Reviewerly/exHarmony huggingface dataset:
MULTI MODAL EMOTIONAL RECOGNITION SYSTEM USING FACIAL RECOGNITION AND SKEW GMM

Abstract-Emotion recognition systems have a significant role in many areas including forensic application, teleconferencing, phone conversation and other areas. These emotions that are exhibited by the individuals can be sensitized through the facial expression and in remote conversations. These emotions can be figured out using the speech signals. These speech signals that are articulated from the voice can be generated and recorded. The recorded pattern using the features the emotions generated can be identified. Emotional recognition is a major research area in speech recognition. The features of the emotions will affect the recognition efficiency of the speech recognition systems. Several techniques are used in recognizing the emotions. This paper contributes towards a novel methodology based on Multi modal emotion recognition system using Skew Gaussian mixture model, the updated equations are derived from using EM Algorithm. The verbal communication signals are recorded into WAV format, which are used for research, collectively with the facial gestures. The emotions of both male and female are obtained using LPC, MFCC, and SDC. Keywords- Emotional recognition, Skew Gaussian mixure model, EM algorithm,LPC, MFCC and SDC.
A Review of Fuzzy Cognitive Maps Extensions and Learning

Fuzzy Cognitive Maps (FCM) is a soft computing technique whose vertices and edges are fuzzy values with an inference mechanism for solving modelling problems; it has been used in modelling complex systems like industrial and process control. The concept was first introduced in 1986, with an initial learning algorithm in 1996; several works have been published on FCM methodology, learnings and applications. Fuzzy cognitive maps continue to evolve both in theory, learning algorithms and application. Many theories like intuitionistic theory, hesitancy theory, grey system theory, wavelet theory, etc., are integrated with the conventional FCM. These extensions have improved Fuzzy cognitive Maps to handle problems of uncertainty, incomplete information, hesitancy, dynamic systems and probabilistic fuzzy events. They also strengthen fuzzy cognitive Maps’ modelling power for application in almost any domain. However, the compilation of the development in methodology and adaptation of FCM are either old or omitted some of the recent advances or focused on specific applications of FCM in some areas. This paper reports extension, learning and applications of FCM from the initial conventional FCM to recent extensions and some of the important features of those extensions and learning.
Web-Based Patient Referral System Design from Clinic to Hospital Using Object Oriented Programming System

The clinic in Tangerang Regency, Indonesia is a healthcare institution where people seek treatment and medical advice. However, the patient referral system to the hospital in the polyclinic still uses the old method, which involves a letter of introduction from the clinic, a conventional way of documenting the chronology of the patient's illness and using a certificate or cover letter at the clinic. Patient data with problems going to the hospital is not stored in the database, and as a result, patients may have to postpone going to the hospital. The purpose of this study is to determine the current patient referral system from clinics to hospitals and design a web-based patient referral information system that can integrate patient data into the hospital. This research uses a descriptive qualitative method to analyze and design the system, employing the Object-Oriented System Design method. The output of this research is software, which is a web-based patient referral information system that displays completed patient data and disease chronology accurately and quickly, supporting digital concepts.
Enhancing Privacy of information with Data Embedding in Medical MRI Images Based on Segmentation and HVS Model

The development of communications technology and the arrival of Medicine modern equipment in the health domain has caused the diagnosis and treatment methods to be considered from a distance and medical centers are equipped with telemedicine systems. Forensic medicine organizations with daily clients and outpatient examinations such as accident clients, Conflict returns as well as spousal abuse. Doctors and employees of the organization also serve as one of the powerful arms of the judiciary, following up on important cases in the medical, laboratory, and psychiatric commissions So that they can take steps to realize the rights of the people. Patient data security has become a serious concern for professionals and one of the methods is using data embedding to the protection against these risks. In this method, medical informatics, telemedicine, and forensic medicine organizations has played a pivotal role and any mistake in the reporting can be catastrophic. The main purpose of this research is to present data on EPRs with Enhancing data embedding Based on SSIM and HVS with the help of medical image segmentation and focus on brain MRI images. In this study, innovations include the addition of the HVS block based on the SSIM criterion to meet transparency and robustness conditions. Selection of the embedding coefficient K is considered adaptively depending on the degree of uniformity of the N - ROI region with the image quality factor. The coordinates of ROI areas in one of the DCT and DWT conversion blocks have been demonstrated to have better performance at Concealment EPRs. The choice of coefficients af,k which consists of the optimization frequency sensitivity function and spatial property is comparatively done to match the visual perception of the visual system. The present study aims to improve the effectiveness of the proposed method, improve the security level, and the confidentiality of patient information, and integrate the storage of patient information and image. The simulation results of the proposed method, considering the parameters of embedding and transparency in comparison with other methods, have been done using evaluation criteria including MSE PSNR, NC, SSIM, and BER.
AI BASED CHATBOT TO ANSWER FAQs

