Reviewerly/exHarmony huggingface dataset:
Revolutionizing Employee Well-being and Performance: Personalized Emotional Chatbots for Banking Professionals in Tamil Nadu

In this article, we delve into personalized emotional chatbots and their potential to enhance the well-being and performance of banking employees in Tamil Nadu. With the banking sector in the region experiencing heightened levels of strain, it is imperative to consider the impact of employee stress and burnout. By harnessing innovative technologies and psychological principles, personalized emotional chatbots offer customized support to mitigate these hurdles. Through carefully examining relevant research and real-life examples, this article illuminates the advantages and possible implementations of these chatbots in the banking industry, ultimately promoting a more positive work culture and increased job satisfaction.
Supporting the Development of Classroom Reading Corner Program at Madrasah Ibtidaiyah Muhammadiyah 01 Pekanbaru Title

The habit of reading should be introduced to children at an early age for them to continue reading throughout their lives. A well-planned and facilitated program for habituating readers is therefore necessary. This Community Service activities aim to provide assistance for the development of the Class Reading Corner Program at Madrasah Ibtidaiyah Muhammadiyah 01 Pekanbaru. Following a review of the school's problems regarding the lack of reading corners, a limited number of children's reading books, and the lack of governance of class reading corners, these activities were carried out based on the results of an analysis of these problems. As part of this program, 48 sets of children's picture books were handed over, including mini bookshelves. The program also socialized development of reading corner program and made a simulation of making picture books involving 14 teachers, 1 administrative staff, and 1 headmaster. This program employed a direct observation and questionnaire for the evaluation. The observation showed that three classroom reading corners are present and in use at the school. A satisfaction level of 89.6% was obtained from activity participants after analyzing the questionnaire, an increase of 29.20% over that obtained prior to socialization and simulation. Furthermore, 93.75 % of the participants wanted a reading corner and 100% indicated that there was a need to enhance or improve its quality. The results from these studies suggest that this mentoring program has achieved a positive effect by raising awareness of the importance of increased reading proficiency in schools.
The Design of a C1 Document Data Extraction Application Using a Tesseract-Optical Character Recognition Engine

The 2019 election process used the Vote Counting Information System, also known as Sistem Informasi Penghitungan Suara (Situng), to provide transparency in the recapitulation process. The data displayed in Situng is from document C1 for 813,336 voting stations in Indonesia. The data collected from the C1 document is entered and uploaded into Situng by the officers of the Municipal General Election Commission (GEC). Since this process is performed by humans, it is not immune to errors. In the recapitulation process of the 2019 election results, there were 269 data entry errors, and the data entry process also did not run according to the specified target, resulting in delays. Furthermore, there were cases of C1 document modification, raising concerns about the data's authenticity. To avoid human errors and increase data entry speed, automatic data entry is a plausible option. The data entered are text data in image documents with the same template format, so that optical character recognition (OCR) can be used to read the text while improving image quality and alignment, resulting in a more accurate OCR reading area. In this study, we developed a C1 document data extraction application using the waterfall SDLC method, which has undergone a systematic and thorough process. The application was developed using Tesseract optical character recognition. Tesseract is an open-source OCR engine and command-line program that allows for the recognition of text characters within a digital image. The accuracy obtained by using this method is still not optimal as a substitute for Situng's data entry officer. To guarantee the integrity of the C1 document, we use the RSA-2048 digital signature scheme. The use of the Tesseract-OCR Engine for character recognition, combined with digital signature capabilities, provides a comprehensive solution to reduce the human error factor that can lead to miscalculations and inaccurate processes.
Development of an Early Warning System Using Social Media for Flood Disaster

This research paper introduces an innovative prototype system that uses IoT technologies to monitor floodwater levels. Integration of an ultrasonic sensor, ESP8266 microcontroller, Arduino IDE, and the ThingSpeak platform aims to establish a robust flood monitoring solution. The paper provides a thorough exploration of the system's background, the problem it addresses, the methodology employed, and the obtained results, along with insights into future research directions. The study meticulously describes the design, implementation and programming code for data collection and transmission within the system. Through extensive field testing and meticulous data analysis, the paper evaluates the precision and effectiveness of the proposed flood monitoring solution. In particular, research underscores the advantages of IoT, emphasizing real-time data collection, logging, and analysis as essential components for efficient flood management. Additionally, the paper elucidates step-by-step instructions for configuring Telegram notifications through the ThingSpeak React app, enhancing the practical applicability of the developed system. The research effectively highlights the potential of IoT in flood monitoring, showcasing its superior accuracy and effectiveness compared to traditional methods. By demonstrating the feasibility and advantages of IoT in the context of flood monitoring, this study contributes valuable information, enriching existing knowledge, and paving the way for future advances in the field. Research encourages the continued exploration of advanced techniques to strengthen flood monitoring and management strategies. Ultimately, this work presents a comprehensive IoT-based prototype for floodwater monitoring, offering valuable information and fostering the promising role of IoT technologies in this critical domain.
A Novel Framework for Information Security During the SDLC Implementation Stage: A Systematic Literature Review

