Reviewerly/exHarmony huggingface dataset:
Efficient Transmission of Secure Images with OFDM using Chaotic Encryption

Information security considered as one of the main goals when it is transmitted through a wireless communication or even it is required to be saved in a PC. Incorporation of a secure and highly reliable of digital data such as (video, audio, images or text) that be required to transmit from source to destination through a communication channel, is turning into a top of the requirements in present-day wi-fi communications. There are two phases to achieve confidentiality, the data level and the network level. Cryptographic techniques are using in data level security. This study focuses on data-level security phase to establish a secure image transmission through an AWGN channel, where (OFDM) the Orthogonal Frequency Division Multiplexing system is used with different encryption techniques, using cipher, permutation, and scrambling algorithms to ensure secure image transmission using MATLAB program. Several statistical tests were used to check the encryption quality, which are entropy, correlation coefficient, NPCR, UACI, histogram. On the other hand, the image transmission quality is evaluated by Bit-Error-Rate at different SNR conditions, BER, and Peak-Signal-to-Noise Ratio, PSNR. The numerical analysis shows that the tow stage-OFDM system proposed present an overall performance improvement among the earlier cryptosystems. The statistical analysis tests show that this system is considered as simple and robust algorithm from security point of view.
A Hybrid Qualitative and Quantitative Approach for Automatic Short Answer Grading Using Classification Algorithms

Assessment plays a very important role in the teaching-learning process. Recently proposed techniques for short answer grading are either Qualitative or Quantitative. Qualitative methods use classification and give scores in an incomprehensible way like correct, incorrect, partially correct, etc., and Quantitative methods use Regression and MSE(Mean Square Error) which assigns scores like 2.3,4.2, etc. requires a learning method to convert those values to integers. In this paper, a hybrid Qualitative Quantitative approach is proposed which is based on machine learning that relies on Multi-class classification where each category of marks is considered as a class. This can be easily converted to qualitative or quantitative systems without additional learning methods. This system follows a model answer-based method where various similarities are extracted from both model and student answers including statistical, Bag of Words, TFIDF, Latent Semantic Analysis, and Neural sentence embedding based Infersent. Using these features various classification models are designed including K Nearest Neighbor, Naïve Bayes, Decision tree, SVM, Random Forest, and XGBoost for various test sizes of the data and cross-validations. The models are tested on a dataset that consists of one question, 80 answers, and a model answer. In total, 72 experiments are conducted, demonstrating that the proposed hybrid approach can be effectively applied to the Automatic Short Answer Grading (ASAG) task.
Multi-Class Kannada Character Recognition Using Machine Learning Methods

Language plays a significant role in the identity of a person, it expresses history and culture. But with increasing popularity of cosmopolitan culture, the new generation is moving away from their origin. Karnataka is one of the most popular states in India which welcomes people from different geographical locations, because of its hospitality, weather, technological forefront etc. On the other hand, it impacts the usage of Kannada language and it challenges the demographic identity of the place. This paper aims to enhance the usage of Kannada language by automatic handwritten Kannada character recognition. In this work MSRIT Kannada handwritten dataset is used to classify 603 characters, which includes consonants, vowels, numbers, ottaksharas and consonants with vowels, using Decision Tree (DT), Convolutional Neural networks (CNN), Support Vector Classifier (SVC). The machine learning algorithms are modeled in python and achieved test accuracy of 97.42% using the decision tree method.
Abnormal Human Behavior Detection in Crowded Scenes Based on Hybrid Neural Networks

Any event in a crowded scene can be classified to contain normal or abnormal behavior displayed by an individual or groups of individuals present in the scene. Smart camera-based surveillance systems are much more capable than manual monitoring of CCTV as there is a chance that the staff-in-charge may miss the occurrence of an abnormal activity. A Neural Network based anomaly detection system identifies such events, so that normal and abnormal behavior can be categorized and detected automatically. This paper proposes to employ the use of Hybrid Neural Networks (based on CNN) to detect different types of anomalous behavior in stationary-camera video footage. The proposed solution to detect anomalous behaviors like punches, kicks, chases, sudden crowd gathering/dispersal etc. is implemented by calculating the Dense Optical Flow and using the same to classify the scene as containing anomalous behavior or not. Classification results of the three methods are compared with respect to accuracy. Hybrid model-1 classification results in the best accuracy of 97.19%.
Social Distance Detector Using Computer Vision and Deep Learning to Combat COVID-19

