Reviewerly/exHarmony huggingface dataset:
Assisting Deaf and Hard-of-Hearing People in Critical Situations: Alleviating Stress and Enhancing Safety

Individuals who are deaf or hard of hearing encounter many difficulties in their daily lives, especially when it comes to receiving important information during emergencies or disasters. Relying on others for updates can create a dependency that may hinder their ability to respond independently and effectively in critical situations. To address this problem, it is crucial to have a warning system that can provide timely alerts directly to deaf and hard-of-hearing individuals, reducing their reliance on family members, tutors, or caregivers during emergencies. Our team has developed a mobile application that can notify individuals of unavoidable events like earthquakes or fires. We used App Inventor, a user-friendly development platform, to create the app, which can be installed on wearable devices for easy accessibility. By utilizing wearable technology and mobile connectivity, our app aims to bridge the communication gap that deaf and hard-of-hearing individuals experience during emergencies. It includes several essential features to enhance its effectiveness, such as customized alert settings based on individual preferences, such as the type of alerts, urgency level, and notification formats (visual or vibration). The app also offers real-time location tracking, which can provide personalized localized alerts, giving deaf and hard-of-hearing individuals crucial information about their immediate surroundings. Furthermore, the app has multilingual support to cater to diverse deaf and hard-of-hearing communities, ensuring that signals and information are conveyed in their preferred languages.
Analysis of Perception Types on Research and Development Programs of Creative and Challenging Defense Science and Technology using Q Methodology

In this study we aimed to reveal the types and characteristics of subjective perceptions regarding institutional operation management (project selection and evaluation, performance utilization, R&D environment) for R&D programs of creative and challenging defense science and technology. For this, we conducted literature review and in-depth interviews with mixed quantitative-qualitative Q methodology approach and developed 33 Q sample statements (Q-set). Subsequently, survey data obtained by a convenience sample of 21 incumbents (P sample), working in South Korea’s defense and military area were analyzed by principal components and varimax rotation with qfactor package. The findings showed found four distinct perspectives: Concerned about the lack of professional evaluators for creative challenge R&D projects ( Type 1 ) , Emphasizes t he u se o f challenging opportunities (Type 2), Pursues t he c reating of challenging R&D environment through the failure experience in the research process (Type 3), Prevention-focused R&D projects evaluation (Type 4).
Private Electronic Road Pricing using Bulletproofs with Vector Commitments

We present a novel approach to privacy preserving electronic road pricing (ERP) based on on-board units (OBUs) and zero-knowledge proofs (ZKPs), and without any need for tamper-proof elements. Since our approach is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">software-only</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">protocol-enforced</i> , it can be rapidly deployed on off-the-shelve or even pre-existing hardware, such as a smartphone or the on-board computer of a car. In addition, communication complexity is only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">logarithmic</i> in function of route length, such that even for short routes the communication cost of the protocol is lower than the cost of naively transmitting the clear text route. Our implementation proves the construction to be computationally practical, especially for the verifier. Since the scheme is based on ZKPs, no unnecessary information gets leaked. At the basis of the scheme lies Bulletproofs, which is modified to provide native support for Pedersen vector commitments with logarithmic impact on proof size.
Effective face recognition with hybrid distance-key frame selection using TBO-ensemble model

The enormous amount of data contained in the video image has grown rapidly along with surveillance, greatly outpacing the capacity of human resources to handle it effectively. Smart surveillance retrieval is an essential component of any modern video surveillance system, considerably boosting the effectiveness, precision, and interoperability of the system. The use of face recognition and other cutting-edge technology in the security surveillance system is rapidly rising. Therefore, in this article, the distributed deep convolutional neural network (DCNN) and distributed deep BiLSTM is proposed to efficiently detect the face from the video. One of the major contributions involved in this research relies on the key frame selection, where four unique distance measurement techniques are fused, and is named hybrid distance- key frame selection. The Tri birds optimization (TBO) technique selects the best solution from a large number of solutions for the ensemble model classifier engaged in face recognition. The ensemble model classifier incorporates various hyper-parameters that are optimally trained. Multiple test videos with 401 and 802 test videos are used as the input for the TBO-ensemble model that attains 97% accuracy, 98.33% precision, recall, and f-measure for epoch 50 and the 500 number of retrievals, respectively.
Understanding Human Dynamic Sampling Objectives to Enable Robot-assisted Scientific Decision Making

