Reviewerly/exHarmony huggingface dataset:
Design of Wechat Applet Shopping System based on Personalized Recommendation

In recent years, more and more people choose to shop on the mobile terminal, which makes the functional requirements and performance complexity of the mobile terminal shopping system increasing. The traditional shopping APP is not enough to deal with the frequent changes of the shopping system. Therefore, there is an urgent need for a flexible, efficient and fast mobile shopping system. Wechat applet was born in this context. Wechat applet has the characteristics of installation free, small memory, low cost and easy extension. With the continuous growth of network information, the problem of data overload promotes the development of personalized recommendation technology. On the basis of literature research and practical research, this paper designs a wechat applet shopping system based on personalized recommendation. The system is divided into seven functional modules: user login, products information display, products classification, shopping cart, order payment, personal information management and personalized recommendation. The system adopts layered architecture design, and the module realizes the corresponding functions by calling API interface. Among them, the personalized recommendation module uses the recommendation algorithm based on collaborative filtering to analyze the user's behavior, and makes recommendations in four different scenarios. The system designed in this paper can meet the shopping needs of users, provide intimate recommendation services, improve shopping efficiency, add shopping fun, and meet the personalized life of users.
Human Activity Recognition using Signal Processing and Classical ML Algorithms

For many of the modern-day applications, human beings are being tracked based on their activity all throughout a given period. The applications may be in surveillance systems, health care, human interaction, marketing, etc. This has become a specific field of study altogether and people are extensively working on this field to find better solutions to the given real-world problems. The data is being collected through devices such as smartwatches (Fitbits, Apple Watch, etc.) or smartphones and is further used to analyze the activity of the human by whom the device is being used. Here we have used the data from UCI Machine Learning Repository which gives us the information acquired from 30 peoples’ smartphones. We have gyroscope and accelerometer data upon which appropriate signal processing techniques have been applied to generate features. Then these features are fed to various classical ML and ensemble models, and we perform a comparative analysis by looking into their specifics and accuracies.
Micro-Seismic Event Detection using statistical feature extraction and machine learning techniques

In this paper, we propose a novel methodology for micro-seismic event detection using multiple statistical feature extraction and machine learning techniques. The method is based on amplitude thresholding and is grounded on the idea, that each relevant seismic event crosses a particular threshold value. We have created a unique dataset of movement, hammering and no-movement using off-the-shelve geophones and 16-bit analog to digital converter. The data is sampled at 10000Hz. Each recorded dataset is of duration 2 min. The data recorded is passed through amplitude thresholding method. Multiple statistical features like root mean square, standard deviation, variance, minimum value and maximum value are extracted from the dataset in both time and frequency domain. k-fold cross validation is done to obtain validation accuracy. We have explored various machine learning algorithms and the results are discussed here. Among all classifiers, Random Forest algorithm achieves the overall highest accuracy of 92.76%.
Advanced Bank Security and Management System

In the prevailing scenario, Net banking plays an essential role in economic development. However, with the increasing significance of digital versions of banking facilities, frauds and scams are exceptionally rising. Moreover, The existing portal of government banks does not provide complete information to their customers regarding various schemes and investment policies. This paper proposes an upgraded design to the current system. It will make online banking reliable and much more efficient. A verification link is sent via Firebase to gain access to the portal. The user can check his eligibility criteria for various active schemes, insurances, and loan capacity. The portal is integrated with Third-Party Apps to track the IP address and region of the device trying to log in, which is sent along with a verification link when a transaction gets initiated. After verification, the video recording gets initiated and saved in the portal's cloud storage simultaneously. The person's face inducting the payment gets matched with all faces added by the primary user, using Microsoft Azure Face API. On successful recognition, a correct OTP input completes the transaction process. The principal objective of this paper is to provide countermeasures against possible security breaches at several stages. This design will eliminate the possibility of scams to an admirable extent and ensure that the account holders are well aware of the schemes they can apply.
Compression of Convolution Neural Network Using Structured Pruning