People interact with systems more and more through voice assistants and chatbots. The days of solely engaging with a service through a keyboard are over. These new modes of user interaction are aided in part by This research will investigate how advancements in Artificial Intelligence and Machine Learning technology are being used to improve many services. In particular it will look at the development of chatbots as a channel for information distribution. This project aimed to implement a web-based chatbot to assist with online banking, using tools that expose artificial intelligence methods such as natural language understanding. Allowing users to interact with the chatbot using natural language input and to train the chatbot using appropriate methods so it will be able to generate a response. The chatbot will allow users to view all their personal banking information all from within the chatbot. In an industry with low user satisfaction rates and limited technology to increase accessibility. It is clear the chatbot overcomes the challenges that many firms face to increase the use of their services and gain a competitive edge over leading competitors. With many people adopting Smart Assistant Devices such as Google Home or Amazon’s Alexa. The chatbot was tested across a range of devices such as Google Home and Assistant on android devices to outline the key differences between the two modes of interaction, spoken and text dialog. These test were carried out to identify the value in integrating such technology surrounding the recent interest in EEE521 Final Year Project 2017/18 B00659303 2 chatbots and conversational interfaces. Proving chatbots can be applied to a specific domain to enhance accessibility, reaffirming that they are more than just a passing fad and have a viable use. Keywords : Chatbot, AI, NLP, ML, NLTK, Data set.
HANDWRITTEN TELUGU COMPOUND CHARACTER PREDICTION USING CONVOLUTIONAL NEURAL NETWORK

Whether in Telugu or another language, handwriting recognition has been a difficult job for a very long time. Handwritten characters frequently have many curves, particularly in Indian languages. With the development of machine learning, handwritten recognition has long been an open task. However, there are still many difficulties because feature extraction is a challenging job because characters are more prevalent in Indian languages. Pick a handwritten Telugu compound character named Guninthalu (a character made up of Telugu vowels and consonants) to be recognised in this essay. Since each character in Guninthalu is nearly identical to the others, Telugu. It's difficult to classify things. Although there are many machine learning methods, achieving accuracy is the main obstacle. Consequently, applying deep learning. Convolutional neural networks are being used to build a machine-learning model for Telugu Handwriting Gunithalu. Create a dataset, and the IEEE Data port will have it accessible. Keywords: Telugu Character Prediction, Convolutional Neural Network, Deep Learning, Handwritten Letter Prediction.
Traverser: A method and a tool to extract user’s traverses within web systems from ontology

People explore specific services or information to accomplish their goals within a web system. Efficient visitors’ exploration routes must be defined as requirements to guide them through a good experience. In order to define these requirements, we focused on links from one object to another and developed a method named Traverser to guide the visitors from the starting point to the destination. In our study, these links are within the ontology of a service domain. The set of links to guide visitors from a certain object to the destination is referred to as a “traverse.” A traverse is inferred from the ontology by tracing associations and inheritance relationships between domain items, taking the rights of each user group into account. The ontology for extracting traverses is developed as a conceptual model with a class diagram of UML, while the roles of an association are diverted as a user group’s permissions. We also developed a tool to define the conceptual model and extract traverses. The effectiveness of Traverser is evaluated by comparing the requirements derived by Traverser with those of the use case approach.
COIN SORTING AND COUNTING MACHINE

The design was entitled Automatic Coin Sorting Machine for it is to sort the Indian coin denomination. This was done to be able to help different Institutions that deal with large number of coins in their daily operation. Some of these are banks, churches, charitable institutions and the transport sector. The sorting part of the design is basically mechanical as it sorts the coin through their physical size then counts them as they pass through the Arduino. An automatic coin counting and sorting machine prototype is developed by using ARDUINO-UNO as the main controller. This work included design the display panel to show total quantity of each coin and total amount. The coin acceptor has been used as it acts as coin selector to determine the denomination of coins in sorting system is designed by using DC motor to hold and distributes coin to the corresponding slot. Key Words: Denomination, coin slot, rotating plate, microcontroller
DYNAMIC FORM BUILDER

Form builders have become an essential tool for individuals and organizations to collect data, streamline processes, and automate workflows. With the increasing need for digitization and online communication, form builders have become even more crucial in today's society. This research paper explores the various aspects of form builders, including their history, development, features, and benefits. The paper also discusses the different types of form builders available, and their pros and cons. The process of creating a Form manually takes a lot of time, effort, money, etc. That's where the Form Builder comes into play. Manually building a CRUD (Create Read Update Delete) part takes 6-7 hours, whereas building a CRUD with the form builder takes about 5 minutes. Form builders save time, effort, etc. How should a Form Builder be designed? Firstly, the Form Builder must be user-friendly so that anyone with a basic understanding of computers can create forms easily.
Graph neural networks solve Maxwell’s equations numerically