Truly collaborative scientific field data collection between human scientists and autonomous robot systems requires a shared understanding of the search objectives and tradeoffs faced when making decisions. Therefore, critical to developing intelligent robots to aid human experts, is an understanding of how scientists make such decisions and how they adapt their data collection strategies when presented with new information in situ . In this study we examined the dynamic data collection decisions of 108 expert geoscience researchers using a simulated field scenario. Human data collection behaviors suggested two distinct objectives: an information-based objective to maximize information coverage, and a discrepancy-based objective to maximize hypothesis verification. We developed a highly-simplified quantitative decision model that allows the robot to predict potential human data collection locations based on the two observed human data collection objectives. Predictions from the simple model revealed a transition from information-based to discrepancy-based objective as the level of information increased. The findings will allow robotic teammates to connect experts’ dynamic science objectives with the adaptation of their sampling behaviors, and in the long term, enable the development of more cognitively-compatible robotic field assistants.
Perancangan Prototype Kartu Pelajar Cerdas Di Ma Insan Kamil Kota Sorong Berbasis Rfid

The purpose of this study is to optimize the use of student cards owned by students, to perform automatic student attendance and as a student's school exam card each semester. The method used is to integrate three main parts, namely the RFID System (cliet), Gateway System, and Server. The designed student card contains a UID (RFID Tag) which is read by an RFID reader every time students enter class for attendance and also when taking school exams. The RFID tag will be data that will be forwarded to Arduino Mega as a data processor and forwarded through the System Gateway to a server in the form of an orange pi so that it can be monitored by the operator. The test results for 5 times the test, the average response time for network access and getting the IP address from the router is 16.6 seconds, the average time the device responds to active student RFID card data from the database is 2.6 seconds. The average time of student's RFID card access to the device to the web server application is 3.8 seconds
IoT-based Smart Pill Reminding System

Background: The concept of pill reminders has been discussed and developed throughout the decade. It varies from cascaded plastic pill boxes to complicated robust dispensers. This proposed smart pill reminding system based on IoT is being designed by considering ease-to-use and cost-effectiveness. Method: A smart pill reminding system is a system that will alert the patient to take their respective pill at the desired time. It will also track the motion of the patient’s hand while taking the pill and will also display the pill count on an LCD Screen. In case a patient forgets/ignores the reminder provided by the system, the system will automatically display the status on the application that will be installed in the relative/caretaker’s phone and through an email on the patient's relative/caretaker’s email address to take subsequent action. The system will monitor the real-time using an RTC module, and as and when the current time matches the medicines time, it will activate its mechanism, and the patient will have a buffer time to take their medicine. In case a patient does take the medicine in the buffer time provided by the system, then one mechanism of the system will be activated. In another case, if a patient does not take the medicine in the stipulated time, further actions will be initiated by the system to benefit the patient. Results: It was tested and found that out of ten times, the system worked accurately nine times, with calculated accuracy as high as 90%. Initially, the Blynk application will display “Welcome Patient” and “You will be updated”. Once the RTC matches the scheduled time to take medicine, the buzzer starts buzzing. If the IR sensor detects the movement of the user’s hand, the LCD will update the pill count, and the pill count is reduced by one. The LCD will also display the message “Medicine Taken”. If the IR sensor does not detect the movement of the user’s hand, the LCD will display the same pill count. The LCD will also display the message “Med1 not Taken”. The Blynk will also be updated and will display the same messages. The green LED shows the status of the consumption of the pill. The email will sent to the caretaker in this case. Conclusion: In this work, all the problems related to the system are overcome in a systematic manner with the help of IoT and basic electronic applications. Thus, the system will help the user take all the prescribed medicines on time and will also update the user’s caretaker via application and email services. This will largely help the working class as well as the senior citizens. Thus, this proposed system can be commercialised as a handy and cost-effective device.
PERKEMBANGAN KEJIWAAN MUSA DALAM NOVEL MADIELIEF KARYA KIRANADA: KAJIAN TEORI PSIKODINAMIKA SIGMUND FREUD