The study's goal is to create a detector that detects and analyses whether pedestrians or individuals in public gatherings are maintaining social distancing. Drone-shot videos, live webcam feeds, and photographs are all kinds of input for the detector. With no human intervention, Dynamic Detection through live stream provides safety and simplifies monitoring of social distance. The webcam input can be integrated with an external webcam or a drone's camera. Furthermore, the YOLOv4 algorithm is used for the data set for the initial phase ofobject detection, identifying various items in each frame. The recognized objects are narrowed down to humans, and the Euclidian distance between one data point and every other data point is determined The Euclidian distance determines if they are maintaining the minimal distance between them or not by depicting them with a colored border box. Euclidian distance assists in detecting if they are keeping the minimal distance between them or not, as shown by a coloredboundary box, red for unsafe and green for safe, with an indication reflecting the number of people in danger.
YOLOv7 based face extraction and novel Eye feature extraction with EYENET

Most of the modern Face feature techniques include face detection and extraction. The novel techniques include feature extraction from the face which are mapped with the names of person used for detecting the person on a live basis. The project starts with a YOLO (You look only once) v7 model which was the high accuracy and prediction model created until date. This model is basically used for object detection, has also been used for face detection purposes. The model is further combined with computer vision color mapping methods to extract the face out. Then a feature-based model is built to extract 12 feature points in eyes from the face. Every person is identified according to the values like length and area from the feature points of the eyes. Comparison between the YOLO v7 and other face detection and feature extraction methods have also been specified. The main aim of the paper is for extraction of certain features and few novel feature techniques that can be used in person re-identification.
Visual Cryptographic Permuted Sampling based Binary Image Watermarking

This paper proposes a visual cryptography per-muted scrambling (VCPS) based watermarking method for binary images. Binary image (BI), which has only black and white pixels, has a nature of compact storage. Due to the rapid growth of digital technology, a lot of personal information such as health records, hand-written signatures and certificates; book collections and design drawings; cartoon art sketches are stored as digital documents as BIs (white and black pixels) by scanning them. These BIs are communicated through the Internet either for the personal or professional case. Here lies a threat of unauthorized usage of these documents and it is essential to protect them by embedding the watermark (owner information of the binary image). The particularity of the BI makes it more challenging for data hiding because the hiding process is different and only few works are available in this domain. In this paper, the data hiding process is carried out using the coding tables and the encrypted binary watermark shares which is flipped and shuffled using visual cryptography. The proposed technique achieves higher visual quality and less computational cost than the state of the art related techniques.
Smart Gloves for Hand Gesture Recognition in Kannada

Communication is one of the fundamental requirements of survival in a society. All human relationships, whether they be personal or work-related, are based on communication. Individuals with speech disabilities have limitations in their communication options. Efficient communication is practically impossible without a clearly defined language that is shared by all parties. As many are unaware about the sign language, there is communication gap between speech impaired people and normal people. Many devices have been developed to bridge this gap. However, the output langvage is usually English. India being a country of diverse languages, there is a pressing need to develop a device that converts gestures to the local language of conversation so that the device can be used by all. The developed system is a glove that translates sign language into text and speech in Kannada. The glove consists of flex sensors and an IMU unit which generates values based on the bend of the fingers and orientation of the hand. Data from the sensors are sent to the control unit, that is Arduino N ano the signals are compared with the pre-determined values for the recognition of sign and is then converted to text. Further the text output is wirelessly transmitted to a cellular phone via Bluetooth module. An app for an android phone was developed that can converts text to speech in Kannada to get the audible speech output.
Fake Product Identification System Using Blockchain

The immutable decentralised and fast nature of blockchain has given rise to numerous applications based on this technology. The integrity of its data is one of the primary features of blockchain technology. By utilising blockchain technology, the system becomes decentralised, enabling the consumer to independently check the accuracy of the data and the product. We suggest a decentralised Blockchain system to stop the fabrication of fake products so that both the supplier and the consumer may use it to trade real goods without having to supervise directly owned stores, which can significantly reduce the cost of product quality assurance. In this project, Quick Response (QR) codes offer a powerful method to tackle the practice of product counterfeiting thanks to new developments in wireless and mobile technologies. Counterfeit goods may be found using a QR code scanner, which connects the product's QR code to a Blockchain. Therefore, this technique might be used to construct blocks in the database using the product's unique codes and record the product's details. It gathers the user's individual code and runs it against records in the Blockchain database. The consumer will be informed if the code matches, or if the product is fake, in which case they will receive the message.
Real Time Face classification and Shape based feature extraction with Deep Neural networks