Machine learning tools solve fundamental electromagnetism equations independently of the domain size.
Peningkatan Kompetensi Guru Menerapkan Model Pembelajaran Kooperatif Melalui Workshop di SDN 12/X Pemusiran Pada Semester Genap Tahun Ajaran 2021/2022

This study aims to obtain information and discuss increasing teacher competence in applying the cooperative learning model through workshops at SDN 12/X Pemusiran in the even semester of the 2021/2022 academic year. This type of research is school action research. This research was conducted at SDN 12/X Pemusiran in the even semester of the 2021/2022 academic year. The research subjects were all teachers at SDN 12/X Pemusiran, totaling 5 people. Data was collected through observation sheets, field notes, and documentation. Data analysis techniques use percentages and data reduction. Implementation of workshops that focus on increasing teacher competence in applying cooperative learning models, has increased. Based on the results of the research, it can be concluded that through workshops it can increase teacher competence in implementing cooperative learning at SDN 12/X Pemusiran in the even semester of the 2021/2022 academic year.
Peningkatan Kemampuan Membaca Permulaan melalui Media Flash Card pada Siswa Kelas 1 SD di biMBA-Aiueo Kramat Semanan

This thesis is motivated by the low development of early reading skills in grade 1 elementary school students at biMBA-AIUEO Kramat Semanan. This study also aims to improve early reading skills using flash card media in pre-school age children. The subject of this study consisted of 13 children, 8 boys and 5 girls. This research is a classroom action research that uses the Kemmis and Mc Taggart research model while the data collection techniques used by observation, interviews, documentation and tests. The data analysis technique using quantitative descriptive percentage. The results shows that the early reading ability of children at biMBA-AIUEO Kramat Semanan improved gradually by using flash card media through the steps, namely, recognizing the relationship of letters to sounds, distinguishing letters/recognizing familiar letters, mentioning words with the same initial letters, combining syllables into words, mentioning pictures that have words, and reading words clearly. The improvement can be proven from the result of the first cycle research is in the criteria of developing as expected by 53%, then increase in cycle II to 80%, and increasing to 96.3% in the third cycle. From the description above, it can be concluded that using flash card media can improve early reading skills in pre-school age children at biMBA-AIUEO Kramat Semanan, Kalideres District, West Jakarta.
Attacking the IETF/ISO Standard for Internal Re-keying CTR-ACPKM

Encrypting too much data using the same key is a bad practice from a security perspective. Hence, it is customary to perform re-keying after a given amount of data is transmitted. While in many cases, the re-keying is done using a fresh execution of some key exchange protocol (e.g., in IKE or TLS), there are scenarios where internal re-keying, i.e., without exchange of information, is performed, mostly due to performance reasons.Originally suggested by Abdalla and Bellare, there are several proposals on how to perform this internal re-keying mechanism. For example, Liliya et al. offered the CryptoPro Key Meshing (CPKM) to be used together with GOST 28147-89 (known as the GOST block cipher). Later, ISO and the IETF adopted the Advanced CryptoPro Key Meshing (ACKPM) in ISO 10116 and RFC 8645, respectively.In this paper, we study the security of ACPKM and CPKM. We show that the internal re-keying suffers from an entropy loss in successive repetitions of the rekeying mechanism. We show some attacks based on this issue. The most prominent one has time and data complexities of O(2κ/2) and success rate of O(2−κ/4) for a κ-bit key.Furthermore, we show that a malicious block cipher designer or a faulty implementation can exploit the ACPKM (or the original CPKM) mechanism to significantly hinder the security of a protocol employing ACPKM (or CPKM). Namely, we show that in such cases, the entropy of the re-keyed key can be greatly reduced.
Secure Message Authentication in the Presence of Leakage and Faults

Security against side-channels and faults is a must for the deployment of embedded cryptography. A wide body of research has investigated solutions to secure implementations against these attacks at different abstraction levels. Yet, to a large extent, current solutions focus on one or the other threat. In this paper, we initiate a mode-level study of cryptographic primitives that can ensure security in a (new and practically-motivated) adversarial model combining leakage and faults. Our goal is to identify constructions that do not require a uniform protection of all their operations against both attack vectors. For this purpose, we first introduce a versatile and intuitive model to capture leakage and faults. We then show that a MAC from Asiacrypt 2021 natively enables a leveled implementation for fault resilience where only its underlying tweakable block cipher must be protected, if only the tag verification can be faulted. We finally describe two approaches to amplify security for fault resilience when also the tag generation can be faulted. One is based on iteration and requires the adversary to inject increasingly large faults to succeed. The other is based on randomness and allows provable security against differential faults.
GRAMMATICAL ERRORS ON ARTICLE USAGE IN EFL STUDENTS’ WRITING OF REPORT TEXT