&lt;p&gt;Novels are never separated from psychological values because the problems told in novels are always related to the psychology of the characters created by the author. This psychological aspect is usually displayed through the character traits and behavior of the characters that are a problem as experienced by humans in real life. Like the novel Madielief by Kiranada which tells the psychological problems of the characters in it. Therefore, this research is examined using psychodynamic theory which aims to describe the psychological development of characters according to Sigmund Freud. This type of research is qualitative research with a literary psychology approach. The data were obtained from the novel Madielief by Kiranada. Research data are in the form of words, phrases, and sentences in paragraphs related to the problem statement. Data collection techniques using reading and note techniques. The results of this study indicate 1) Musa’s personality background in Kiranada’s Madielief novel, 2) Musa’s neurotic form of anxiety in Kiranada’s Madielief novel, 3) The psychological development of Musa in the Canadian Madielief novel&lt;/p&gt;
Analisis Kualitas Air Tanah berdasarkan Parameter Mikrobiologi (Studi Kasus: Kapanewon Ngaglik, Yogyakarta)

Yogyakarta, Indonesia, has various municipal wastewater treatment (IPAL) facilities; however, only 40% are operational. The study examines how IPAL affects groundwater quality and identifies its causes. Ngaglik, one of three province districts with the largest IPAL number and population density, is the research site. This study only measures microbiological groundwater quality based on Total coliforms and Eschericia coli, according to APHA 2017. Inverse distance weighing (IDW) maps microbiological groundwater quality. The results of the study showed that the observed area had Total Coliform values of 0–170 MPN/100 ml and Escherichia coli concentrations of 0–7. Eleven of the twelve test samples met Permenkes drinking water quality requirements No. 492/Menkes/Per/IV/2010 and PerGub DIY No. 20 of 2008. Seven E. coli concentration measurements exceeded drinkable water requirements. This study showed that E. coli bacteria may have infiltrated groundwater near the sample site. The incident was the first sign that Kapanewon Ngaglik Municipal IPAL was malfunctioning. This data should help assess and improve the effectiveness of the municipal IPAL at the study site and other local IPALs.
Self-C2AD: Enhancing CA Auditing in IoT with Self-Enforcement Based on an SM2 Signature Algorithm

Malicious certificate authorities (CAs) pose a significant threat to the security of the Internet of Things (IoT). Existing CA auditing schemes primarily rely on passive detection and public data collection, lacking real-time and comprehensive monitoring. In this paper, we propose a novel double-authentication preventing signature scheme based on an SM2 algorithm, referred to as Dap-SM2. We further enhance its functionality by introducing Self-C2AD, a CA auditing mechanism with self-enforcement. In our proposed mechanism, any malicious CA that generates two certificates with different descriptions (such as public key and basic information) for the same IoT device will immediately lose its private key. To ensure the security of our proposed scheme, we provide a detailed security analysis of Dap-SM2. The analysis demonstrates that our Self-C2AD mechanism meets the necessary security requirements, offering robust protection against malicious CAs. Additionally, we conduct an efficiency evaluation and present experimental data to illustrate the promising potential of our construction for future IoT applications. By introducing the Dap-SM2 scheme and the Self-C2AD mechanism, we address the critical issue of malicious CAs in the IoT domain. Our approach provides real-time and comprehensive auditing capabilities, surpassing the limitations of existing schemes. The security analysis confirms the effectiveness of Dap-SM2, while the efficiency evaluation and experimental data demonstrate its suitability for practical IoT applications. In summary, our work presents a novel solution to combat the threat of malicious CAs in the IoT context. The Dap-SM2 scheme, coupled with the Self-C2AD mechanism, offers enhanced security and real-time auditing capabilities. The security analysis validates the robustness of our approach, while the efficiency evaluation and experimental data showcase its potential for future IoT deployments.
E-Gram Panchayath

A student initiative called "E-Gram Panchayath" seeks to modernize and digitize the operations of rural local government entities, particularly gram panchayats in India. The project's objective is to provide a web-based platform that will help gram panchayats carry out their obligations and responsibilities, such as the administration of local development, management of community resources, and provision of public services projects. The platform will offer a variety of functions, such as online complaint forms and a platform for accessing official data and papers, tools for monitoring the progression of requests, and development initiatives. The project's objective is to increase the accountability, openness, and efficiency of gram better outcomes for the communities they serve as a result of panchayats.
Dithering techniques and segmentation analysis on a mammogram breast cancer image

