Reviewerly/exHarmony huggingface dataset:
Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition

Recognizing human activities from sensor data is a vital task in various domains, but obtaining diverse and labeled sensor data remains challenging and costly. In this paper, we propose an unsupervised statistical feature-guided diffusion model for sensor-based human activity recognition. The proposed method aims to generate synthetic time-series sensor data without relying on labeled data, addressing the scarcity and annotation difficulties associated with real-world sensor data. By conditioning the diffusion model on statistical information such as mean, standard deviation, Z-score, and skewness, we generate diverse and representative synthetic sensor data. We conducted experiments on public human activity recognition datasets and compared the proposed method to conventional oversampling methods and state-of-the-art generative adversarial network methods. The experimental results demonstrate that the proposed method can improve the performance of human activity recognition and outperform existing techniques.
Trustworthy Sensor Fusion against Inaudible Command Attacks in Advanced Driver-Assistance System

There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. How to empirically defend against these inaudible attacks remains an open question. Previous research investigates utilizing deep learning-based multimodal fusion for defense, without considering the model uncertainty in trustworthiness. As deep learning has been applied to increasingly sensitive tasks, uncertainty measurement is crucial in helping improve model robustness, especially in mission-critical scenarios. In this paper, we propose the Multimodal Fusion Framework (MFF) as an intelligent security system to defend against inaudible voice command attacks. MFF fuses heterogeneous audio-vision modalities using VGG family neural networks and achieves the detection accuracy of 92.25% in the comparative fusion method empirical study. Additionally, extensive experiments on audio-vision tasks reveal the model's uncertainty. Using Expected Calibration Errors, we measure calibration errors and Monte-Carlo Dropout to estimate the predictive distribution for the proposed models. Our findings show empirically to train robust multimodal models, improve standard accuracy and provide a further step toward interpretability. Finally, we discuss the pros and cons of our approach and its applicability for Advanced Driver Assistance Systems.
GoMORE: Global Model Reuse for Resource-Constrained Wireless Federated Learning

Due to the dynamics of wireless environment and limited bandwidth, wireless federated learning (FL) is challenged by frequent transmission errors and incomplete aggregation from devices. In order to overcome these challenges, we propose a global model reuse strategy (GoMORE) that reuses the outdated global model to replace the local model parameters once a transmission error occurs. We analytically prove that the proposed GoMORE is strictly superior over the existing strategy, especially at low signal-to-noise ratios (SNRs). In addition, based on the derived expression of weight divergence, we further optimize the number of participating devices in the model aggregation to maximize the FL performance with limited communication resources. Numerical results verify that the proposed GoMORE successfully approaches the performance upper bound by an ideal transmission. It also mitigates the negative impact of non-independent and non-identically distributed (non-IID) data while achieving over 5 dB reduction in energy consumption.
Scaling Spherical CNNs

Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain (via the convolution theorem), which is still costlier than the usual planar convolutions. For this reason, applications of spherical CNNs have so far been limited to small problems that can be approached with low model capacity. In this work, we show how spherical CNNs can be scaled for much larger problems. To achieve this, we make critical improvements including novel variants of common model components, an implementation of core operations to exploit hardware accelerator characteristics, and application-specific input representations that exploit the properties of our model. Experiments show our larger spherical CNNs reach state-of-the-art on several targets of the QM9 molecular benchmark, which was previously dominated by equivariant graph neural networks, and achieve competitive performance on multiple weather forecasting tasks. Our code is available at https://github.com/google-research/spherical-cnn.
The ADAIO System at the BEA-2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues

This paper presents the ADAIO team's system entry in the Building Educational Applications (BEA) 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues. The task aims to assess the performance of state-of-the-art generative models as AI teachers in producing suitable responses within a student-teacher dialogue. Our system comprises evaluating various baseline models using OpenAI GPT-3 and designing diverse prompts to prompt the OpenAI models for teacher response generation. After the challenge, our system achieved second place by employing a few-shot prompt-based approach with the OpenAI text-davinci-003 model. The results highlight the few-shot learning capabilities of large-language models, particularly OpenAI's GPT-3, in the role of AI teachers.
Steganography using Syntactic Method