Feature fusion and pose detection with real time face images is based on synergy techniques used in HyperFace and occlusion-based techniques than employ L-NMS or IRP. We implement real time categorizing of the live images by Head pose estimation into straight and side face, these images are separately trained over two Neural network models executing in parallel and then extracting certain set of features from the face with shape-based feature fusion techniques using CNN to identify people in a certain occlusion-based environment. Our work enhances the accuracy and speed of detection of faces in the real time scenarios by considering the modified form of HyperFace architecture. Feature based techniques have shown more accuracy in detection of images compared to other face detection methods. Data augmentation is performed over face images to store face features accurately with face images in the database. Face features are predicted based on visibility mentioned during the training of modified Hyperface architecture. The features are thus used to create shapes and find the required angles using law of cosines. Images of people and angles are stored in the database for person identification.
Phishing Attack Mitigation Using Convolutional Neural Networks

Phishing has acquired immense consideration from both scholarly world and industry. The variety of assault examples and mediums related with phishing made the advancement of an ideal arrangement testing. Likewise, aggressors generally make genuine looking substance utilizing authentic phrasing or real looking URLs and sites. A large number of current phishing arrangements requires manual feature extraction that needs master area information and smart determination of important elements to be proficient. Moreover, best phishing arrangements experienced enormous computational expenses. CNN marks approaching messages into phishing or legit. Finding out the system on email dataset shows good execution as far as exactness, accuracy, and review when contrasted with comparative methodologies. The objective is to detect and mitigate phishing attack using convolutional neural network. At first machine learning approach is shown in the paper. The algorithm aims at detecting phishing attack based on extracted features which provides good accuracy. To provide more accuracy and to use more computational power convolutional neural network
Kidney Abnormalities Detection and Classification Using CNN-based Feature Extraction

The presents of noises degrade the quality of ultrasound images and diminishes the disease diagnosis accuracy. Thus, an effective automatic stone and cyst detection system is beneficial to both the medical practitioners and patients. In this paper, an automatic detection and classification system for kidney stone and cyst image is proposed. The Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLHE) techniques are applied to improve the quality of the images. In the next step, segmentation has been done based on the entropy of the image. The gamma correction technique has been applied to improve the overall brightness and an optimal global threshold value is selected to extract the region. The CNN model has attained much attention in medical image recognition and classification. In this paper, the pre-trained model ResNet-50 is utilized as a feature-extractor and Support Vector Machine as classifier to categorize the normal, cyst and stone images. The CNN model is analyzed with various other classification models such as k-nearest neighbor, decision tree and Naïve Bayes. The results demonstrate that the ResNet-50 with supervised classification algorithm SVM is an optimal solution for analyzing kidney diseases.
Using Neural Networks To Map Context Features To Probability Distributions

Mixture Density Networks (MDN) is a type of neural network model which is used for predicting the target in high dimensional space. In MDN, there are adaptive mixture weights that come from the target distribution. Mixture Density Networks are comparatively more accurate than “traditional clustering” algorithms, as they use a better estimator - probability. Mixture density networks can be represented with the X vector of probabilities all pointing to a different certain class, where each dimension of the vector is an outcome of an event/input in order to classify something that may occur or is also called a multinomial classification. In this paper, mixture density networks are used for contextual pitch prediction. The authors focus on developing models that aim to model the distribution of pitch types and their contexts to help model at bats and predict the outcomes. It was observed that MDNs are well suited for modelling this distribution.
Blockchain based Digital Record Storage and Security for Education using Polygon and IPFS

Document verification is a tedious and prolonged process. It uses many factors to authenticate a document, like its originality, expiration date, and legitimacy. Every document can have vastly diverse content and each of them has to be evaluated distinctly. Also, documents are prone to skillful duplicacy - which can be very hard to detect. This paper proposes a model which introduces the application of blockchain to verify documents, with special reference to educational records, which are maximally forged in recent generations. Recent advancements in Blockchain have broadened its use cases. One such use case, which is the analysis of blockchain and its role in the field of educational records maintenance, is the main focus of this paper. This paper also discusses some benefits and risks associated with the application of blockchain in the educational field. The proposed model helps in verifying an educational record and storing it on the blockchain. Verified documents are those which are acceptable among all the reputed institutions, and have a verification tag attached to them. This makes the document authentic.
Scaling Up a Cloud Microservices Simulator