The objective of the publishing industry is to minimize the number of colors in an image for printing. Also, to retain the better quality from the picture, some useful techniques are needed with the aid of only a few colors. The contouring consequences occur when an image quantized bluntly regardless of the quantization, either uniform or non-uniform. To alleviate the issues of contouring, a slight amount of random noise is added (dithered) to the original image, followed by the quantization method. The methods such as dithering and half toning are extensively used strategies in acquiring texts and pictures in books, newspapers, magazines, computer monitors, and LCDs. This article investigates the dithering technique on a mammogram image with different procedures, such as dithering followed by quantization and dithering, quantization followed by filtering are detailed with Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) results. For better visualization, segmentation techniques are applied to the dithering technique.
An image style transfer algorithm based on attention mechanism and GhostNet

Image style transfer, which combines the content of one image with the style of another to create a new image, has many potential applications in the disciplines of image creation, creative style reproduction, animation production, and other areas. The majority of early image style transfer techniques used manual features. Due to advances in deep learning technology, the Generative Adversarial Network (GAN) method greatly increases the accuracy of image style transfer; however, when it comes to scenes with large color blocks, high resolution, or horizontal boundaries, their performance still falls short of practical application requirements. In this research, we present a GhostNet- and attention-based image style transfer technique. We specifically introduce the SELayer to learn and increase the weight value of each channel, which can improve the quality of style transfer and data processing capacity. In addition, GhostNet is included to accelerate image production even more. The findings of the experiments, both quantitative and qualitative, demonstrate that the suggested technique can enhance the impact of style transfer.
Authorship Attribution of Literary Texts Using Named Entity Masking and MaxLogit-Based Sequence Classification for Varying Text Lengths

This paper explores the problem of identifying an author based on text passages of varying length, ranging from 100 to 2,000 words. The study builds on previous research on authorship attribution of Polish literary texts, finding that the TF-IDF with multilayer perceptron outperforms other techniques. The study investigates whether the issue with BERT in authorship attribution can be mitigated by removing named entities from the input data and replacing posteriori probabilities with logits in sequence classification. The results demonstrate that machine learning methods are capable of almost perfect authorship attribution on short texts, and the proposed MaxLogit approach significantly improves results. However, except in the case of short passages up to 400 words, better results are obtained with TF-IDF than with BERT. The study concludes with a discussion of the results and suggestions for future research.
Detection of Anomalies in Time Series Data

The detection of Anomaly in time series data has gained a significant amount of popularity due to its indulgence in various domains of industry such as healthcare, the financial sector, industrial services, and other several reasons. This paper represents an elaborate survey of varied anomaly detection techniques that are tested on time series data. The aim of the paper is to identify various anomalous outlines in the dataset through a number of different approaches, algorithms, and techniques. At the beginning of the paper, the importance of detecting anomalies in real-world scenarios is explained followed by describing the efficient techniques capable of dealing with it. The paper covers all the aspects of anomaly detection starting from traditional analytical and statistical models to modern-day advanced machine learning methods. The moving averages, exponential smoothening, and process control which are considered to be the traditional approaches for detecting anomalies in Machine learning-based models such as one-class Support Vector Machine (SVM), deep learning-based models, and isolation forests are the distinct topics that are covered in the paper. Moving ahead, the paper revolves around addressing different challenges and limitations associated with the detection of anomalies in time series data. The challenges circle around topics such as selecting appropriate evaluation metrics, addressing class imbalance, handling high-dimensional data, and how to deal with concept drift. Through a systematic analysis of the existing works, this paper aims to provide data scientists, practitioners, and researchers with summarized information for understanding the background of anomaly detection methods. Finally, this paper delves into a number of methodologies used in advancing anomaly detection in time series data and justifying their impact on real-world applications.
Alpha-Numeric Character Recognition for Blinds Using Deep-Learning Techniques

Abstract: In this study, we are introducing an alphanumeric character recognition system using Deep Learning concepts. This system will classify Alphabets and Numeric characters with high accuracy. Identification and classification of characters would be essential for users with who have problem reading the characters in a document or any other text-based media. We utilize a dataset containing of letters A to Z with a total of 3,89,765 images which is approximately 15000 images per Alphabet. We classify the characters based on the shape of each character and using Deep Learning concepts like CNN, RNN and Random Forest architecture to train out the dataset and develop a system which produces the highest accuracy. The success rate in identifying the characters makes it a very easy and an efficient tool.