This research delves into the critical aspects of information security during the implementation stage of the Software Development Life Cycle (SDLC). By employing a systematic literature review, the study synthesizes findings from various digital repositories, including IEEE Xplore, ACM Digital Library, Scopus, and ScienceDirect, to outline a comprehensive framework addressing the implementation stage's unique security challenges. This research contributes to the field by proposing a novel assurance model for software development vendors that focuses on enhancing information security measures during the implementation stage. The study's findings reveal 12 key steps organizations can adopt to mitigate security risks and enhance information security measures during this critical phase. These steps provide actionable insights and strategies tailored to support security protocols effectively. The article concludes that by incorporating these steps, organizations can significantly improve their security posture, ensuring the integrity and reliability of the software development process, particularly during the implementation stage. This approach not only addresses immediate security concerns but also sets a precedent for future research and practice in secure software development, particularly in the critical implementation stage of the SDLC.
Investigating the Impact of ReLU and Sigmoid Activation Functions on Animal Classification Using CNN Models

VGG16 is a convolutional neural network model used for image recognition. It is unique in that it only has 16 weighted layers, rather than relying on a large number of hyperparameters. It is considered as one of the best vision model architectures. This study compares the performance of ReLU (rectified linear unit) and sigmoid activation functions in CNN models for animal classification. To choose which model to use, we tested 2 state-of-the-art CNN architectures: the default VGG16 with the proposed method VGG16. A data set consisting of 2,000 images of five different animals was used. The results show that ReLU achieves higher classification accuracy than sigmoid. The model with ReLU on convolutional and fully connected layers achieved the highest accuracy of 97.56% on the test dataset. However, further experiments and considerations are needed to improve the results. Research aims to find better activation functions and identify factors that influence model performance. The data set consists of animal images collected from Kaggle, including cats, cows, elephants, horses, and sheep. It is divided into training and test sets (ratio 80:20). The CNN model has two convolution layers and two fully connected layers. ReLU and sigmoid activation functions with different learning rates are used. Evaluation metrics include precision, precision, recall, F1 score, and test cost. ReLU outperforms sigmoid in accuracy, precision, recall, and F1 score. However, other factors such as the size, complexity and parameters of the data set must be taken into account. This study emphasizes the importance of choosing the right activation function for better classification accuracy. ReLU is identified as effective in solving the vanish gradient problem. These findings can guide future research to improve CNN models in animal classification.
AUV Surfacing Control With Adversarial Attack Against DLaaS Framework

In this study, we consider surfacing control problems for the autonomous underwater vehicle in three-dimensional space under emergencies. Most model-based controllers cannot effectively solve these problems due to the unknown task environment model. Moreover, existing deep reinforcement learning (DRL) methods have some limitations, such as slow convergence or overestimation bias. For these purposes, we propose a model-free DRL algorithm based on the Deep Deterministic Policy Gradient within the paradigm of deep learning as a service (DLaaS). The algorithm combines existing expert episodes as demonstration and transitions from the interaction between the agent and task environment, which potentially reduce risk from adversarial attack. We propose a variable delay learning mechanism to improve the performance of the proposed algorithm. The simulation results show that our method can converge faster and has a more robust performance with adversarial attack than the existing DRL method under the premise of solving the AUV surfacing control tasks for safety.
Public and Private Clouds in Hands-on, Online Learning

Adoption of cloud computing is a clear trend in online learning.What types of cloud platforms are available?What are the pros and the cons of different cloud options?We will discuss cloud options for hands-on education, based on our experience with multiple cloud platforms.There are different ways of implementing cloud computing.A public cloud is owned and operated by an external service provider.Computing resources are allocated on a pay-per-usage basis.A private cloud is owned and managed by local institutions.Computing resources are provisioned to internal users on demand.Private clouds are highly customizable and may be adjusted to better meet the needs of local users.However, setting up a private cloud requires expertise and resources.Access to public clouds can be set up quickly but users possess less control.In a diversified online learning environment, multiple cloud platforms may be necessary.
The Design and Implementation of an Intelligent Letter Box