Information security, which is becoming more crucial as information sharing plays an increasingly significant part in the day-to-day activities of today's people, is one of the methods that may be used for information security.For the sake of information security, we present a method to linguistic steganography that is based on syntactic banks.While the unprocessed input a phrase structure of the sentence is produced by parsing the sentence using the Stanford parser, which can create a phrase structure of the sentence.They create the grammatical structure of the sentence.The input secret message is encoded using Shannon-Fano coding in order to achieve compression.With the absolute minimum, the maximum feasible total bit length after that, the job of syntax transformation looks through the syntax set of the provided.The key-controlled phrase already exists in the syntactic bank and modifies it so that it corresponds to the required syntax that may express the key-controlled This is hidden information that is created semi-randomly and inserted into the text at discrete points.The stego text that is produced as a consequence will still remain the input text is given the appearance of being innocent after a semantically unmodified syntactic transformation is applied to it.Again, we aim to use the SHA-512 hash method to generate a keyed-hash message authentication code (HMAC) to secure the communication.This increases the consistency of the stage text that was produced.
THE STYLE OF INTROVERT AND EXTROVERT IN ANNOTATED BIBLIOGRAPHY WRITING

Writing is highly essential in school, especially in the English Department, since they must master the fundamentals of writing. Unfortunately, writing is considered as the most challenging of the four English language abilities. Numerous researches discovered that EFL students also experienced a variety of issues with academic writing. This issue must be handled carefully since it has an impact on the teachers’ ability to teach writing in the classroom. In order to overcome these issues, students' personalities must be understood. Students' personalities have a critical effect on their capacity to write. Introvert and extrovert are two personality characteristics that contribute to individual differences. This research used descriptive research where the participants were 8 students who took academic writing class. The students were given a personality questionnaire and the result showed 4 students were introvert and 4 others are extrovert. Then the students were given an annotated bibliography test to find the type and key elements of annotated bibliography used by the students. The writer found that the students used summative annotations and combined annotations. 1 introvert student uses combined annotation and 3 introvert students use summative annotation. Whereas for extrovert students, 2 people use combined annotation and 2 other people use summative annotation. Various obstacles experienced by the students thus effecting their academic writing.
Comparative Analysis of Type-1 and Type-2 Fuzzy Controllers: Exploiting Synergies for Improved Control System Performance

The utilization of fuzzy controllers in control systems has gained significant attention in various fields due to their ability to handle complex and uncertain systems. This paper focuses on comparing the performance of two types of fuzzy controllers: type-1 fuzzy controller and type-2 fuzzy controller. While type-1 fuzzy controllers have been widely studied and applied, the advantages of type-2 fuzzy controllers in dealing with uncertain and ill-defined systems have garnered attention. A thorough analysis and comparison are conducted to investigate the practical benefits of type-2 fuzzy controllers. The study includes an overview of type-1 and type-2 fuzzy sets, followed by the design methodology for the two controllers. The designed controllers are implemented practically, and the results are analyzed and discussed. Previous research studies have shown the potential of type-2 fuzzy controllers to achieve superior performance compared to type-1 controllers. This paper contributes to the existing body of knowledge by presenting empirical evidence and practical insights into the performance of type-2 fuzzy controllers in real-world applications. The findings shed light on the benefits and limitations of utilizing type-2 fuzzy controllers, particularly in scenarios where system knowledge is limited or incomplete. The results of this study provide valuable guidance for control system designers and practitioners in selecting the most appropriate controller for their specific applications. Furthermore, the insights gained from this research contribute to the ongoing exploration and development of fuzzy control systems, enhancing their effectiveness in various domains.
SEDIMENTARY DEPOSITION OF METALS IN SEDIMENTS FROM ILHA GRANDE BAY, RJ (BRAZIL)

The industrial development of coastal regions in recent decades and the lack of studies of these systems have led to the need to develop studies on metal contamination that distinguish impacted areas from conserved areas and which can be used to evaluate contamination processes through the study of historical records of deposited material. In the present study, geochemical sedimentary analysis predicts the evolution of the entry and behavior of metals in the Enseada do Abrão in Ilha Grande Bay. Sedimentary evidence was collected to assess concentrations of metals and organic matter and to correlate them with factors possibly controlling their deposition, identifying their respective sources. Furthermore, through 210Pb dating, the deposition of both metals and organic matter in recent decades can be monitored. To this end, analyses of basic compounds (total organic carbon and total nitrogen) and metals (Al, Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, V and Zn) were performed. The data obtained were normalized using Al as the reference element, and average values for shale were used as a baseline for enrichment factors and to calculate the geoaccumulation index. It was possible to estimate sedimentation rates and metal fluxes through 210Pb dating. It could be observed that despite increasing development the analyzed sediments did not have increased concentrations of metals. Furthermore, the concentrations of the elements studied were found to be below the values ​​found in affected areas. Al did not show correlations with other metals, and the enrichment factor did not show increased concentrations of metals in the most recent layers, nor were there many values ​​that would suggest enrichment originating from anthropogenic contribution throughout the studied profile. Based on the values ​​of IGeo, it can be observed that the metals present are within their baseline levels and do not exhibit a significant anthropogenic contribution. The metal fluxes did not show increasing values. These evaluations suggest the absence of contamination in the studied area, and the area can thus still be considered to be one not impacted by metals of anthropogenic origin. This discovered record is useful as a basis for monitoring activities that have expanded in recent decades.
The Role of Social Technologies in Enhancing Organizational Ingenuity: A Survey Study in Asiacell Communications

