical structure that consists of two public keys and five private keys. in a algebra called HSS algebra. This new structure has improved the security and sophistication of the public key cryptosystem.
JoVE Video Dataset

Intertemporal choice plays a crucial role in our daily lives, influencing decisions related to education, health, consumption, and investment. This research proposes an innovative experimental protocol that examines how social comparison and social distance jointly affect the neural processes involved in outcome assessment for intertemporal choices. The study is based on the theoretical framework of cognitive resource competition. This protocol enables researchers to dynamically establish an indifference point for each participant, effectively eliminating the influence of any biased indifference points on the assessment of intertemporal choices. Consequently, the study solely measures the combined impact of social comparison and social distance on how participants evaluate intertemporal choice outcomes. The findings reveal that individuals are more inclined to opt for immediate outcomes under negative unfair conditions. Moreover, compared to the fair and positive unfair conditions, people tend to undervalue delayed outcomes in the negative unfair condition. The strength of this approach lies in its dynamic indifference point setting, making it an effective method to investigate the influence of various external factors (such as social status and power level) on intertemporal decision-making. While the protocol is designed to measure electrophysiological events like event-related potentials, it can also be tailored for use with fMRI.
IBM makes error-correction leap

Chips that connect seven qubits could make correcting errors on quantum computers easier
Implementing Hypergraph Databases with Fast Relationship Linking and Traversal Using ILE

This paper suggests a new method of implementing a hypergraph database system with more robust, very efficient relationship linking. This implementation method is needed because the hypergraph is a very useful data model, yet the extant hypergraph database implementations do not appear to link relationships robustly or quickly compared to ordinary graph databases. Specifically, this paper shows how to implement a hypergraph database so that each hyperedge representing a relationship that links a set of nodes and/or other hyperedges, has direct, ultrafast, bidirectional, pointer-based connections with such nodes and/or other hyperedges. This paper's method of implementing hypergraph databases involves using and extending the author's previously published ILE, or Intentionally Linked Entities, database system.
Study of Overfitting by Machine Learning Methods Using Generalization Equations

The training error of Machine Learning (ML) methods has been extensively used for performance assessment, and its low values have been used as a main justification for complex methods such as estimator fusion and ensembles, and hyper parameter tuning. We present two practical cases where independent tests indicate that the low training error is more of a reflection of over-fitting rather than the generalization ability. We derive a generic form of the generalization equations that separates the training error terms of ML methods from their epistemic terms that correspond to approximation and learnability properties. It provides a framework to separately account for both terms to ensure an overall high generalization performance. For regression estimation tasks, we derive conditions for performance enhancements achieved by hyper parameter tuning, and fusion and ensemble methods over their constituent methods. We present experimental measurements and ML estimates that illustrate the analytical results for the throughput profile estimation of a data transport infrastructure.
Single Image Clothing Style Recognition Using Mask RCNN

Visual fashion style recognition from social media is a key application for online marketing. Currently, such is achieved fully manually, which is ineffectively. We aim to solve it using artificial intelligence. In this paper, a clothing style recognition algorithm based on Mask RCNN is proposed. As the most advanced convolutional neural network, Mask RCNN has made great progress in all aspects of image processing. The paper also contributes a clothing dataset with five different styles. After iterative training, the final MAP of the model reaches 0.79, which can meet the requirements of high-precision clothing recognition. To enable deep learning training and testing, we also propose a fashion style dataset. The experimental results also demonstrate the excellent performance of Mask RCNN in clothing style recognition.
Non-Linear Filtering with Multivariable Skew T Noises

Compared with the Gaussian noise, the heavy tailed and skew Non-Gaussian noise fit the noise characteristics better in the real environment; Similarly, most of the systems in real-world scene are nonlinear. Assuming that the process noise and measurement noise which obey multivariable Skew T distribution, we combine the heavy tailed and skew Non-Gaussian noise with nonlinear system, and develop a variational Bayesian extended Kalman filtering in this paper, which is explained in detail later. then verify the performance of the algorithm from multiple perspectives of simulation data, including: Setting l00s and 300s simulation duration respectively to verify the tracking accuracy and stability of the algorithm; The comparison with the extended Kalman filter shows that the accuracy of this algorithm is higher than that of the former; The tracking accuracy of the algorithm can be improved by increasing the number of iterations. Finally, we summarize the advantages and disadvantages of the algorithm.