A major cost of transportation for the US postal services is collecting mail from blue collection boxes across the country, with their numbers steadily declining over the years, to save on fuel costs.This cost could be significantly curtailed if boxes that contain mail at collection times can be identified.This paper provides a detailed study and implementation of a smart mail detection and alerting system.The system detects if any mail is placed in the blue collection box and sends an alert to the local post office indicating whether the box should or should not be visited.
Research Experience for Secondary School Teachers on Renewable Energy: Design and Implementation of a Small Scale Solar Tracker

This paper describes the experiences gained during the first year implementation of a Research Experiences for Teachers (RET) in Renewable Energy (ENERGY) at Georgia Southern University (GSU).RET is a NSF grant program that supports the professional development for secondary school teachers with emphasis on hands-on laboratory research and experiences in STEM fields.The paper also refers to the teachers' experiences gained at the GSU Engineering Research Labs during the summer 2017.Participants in the RET program completed 280 hours (seven weeks) of training and research experience in renewable energy topics.About 50 hours were devoted to lectures and workshops, and the rest, about 230 hours, were research activities, collaborating in research teams with undergraduate and graduate students, and engineering professors.Participants also developed lessons for their courses that were delivered to their own students during the 2017-2018 school year.The lessons were directly related to the experiences that teachers gained in the summer.
A Personalized Computer Based Tutor for Vector Algebra

Many of the world's challenges today need to be solved through advances in one or more of the STEM disciplines.This calls for preparing our students to be tomorrow's leading engineers and scientists.It is vital that students in a freshmen class understand how to analyze vectors, and split an arbitrary vector into components along different directions.From a student point of view, concepts in vectors can seem challenging, especially if they do not have strong knowledge background in geometry and trigonometry.We made a computer based personalized tutoring system using state of the art tools provided by Carnegie Mellon University's Cognitive Tutor Authoring Tools (CTAT).In this work, we describe steps that went into building this highly effective tutoring system that challenges the students with a problem, provides immediate feedback, and offers need based hints at different levels, leading the student to solve the problem correctly.
Improved and formal proposal for device-independent quantum private query <sup>*</sup>

Abstract In this paper, we present a novel quantum private query (QPQ) scheme that is fully device-independent. As far as we know, this is the first QPQ scheme that uses EPR pairs and offers full device independence. Our approach takes into account the self-testing of shared EPR pairs and the self-testing of projective measurement operators in a mistrustful scenario where neither the client nor the server trusts each other. To achieve full device independence, we also exploit self-testing of a specific class of POVM operators used by the client in our scheme. Additionally, we evaluate the performance of our proposal formally and derive an upper limit of the maximum cheating probabilities (when the protocol does not terminate) for both the dishonest client and the dishonest server.
A Study of Online Assessment Tools to Practice Programming and Their Effect on Students Grades

Abstract "Practice makes perfect" is an old phrase that proves truth in many aspects of the life of a computer engineer. Students in programming courses are reminded constantly by their instructors to practice in order to become better developers. Traditionally, book exercises have been used or assigned to students for practicing programming. However unless these exercises are counted for credit, some students will lack the motivation to do them. On the other hand, assigning too many problems for credit can become a time consuming activity for both students and faculty as well as a grading burden for instructors. It is also known that there are a lot of online resources for practicing programming, but students can get overwhelmed with so many tools. In this work in progress paper we present our preliminary results of how using online assessment tools can help student practice and improve their programming skills. The tools used provide immediate feedback and automatic grading. The hypothesis is that these tools help students to practice more and by giving them immediate feedback and quick grading, they get better at programming and consequently get better test scores. Preliminary data collected shows this to be the case. In this paper we present different scenarios how these tools were used and their effect in the final exam results in different semesters.
Introducing Emerging Computer Engineering Research to Community College Students through a Summer Internship Project on Development of a Mobile Gesture Recognition System