Deep Neural Network(DNN) is currently solving many real-life problems with excellent accuracy. However, de-signing a compact neural network and training them from scratch face two challenges. First, as in many problems, data-sets are relatively small; the model starts to overfit and has low validation accuracy. Second, training from scratch requires substantial computational resources. So many developers use transfer learning where we start from a standard model such as VGGNet with pre-trained weights. The pre-trained model, trained on a similar problem of high complexity. For example, for the Image Classification Problem, one can use VGG16, ResNet, AlexaNet, and GoogleNet. These pre-trained models are trained on ImageNet Dataset with millions of images of 1000 different classes. Such pre-trained models are enormous, and computation cost is huge during inference, making it unusable for many real-life situations where we need to deploy the model on resource-constrained devices. Thus, much work is going on to compress the standard pre-trained model to achieve the required accuracy with minimum computational cost. There are two types of pruning techniques. (i) Unstructured pruning: parameter-based pruning that prunes individual parameters. (ii) Structured pruning: Here, we prune a set of parameters that perform specific operations such as activation neurons and convolution operations. This paper focuses on structured pruning as it directly results in compression and faster execution. There are two strategies for structured pruning. (i) Saliency-based approach where we compute the impact of parameters on output and remove parameters with minimum value. The second one is similarities based where we find the redundant features and remove one of them such that pruning makes a minimum change in output. In this paper, we combine both the approach where we for the initial iteration we perform pruning based on saliency and later iteration; we perform pruning based on similarity-aware approach. Here we observed that a combined approach leads to better results for pruning.
Gene Determination Algorithm: A Blockchain-based Case Study of Crypto Kitties

Crypto Kitties is one of the most well-known games built on the Ethereum blockchain and has been in operation for a longer time. This paper examines the market’s fairness by focusing on the gene determination algorithm required for breeding two kittens. This method has very little unpredictability, and as a result, players who understand it have a considerable edge over those who do not. Furthermore, this paper demonstrates a methodology that aims to increase randomness in the gene determination algorithm by associating it with a discrete crossover operator. In the end, this paper addresses some of the drawbacks of crypto kitties and countermeasures to overcome them.
Mobile Application for Personalized Food Recommendation

Personalization in business products has always proven to be a powerful marketing approach by which businesses collect data from users, analyze data and use Machine Learning (ML) algorithms to deliver relevant personalized content to the user. ML has shown to be a ground breaking technology that can be used in the everyday decision-making processes of our lives. A personalized food recommender caters to the exact need and taste of the user taking in account factors like the weather, temperature, type of diet and time of the day. This paper illustrates the design of the application that takes inputs from the user, analyzes the data and feeds it to the machine learning algorithm. The algorithm works on the concept of content-based filtering and a collaborative filtering model. An item feature similarity and item-user similarity matrices are created. Based on these values most similar food items are recommended on the application.
Transfer Learning with Deep Neural Networks for Image Classification in the E-commerce Industry

E-commerce has changed the way people shop and unleashed new levels of demand for goods by making it easier to buy and deliver goods to customers. There are now more choices for consumers with shorter production cycles thanks to fashion's digitization. Digital technology in the e-commerce industry is grabbing the attention of customers. The need for a system that facilitates product searching is therefore growing. Using fashion image classification, online retailers can quickly and efficiently manage the volume of their products. Use it to sort products based on their brands, types, or even styles. Fashion ensembles and even product recommendations could be created with it. Product filtering and search can benefit from the results of apparel image classification. For coarse-level image classification, a wide range of models have been successful, but only a few of them could be applied to fashion images because of the nature of the data: such images could belong to a single high-level topic like 'shirt’, ‘saree’, or ‘earrings’, but at the lower level be fine-grained (for example, 'cotton saree,' ‘long earrings’). The fine-grained hierarchy of each clothing store makes it difficult to collect a large dataset. For good predictive quality, research has shown that there is a need for machine learning techniques. Currently, there are new possibilities for vertical integration of research results into industrial or commercial applications due to recent advances in the image retrieval domain. One has to do with the difficulty sellers have when trying to upload images of their products to a marketplace and the ensuing manual tagging that occurs as a result. As a result of the classification errors, it is no longer listed in search results. Using an image-based search algorithm will allow customers to click on a picture of an object and find related products without having to type anything in. As a result, we have created a fashion industry-specific intelligent technique based on deep learning for quick searches and accurate product classifications by applying transfer learning and experiment results on proposed methodology proved that XCEPTION deep neural network model outperforms among all other networks.
FIR Band pass and Band stop Filters Design via HHO Algorithm

In this research work, we proposed a meta-heuristics optimization technique named Harris' hawks optimization (HHO) based method for computing optimal filter coefficients of FIR type BPF and BSF. HHO is a population-based optimization paradigm that uses two phases to find the optimum solution. In the exploration phase, hawks search deeply on any particular area to detect prey and perform different attack strategies in the exploitation phase based on rabbit escape energy and hawks chasing capability. Different performance metrics of the designed filters using the HHO technique are compared with other algorithms like CSO, BFO, HGWOCS and ACSA based FIR filters of order 20.
Breast Cancer Prediction by Leveraging Machine Learning and Deep learning Techniques with Different Imaging Modalities