The study aimed to identify the role played by social technologies in enhancing and supporting organizational ingenuity, and in order to achieve this goal, the study model was built based on what has been reviewed from previous studies. Perceived benefit, perceived benefit, social interaction, social efficiency, perceived encouragement, social influence and facilitation of conditions), while the dimensions of organizational ingenuity were (exploration ingenuity and exploitative prowess). The study showed that there is a significant effect of social technologies on all organizational ingenuity, both exploratory and exploitative in the company in question. proceeds to the company.
The Influence of Constructivist Strategies in Writing the Foreign Language on the Learners Achievements at Higher Secondary Level

Ideas which are built without having any prior knowledge of the subject is not really wrong but called alternative, pre or original concepts. The present study was designed to diagnose alternative conceptions, to discover the best possible reasons behind those alternative conceptions and develop teaching learning constructivist strategies to overcome those alternative conceptions. Moreover, the effectiveness of proposed constructive strategies was found in writing the second language at secondary level. The present research used sequentially exploratory mixed method with the intervention of experimental research design. This research consisted on two phases. In phase-1 grounded theory was used to determine the alternative conception and their reasons in writing the second language at secondary level. The researcher used diagnostic test, diagnostic test interview as research tool in phas-1. In phase-11, pre-test post-test control group design was used. Furthermore, qualitative observation of experimental group was done. Test was used to diagnose alternative conceptions and how they develop. Qualitative data was analyzed by using Cresswell content analysis approach. Quantitative data was analyzed by using SPSS software. Findings of phase-1 were used to develop constructivist strategies to overcome those alternative conceptions. Findings of phase-11 proved that proposed constructivist strategies were effective to overcome the alternative conceptions in writing second language learning process.
WAVE-BASED SIGNAL SECURITY AND PRIVACY STUDIES USING AUTOMATIC HIGH-BIT-RATE OPTICAL COMMUNICATIONS WITH QUANTUM CRYPTOGRAPHIC

The information transfer through the fibers, which converts the electronic signals into light and employs the light emitting and the light source which enables the information transfer, is known as optical communications. Noise reduction is needed for information transfer. More noise is transferred in the optical fiber, which is the main problem with the information transfer. Then the communication multiplies the bandwidth, and the optical communication can be enabled. The high modulation method and quantum cryptographic system manage the modulation method to reduce noise from information transmission. The guided waves for many communication purposes, multi-rate communication, and non-moderate communication can be used in this research. The application of the quantum cryptographic system and the modulation for analog signal processing keep the noise reduction, bandwidth, and optical communication can be improved by up to 97% of noise can be released in the fiber optic for the mortification of the communication system. The noise reduction can be made with optical communication. Up to 80% of noise can be removed in the modulation and the quantum cryptographic. Automatic improvement of the high bit rate communication using the quantum cryptographic and the system to reduce the multiplication and bandwidth using the quantum cryptographic system.
An ERP study of the influence of bodily resonances on emotional prosody perception

A tenet of open source software development is to accept contributions from users–developers (typically after appropriate vetting). But should this also include interventions done as part of research on open source development? Following an incident in which buggy code was submitted to the Linux kernel to see whether it would be caught, we conduct a survey among open source developers and empirical software engineering researchers to see what behaviors they think are acceptable. This covers two main issues: the use of publicly accessible information, and conducting active experimentation. The survey had 224 respondents. The results indicate that open-source developers are largely open to research, provided it is done transparently. In other words, many would agree to experiments on open-source projects if the subjects were notified and provided informed consent, and in special cases also if only the project leaders agree. While researchers generally hold similar opinions, they sometimes fail to appreciate certain nuances that are important to developers. Examples include observing license restrictions on publishing open-source code and safeguarding the code. Conversely, researchers seem to be more concerned than developers about privacy issues. Based on these results, it is recommended that open source repositories and projects address use for research in their access guidelines, and that researchers take care to ask permission also when not formally required to do so. We note too that the open source community wants to be heard, so professional societies and IRBs should consult with them when formulating ethics codes.
Bayesian Additive Regression Trees for Classification of Unbalanced Class of Credit Collectability Data