Articles are one of the most difficult parts in writing, especially for EFL students. Different use of articles often leads the students to make errors. This study is intended to know the types of article errors made by students in writing report text and to explain why the errors occur. This study used a descriptive qualitative method. The subjects of this study were the students of class nine at X Junior High School in Jombang. Based on the analysis, it had been found that there were 3 types of article errors: the definite article, the indefinite article, and no article. The results of this study indicated that the students made errors on all types of articles. The errors happened because of both interlingual transfer and intralingual transfer. Finally, it can be concluded that the students had a lack of knowledge in English articles.
INVESTIGATING THE EFFECT OF FREEWRITING TECHNIQUES ON THE WRITING SKILLS OF INDONESIAN JUNIOR HIGH SCHOOL STUDENTS

The aim of this study was to investigate the effects of using the freewriting techniques on the writing ability of Indonesian junior high school students in producing descriptive texts. The study was conducted in a private school located in Banten province and involved 33 students who were selected through a total sampling. A quasi-experimental design was used, with pre- and post-tests serving as the research instruments. The tests were validated for content validity and inter-rater reliability. The results were analyzed using a t-test, which showed that the use of freewriting techniques had a significant positive effect on the writing ability of the students. The t-count (2.533) was higher than the t-table (2.039) at a significance level of 0.05, indicating that the alternative hypothesis (Ha) was accepted and the null hypothesis (H0) was rejected. The average score of the experimental class was higher than that of the control class, further emphasizing the positive impact of the freewriting techniques. These results suggest that the implementation of freewriting as a teaching techniques has the potential to enhance the writing ability of Indonesian junior high school students.
End-to-End Powerline Detection Based on Images from UAVs

Transmission line detection is the basic task of using UAVs for transmission line inspection and other related tasks. However, the detection results based on traditional methods are vulnerable to noise, and the results may not meet the requirements. The deep learning method based on segmentation may cause a lack of vector information and cannot be applied to subsequent high-level tasks, such as distance estimation, location, and so on. In this paper, the characteristics of transmission lines in UAV images are summarized and utilized, and a lightweight powerline detection network is proposed. In addition, due to the reason that powerlines often run through the whole image and are sparse compared to the background, the FPN structure with Hough transform and the neck structure with multi-scale output are introduced. The former can make better use of edge information in a deep neural network as well as reduce the training time. The latter can reduce the error caused by the imbalance between positive and negative samples, make it easier to detect the lines running through the whole image, and finally improve the network performance. This paper also constructs a powerline detection dataset. While the net this paper proposes can achieve real-time detection, the f-score of the detection dataset reaches 85.6%. This method improves the effect of the powerline extraction task and lays the groundwork for subsequent possible high-level tasks.
Extending loophole-free nonlocal correlations to arbitrarily large distances

Abstract One of the most striking features of quantum theory is that it allows spatially separated observers to share correlations that resist local-causal (classical) explanations, a phenomenon referred to as Bell nonlocality[1, 2]. Besides their foundational relevance, the nonlocal correlations enable distant observers to accomplish classically inconceivable information processing and cryptographic feats such as unconditionally secure device-independent key distribution schemes[3-5]. However, the distances over which nonlocal correlations can be realized in state-of-the-art Bell experiments remain severely limited owing to the high threshold efficiencies of the detectors and the fragility of the nonlocal correlations to experimental noise[6, 7]. Instead of looking for quantum strategies with marginally lower threshold requirements, we exploit the properties of loophole-free nonlocal correlations, which are experimentally attainable today, albeit at short distances, to extend them over arbitrarily large distances. Specifically, we consider Bell experiments wherein the spatially separated parties randomly choose the location of their measurement devices in addition to their measurement settings. We demonstrate that when devices close to the entanglement source are perfect and witness extremal loophole-free nonlocal correlations, such correlations can be extended to devices placed arbitrarily far from the source, with almost zero detection efficiency and visibility. To accommodate imperfections close to the source, we demonstrate a specific analytical tradeoff: the higher the loophole-free nonlocality close to the source, the lower the threshold requirements away from the source. We utilize this analytical tradeoff paired with optimal quantum strategies to estimate the critical requirements of a measurement device placed away from the source in the simplest non-trivial scenario and formulate a versatile numerical method applicable to generic network scenarios. Our results demonstrate that the properties of already feasible short-distance loophole-free nonlocal correlations can be exploited to extend them to longer distances, enabling device-independent information processing and cryptography fuelled by long-distance loophole-free nonlocal correlations to become near-term commonplace technologies.