The high mortality rate due to breast cancer is a major concern that needs to be addressed critically. Early detection is the most necessary factor in cancer studies. Accurate clinical practice is required which can classify normal breast cells from abnormal cells. This classification requires various approaches in which analysis of imaging modalities and their results needs to be verified. Currently, available screening techniques are analyzed and their challenges and issues are presented. This work investigated some recently proposed approaches for breast cancer detection using Machine Learning (ML) and Deep Learning (DL) techniques and summarizes the contributions to this area. Convolutional Neural Network (CNN) has become a very popular medical images analysis technique due to the accuracy they have achieved in recent studies. This study has attempted to collect and compare the various classifiers used to detect breast cancer. The promising increase in the performance of advanced DL techniques are explored and the challenges and the approaches are discussed. This can result in tremendous improvements in performance and accuracy in the future.
A Practical Animal Detection and Collision Avoidance System Using Deep Learning Model

Nowadays some countries are using advanced traffic light control systems to control the traffic signals in order to ensure smooth travel for road users. Most of the researchers use various methods to identify the density of vehicles using cameras or sensors. Sometimes these roads are occupied by animals to sleep or lie down which disturbs the smooth movement of the vehicles. Due to the occupancy of the animals on the road, traffic also increases, the density of the vehicles may increase and hence there are lots of chances that animals collide with vehicles. Most of the cities are using automatic traffic light control systems. There are no traffic police available to get rid of the animals. Especially in Ethiopia, besides pedestrians, a major number of animals are horses lying down on traffic roads. In this paper, an efficient artificial intelligence NASNetMobile model is proposed to identify the animals on traffic roads and pedestrians using an embedded machine learning system. Further, the system will provide information to the traffic police immediately using short messaging service. Standard Model recognition algorithm based on Convolution Neural Network (CNN) consumes extra computational time in comparison to the training and testing of the model. Transfer Learning is used to specifically adjust the prior models and transfer a standard deep learning model to a specific one with different loads and results. Also, the CNN structure is modified to increase overall functioning, and the traffic environs are trained to the specific scenario. The proposed system has generated test images more than 121,000 images on NASNetMobile architecture consisting even lower number of both true and false pictures and tried out on various images of creatures at junctions. A significant precise value of nearly 86.5% is obtained regarding recognition by utilizing the suggested model.
GALEIR: Global Atmospheric Light Estimation based Underwater Image Restoration

This paper presents a modified version of the underwater dark channel prior algorithm for subaquatic image restoration based on Global Atmospheric Light Estimation for Underwater Image Restoration called as GALEIR. The proposed method uses the concept of underwater dark channel prior and attempts to increase the accuracy in atmospheric light estimation. Our methodology reduces the chances of inaccurate estimation of global atmospheric light by applying statistical mode operation on 1% of the brightest pixels chosen. Besides, a fast guided filter has been employed instead of complex Soft Matting for the transmission map refinement. The above-mentioned changes incorporated in the underwater dark channel prior algorithm increases the visual quality of the underwater image and consumes less computational time compared to iterative Soft Matting based restoration algorithms. We proffer the analysis of GALEIR by which its limitations and applicability are revealed. GALEIR has been evaluated on Underwater Colour Image Quality Evaluator (UCIQE), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Underwater Image Quality Metrics (UIQM), and time complexity. The proposed algorithm yields higher UCIQE and lower BRISQUE scores of about 1.231 and 23.86, respectively, performing better than the state-of-the-art techniques.
Security and Privacy in IoT using Blockchain and Lightweight Cryptographic Protocol

The Internet of Things (IoT) has become an emerging topic in the field of digital communication to evolve significantly the modern technologies. To ensure secrecy and privacy of data in IoT applications is the key challenge of this area. To facilitate this enormous challenge, we in detail focus on the security and privacy domain of IoT. By deeply analyzing the challenges in respect of fulfilling the security requirements and protecting the confidential private data, we propose a possible solution to each logical layer of IoT architecture. In addition, we designed the encryption mechanism, decentralizing privacy, secure communication, lightweight cryptographic protocol and demonstration of solutions to all security threats.
Image Classification using Deep Learning: An Experimental Study on Handwritten Digit Recognition

This paper presents an experimental study of the use of Deep Learning using Convolution Neural Networks (CNNs) for Image Classification. Specially, the problem being addressed here is of recognition of handwritten digits. The objective is to report variations in testing errors and accuracies with varying kernel size and varying number of feature maps. We performed handwritten digit classification using neural network and deep learning for a subset from the MNIST dataset, which contains 60,000 training images and 10,000 test images in all. It is observed that the accuracy and loss are stabilizing with minor change in the kernel size and the number of feature maps.
Smart Contract - Security Assessment Integrated Framework (SC-SIF) for Hyperledger Fabric