Aims: This study aims at determining the classification results using the Bayesian Additive Regression Trees (BART) method on bank credit collectability data, where there is a class imbalance in the data.&#x0D; Study Design: Quantitative Design.&#x0D; Place and Duration of Study: The used data are secondary data in the form of bank debtor’s credit collectability data with nine predictor variables and one response variable in the form of credit collectability. They are collected from Banks in East Java, Indonesia, from the date of 01 May 1986 to 31 May 2018.&#x0D; Methodology: The Bayesian approach is one of the estimation methods in statistics that is currently being popularly used, this is because the rapid development of technology makes computational challenges no longer a problem. The Bayesian estimation continues to develop and can be used in various statistical methods, for instance both for regression and classification. The Classification and Regression Trees (CART) method is one of the most popular classification methods used. Debtors, in a bank, who have delinquent credit have a small proportion compared to debtors who have current credit. Standard classifier methods such as CART are not suitable for handling this case, as CART is sensitive to classes that have a high degree. Hence, additional methods such as ensemble BART (Bayesian Additive Regression Trees), are needed in order to increase the accuracy of classification in cases of class imbalance.&#x0D; Results: The results of the cross-validation on the BART show a high consistency of classification accuracy, 83.49%. This indicates that the BART method can work consistently even though there is a class imbalance. The results of this study indicate that the classification accuracy of the training data is 84.53%, while the accuracy in the testing data is 85.48%. These results also show that the BART method has ability to overcome overfitting in the classification method, where overfitting often occurs in most of the classification methods that have very good classification abilities.&#x0D; Conclusion: The testing data show that the accuracy is relatively similar to the one of the training data, this indicates that the BART method has been able to capture patterns in the data.
The Impact of the Application of Information technology governance on financial performance through the mediating role of Banking governance A survey of sample of Iraq banks listed in the Iraq stock exchange

The current study aimed to measure the long-run impact of IT governance in the COBIT framework on the financial performance of Iraqi banks within the framework of banking governance as a mediating variable. by running balanced panel data for a sample of 19 listed Iraqi banks during the period (2018-2020) using the path analysis method. The study concluded that there is a direct positive impact of IT governance on financial performance, as opposed to an indirect, negative impact of IT governance on performance through banking governance. Thus, banking governance plays a mediating role "partial mediation" in the relationship of IT governance with the financial performance of Iraqi banks. Also, this direct effect has a medium practical importance, in contrast to a large practical importance for the indirect effect, while there was no effect size (practical importance) for the total effect. This is because the negative impact of banking governance cancels any direct impact of IT governance on financial performance. Hence, any overall impact of IT governance on financial performance vanishes, and any practical importance of this relationship in the Iraqi accounting and administrative environment vanishes with it. Thus, Iraqi banks must rely on one element of governance only, whether banking governance or information technology governance. Finally, the study also concluded that there is no inverse circular causal relationship between financial performance and the level of banking governance.
The Effectiveness of Using Padlet Application in Developing Scientific Thinking Skills of Tenth Grade Biology Students

The goal of the current study is to determine whether using the Padlet application may have an effect on how biology students in the tenth grade develop their capacity for scientific thought. The researcher uses a quasi-experimental research design to accomplish the study's goals. 62 pupils in the tenth grade made up the study's participants; they were split into two groups: a control group (n = 31) and an experimental group (n = 31). While the latter is instructed utilizing the Padlet app, the former is instructed conventionally. Pre-post-tests of scientific thinking abilities were used to collect the data, and these tests underwent validity and reliability checks. The results showed that using Padlet to improve students' biology learning performance is helpful. Between the means of the two groups, there were statistically significant differences in favour of the experimental group, which performed better on the post-test. The outcomes also showed a statistically significant difference in favour of the experimental group for both the overall performance of skills and the score of each ability individually on the post-test of scientific thinking skills. The study came to various conclusions in light of the data, the most crucial of which is the requirement to keep using the Padlet app in instruction.