Cloud services have recently shifted from single-tiered software applications to a complex collection of microservices. Microservices architectures allow applications to be scaled swiftly and easily, thus shortening the time it takes to market new features. However, there are some problems that need to be solved before this utility computing vision can be fully realised. The microservices architecture has several benefits, but as we scale up their often behaviour becomes quite unpredictable. To analyse and improve upon this problem, we aim to scale up MuSim, an open-source microservice simulator, drawing inspiration from DeathStarBench.
Training Deep Energy-Based Models Through Cyclical Stochastic Gradient Langevin Dynamics

Non-normalized log probability is assigned to data samples by energy-based models (EBMs). Applications for this functionality include image synthesis, data denoising, sample restoration, outlier identification, and Bayesian inference. Long mixing times for high-dimensional datasets are a common problem for MCMC-based sampling techniques. Some methods for scaling MCMC-based EBM training with continuous neural networks have been proposed to obtain better sampling than previous likelihood models and approaches similar to modern GAN approaches without experiencing mode collapse. This study suggests a novel approach called Cyclical Stochastic Gradient Langevin Dynamics for enhancing MCMC sampling of EBMs due to the cost of mixing times. On the MNIST dataset, we discovered that Cyclical SGLD is faster than Stochastic Gradient Langevin Dynamics (SGLD) and even more effective and reliable at producing realistic images.
A Comparison of Distributed, PAM, and Trie Data Structure Dictionaries in Automatic Spelling Correction for Indonesian Formal Text

Spelling errors can be divided into two groups, non-word errors and word errors. A non-word errors produce words that do not exist in dictionary, while word errors is a real word but not the right word. In this work, we address the non-word errors spelling correction for Indonesian formal text. The objective of our work is to compare the effectiveness of three kinds of dictionary structure for spelling correction, distributed dictionary, PAM (partition around medoids) dictionary, and dictionary using trie data structure, with the baseline of a simple flat dictionary. We conduct experiments with two variations of edit distances, i.e. Levenshtein and Damerau-Levenshtein, and utilized n-grams for ranking suggestion. We also build a gold standard of 200 sentences that consists of 4,323 tokens with 288 of them are non-word errors. Among the various combinations of dictionary type and edit distance, the trie data structure with Damerau-Levenshtein distance gets the best accuracy to produce candidate correction, i.e. 95.89% in 45.31 seconds. Furthermore, the combination of trie data structure with Damerau-Levenshtein distance also gets the best accuracy in choosing the best candidate, i.e. 73.15%.
Design of Wearable Device for Monitoring the Position of A Person with Dementia

The incidence of missing persons with dementia is defined as wandering, a condition in which the person with dementia is lost track of their caregivers. We propose to develop a wearable system that can be used more effectively by people with dementia. The wearable device design aims to reduce the incidence of wandering in the dementia patient. The experimental results show that the system can determine the patient's position and the safe zone within a distance of 15 meters on the mobile application. The Euclidean formula for calculating the distance between a patient and a user as a caregiver or family gives relatively accurate results, with an average distance difference of 0.38 meters. The notification system in the form of vibration, sound, and background color on the application system can notify caregivers or families when the patient is outside a safe area with the coordinates of the patient and user with a 100% success rate. As a backup, SMS messages are sent by the system when the internet signal on the device is interrupted. Thus, the system can still send the location of the patient's google maps coordinates. It can be concluded that the system runs well when the device is in the zone and outside the safe zone.
Implementation Brute Force-KNN Method for Scholarship Program Selection

The selection process for the Kartu Indonesia Pintar (KIP) scholarship selection is done manually. The selection process that was carried out manually in processing the selection of the KIP scholarship recipients, resulted in a long time and inaccurate selection results. Information related to this process was obtained from interviews with the student body in charge of selecting the KIP scholarship. The number of applicants for the KIP scholarship has gone up every year, and the allocation of graduating students given by the ministry of religion as the scholarship provider is limited, so a truly accountable selection process is needed. Based on these problems, it is proposed to process the KIP scholarship selection using the KNN and Brute force methods with the research title Implementation of the Brute Force-KNN for Selecting of KIP Scholarship Program Recipients. The method used for this research is KNN and Brute force. The Brute force method is used to maximize the selection of the proper variable values, so that it can get a higher accuracy value. The analytical technique compares other methods, namely Naïve Bayes, KNN, and Naïve Bayes with Brute Force. This study uses a KIP registration dataset of 18 attributes and 2302 rows. The results of research using KNN obtained an accuracy of 68%, Naïve Bayes 65%, Naïve Bayes with Brute Force 74.13%, and KNN with Brute Force 75.65%, therefore the Brute Force-KNN approach can increase the level of accuracy.