Abstract Community colleges play an important role in STEM education because they enroll almost half of the nation's undergraduate students. Undergraduate research experience has been proven effective to engage and retain students in the STEM fields. In order to provide community college students with opportunities and experiences in cutting-edge engineering research, a cooperative internship program between a community college and a public comprehensive university has been developed. In summer 2018, the program engaged 20 community college students in six 10-week engineering research projects conducted at the four-year university. This paper mainly describes the computer engineering internship project, in which four sophomore students from the community college developed a mobile gesture recognition system by integrating bio-signal processing, machine learning, real-time system design, and mobile and cloud computing technologies. The project was conducted in a research lab at the four-year university and was supervised by a faculty advisor and a graduate student mentor. The research outcome of the project as well as the results of the pre- and post-program surveys show that the internship program was a great success in allowing the student interns to gain valuable computer engineering research experience and strengthening their confidence and interest in pursuing a STEM profession. The program also helped the students improve their skills in teamwork, time management, scientific writing, and presentation.
A Minimum Discounted Reward Hamilton–Jacobi Formulation for Computing Reachable Sets

We propose a novel formulation for approximating reachable sets through a minimum discounted reward optimal control problem. The formulation yields a continuous solution that can be obtained by solving a Hamilton-Jacobi equation. Furthermore, the numerical approximation to this solution is the unique fixed-point to a contraction mapping. This allows for more efficient solution methods that are not applicable under traditional formulations for solving reachable sets. Lastly, this formulation provides a link between reinforcement learning and learning reachable sets for systems with unknown dynamics, allowing algorithms from the former to be applied to the latter. We use two benchmark examples, double integrator, and pursuit-evasion games, to show the correctness of the formulation as well as its strengths in comparison to previous work.
Indistinguishability Obfuscation from Well-Founded Assumptions

At least since the initial public proposal of public-key cryptography based on computational hardness conjectures, cryptographers have contemplated the possibility of a "one-way compiler" that translates computer programs into "incomprehensible" but equivalent forms. And yet, the search for such a "one-way compiler" remained elusive for decades. We examine a formalization of this concept with the notion of indistinguishability obfuscation (iO) . Roughly speaking, iO requires that the compiled versions of any two equivalent programs (with the same size and running time) be indistinguishable from any efficient adversary. Finally, we show how to construct iO in such a way that we can prove the security of our iO scheme based on well-studied computational hardness conjectures in cryptography.
Multiview Deep Anomaly Detection: A Systematic Exploration

Anomaly detection (AD), which models a given normal class and distinguishes it from the rest of abnormal classes, has been a long-standing topic with ubiquitous applications. As modern scenarios often deal with massive high-dimensional complex data spawned by multiple sources, it is natural to consider AD from the perspective of multiview deep learning. However, it has not been formally discussed by the literature and remains underexplored. Motivated by this blank, this article makes fourfold contributions: First, to the best of our knowledge, this is the first work that formally identifies and formulates the multiview deep AD problem. Second, we take recent advances in relevant areas into account and systematically devise various baseline solutions, which lays the foundation for multiview deep AD research. Third, to remedy the problem that limited benchmark datasets are available for multiview deep AD, we extensively collect the existing public data and process them into more than 30 multiview benchmark datasets via multiple means, so as to provide a better evaluation platform for multiview deep AD. Finally, by comprehensively evaluating the devised solutions on different types of multiview deep AD benchmark datasets, we conduct a thorough analysis on the effectiveness of the designed baselines and hopefully provide other researchers with beneficial guidance and insight into the new multiview deep AD topic.
A Pseudoinversion-Free Method for Weight Updating in Broad Learning System

Neural networks have evolved into one of the most critical tools in the field of artificial intelligence. As a kind of shallow feedforward neural network, the broad learning system (BLS) uses a training process based on random and pseudoinverse methods, and it does not need to go through a complete training cycle to obtain new parameters when adding nodes. Instead, it performs rapid update iterations on the basis of existing parameters through a series of dynamic update algorithms, which enables BLS to combine high efficiency and accuracy flexibly. The training strategy of BLS is completely different from the existing mainstream neural network training strategy based on the gradient descent algorithm, and the superiority of the former has been proven in many experiments. This article applies an ingenious method of pseudoinversion to the weight updating process in BLS and employs it as an alternative strategy for the dynamic update algorithms in the original BLS. Theoretical analyses and numerical experiments demonstrate the efficiency and effectiveness of BLS aided with this method. The research presented in this article can be regarded as an extended study of the BLS theory, providing an innovative idea and direction for future research on BLS.