In the era of ever-changing technology, we people are adopting new technologies/domains to make a better world. In recent times, Blockchain technology is known for its distributed, permissioned/permission-less and immutability nature. Likewise, the Hyperledger fabric is a permission-based framework that can be used in cross-industry applications for Blockchain technology. The logic behind the cross-industry applications is written in a specific language called a smart contract. Though Hyper ledger fabric is permissioned and is considered immutable, detecting architectural threats on the security and privacy mechanisms of Hyperledger Fabric is a challenge. In this paper, we are proposing the Security Assessment Integrated (SC-SIF) Framework for smart contract assessment in the Hyperledger fabric. This can be used to analyze, scan, and evaluate the smart contracts security vulnerability written in NodeJS/Go/script language. Furthermore, framework provide an integration platform to define and configure multiple security tools including communication protocol, exchange policy and security rules to obtain the security vulnerability on provided smart contracts. It also provides an option to evaluate the tools security assessment report and generate smart contract assessment report in human readable form (JSON/XML) that can help research community, developer, and analyst to improve their smart contract. This paper also briefs literature survey of existing security assessment smart contract and incorporates the working of the ledger, channels, endorsement policy roles, and the transaction flow.
Yuwathi: Early Detection of Breast Cancer and Classification of Mammography Images Using Machine Learning

According to the World Health Organization's (WHO) data and records, breast cancer is one of the most common diseases among women. As a result of the mutations of the genes within a cell, the cell starts growing uncontrollably and rapidly. Such a condition is known as cancer. Cancer tumors can be categorized into two major categories, benign and malignant. However, there is no existing solution in practice to automate early breast cancer identification and risk prediction using medical images (Mammograms). This paper discusses automating breast cancer detection, breast density identification, risk prediction, and solution suggestion using machine learning, image processing, and computer vision techniques. All the mentioned features can be accessed using the application "YUWATHI", and a user can take advantage of this application by using a smartphone also a web application. The objectives of the present study are mammographic mass detection without user intervention, identifying pectoral muscles and removing them, training a machine learning model to identify the future risk of breast cancers by obtaining clinical reports from the OCR application and suggesting solutions for the above problems using a computer-aided diagnosis (CADx) system that helps doctors to make decisions swiftly. The algorithms used for breast cancer detection, breast density classification, and future breast cancer risk prediction are Convolutional Neural Network (CNN), CNN and Logistic Regression with the accuracies 97.32%, 71.97% 74.76%, respectively.
Handwriting Quality Assessment using Structural Features and Support Vector Machines

Handwriting plays a major role in written communication. Based on appealing handwriting students gather better scores and professionals become more successful. Timely feedback on the quality of handwriting helps to make course corrections. In this work, we report a system that takes in a scanned image of document to analyse & provide a quality indicator for the handwriting. The study performs a comparative analysis of various well-known classifiers (such as SVM, k-NN) using structural features (such as slant, inter-character spacing, character size). The features are obtained from binarized images as well as skeletonized images. It is observed that SVM classifier generates the maximum accuracy with the feature-sets.
MPAA Rating Prediction Using Script Analysis for Movies

A movie is a visual work of art that uses film to convey ideas, tales, perceptions, emotions, beauty, and atmosphere in order to recreate an experience. Movies help and inspire social changes, attitudes and other emotions. Exposure to unwanted or unjustified scenes and dialogues can adversely affect the growth of vulnerable parts of society, such as children. The expected outcome of this research paper is to predict the Movie picture association rating based on the script. We extract the relevant movie ratings and propose bidirectional LSTM based models with attention to predict movie rating. We achieve an accuracy of 56% for classification for movies. The model classifies into 5 classes - (R,G, PG,PG-13, NC-17).
Evaluation of Cryptographically Secure Pseudo Random Number Generators for Post Quantum Era

The construction of a Pseudo-Random Number Generator (PRNG) is an extremely decisive task in the field of Cryptography, Gaming, Computational statistics, Simulation processes, Gambling, and other fields. But many of the PRNGs are not cryptographically secure so various kinds of models have been presented to generate Pseudo-Random numbers. As generators are used in the field of cryptography too therefore it is inescapable to assess it among scientists and researchers. In this study, we would evaluate different kinds of RNGs against quantum attacks including the one which is based on lattice-based cryptography. In the end, we would contrive to wind up how the lattice-based PRNGS are pragmatic for the Post Quantum Cryptography.