Reviewerly/exHarmony huggingface dataset:
Enhancing Protection Against Scalper Bots with ML

In this modern fast-moving digital world, Online Shopping has become an important part. When these online shopping sites announce an offer for a limited amount of time to a product that has high demand, customers will rush to buy the products for diluted prices. But unfortunately, there are some other people called scalpers, who will buy those products in large quantities and sell them in the market for higher prices to make a profit. In order to buy those products faster, they are using some bots called Scalper bots. Using this, they can buy a large number of products in a short interval of time which is much faster than normal humans. Thereby making legitimate customers lose their opportunity to buy the product. The main problem is that the scalping bots are very fast and intelligent enough to buy many products in a short amount of time and can even bypass some varieties of CAPTCHAs. The efficiency of the bot lies in its speed. If we are able to find whether the user is a human or a bot, we can extend the time required to perform the checkout/buying process for the bot. By doing so, we can reduce its efficiency tremendously. Our solution involves a Machine Learning Algorithm to find out the average minimum amount of time taken by a human to complete the checkout process and use that as a standard to determine whether a user is a bot or a human. The proposed system improves the accuracy by predicting bots with the help of advanced machine learning algorithms.
Lossless Image Compression Using Machine Learning

As communication networks evolve, so does the technology for exchanging and transmitting information. With millions of photos uploaded on social media every day, wireless sensor networks are rapidly being utilized to take images in a variety of applications such as traffic lights, roadways, social media, assembly, websites, and malls. As a result, it is required to reduce the size of these photos while retaining a suitable degree of quality. To compress RGB photos in this study, we used the K-mean clustering algorithm. Image quality is evaluated using after image compression, PSNR, MSE, and SSIM. The higher the K in the SSIM index, the more comparable the two photos are. This is a good sign that the image size has been greatly reduced. The proposed compression approach, which is based on the KMean clustering algorithm, is used to compress photos to minimize their size, server burden, and data transmission speed.
Research on the Application of Deep Learning Algorithm in Big Data Image Classification

Deep learning is the key and difficult point in the current application research of big data image classification. Compared with the traditional shallow network model, the multi-layer network structure with deep learning algorithm as the core can correctly express complex functions, thus presenting stronger characteristics of learning and representation ability and improving the accuracy of image classification. However, based on the current state of big data image classification, Restricted Boltzmann Machine (RBM) was found. As the basic unit of deep learning algorithm, has problems such as high complexity and low likelihood of training data during training, which directly increases the training time of deep learning model. Therefore, on the basis of understanding the deep learning algorithm, this paper studies and analyzes the image classification method and proposes the image classification method of multi-layer RBM network. The final empirical results show that, compared with other image classification methods, the image classification technique based on deep learning algorithm improves the accuracy of practical operation, and shows strong robustness and generalization.
Efficient Designing of Mockups Using Convolutional Neural Network

Software development methodologies have seen major changes in terms of functional programming and open-source libraries. User Interface design takes a major proportion of development effort, and the creation of mockups is still a manual process. It not only involves time but also requires continuous feedback from the end user, making human behavior a major component for prototype designing. The emergence of an artificial neural network accounts for simplifying human behavior and replicates the actions systematically taken by a human. This paper discusses the usage of the convolutional neural network model of artificial neural networks to generate mockups as per user responses and suggesting User Interface mockups that best suits a business scenario. The auto-generation of mockups leads to a significant reduction of time and cost to the company for developing the initial mockups of a system. The reduction in time can be counted in not Person Days (PDs) but in months because it reduces the time taken in the initial research and leads to direct validation from the customer. The paper proposes the possibility of using end user data and predicting a relevant mockup by eliminating the initial human error regarding the templates that are stored in the system.
K-Means Clustering Decision Approach in Data Hiding Sample Selection

Nowadays, data science plays an important role in decision-making process. Data science application varies from organization to organization, where it plays a very important role in decision-making process in multidimensional data. Author suggests an application of k-means clustering data science algorithm in the sample selection process of carrier during data hiding behind carrier. Suggested approach helps to minimize the time complexity for sample selection and also cluster samples more accurately which helps to focus on specific cluster rather than entire samples in carrier. The experimental results are calculated far better than any other clustering technique discussed in the result section of this paper. The primary objective of author is to make data hiding process so simple and accurate to minimize quantization error. K-means clustering is an effective technique to group samples into different classes called as cluster based on Euclidean distance and MOI. An accuracy of cluster depends on how tightly items in clusters are bounded. Results are compared with existing approach which are better and hence proved that k-means clustering data science approach is the best match for sample selection process during data hiding.
An Improved Image Denoising Method to Suppress Staircase Effect

The conflicts between edge regions and smooth regions of total variation (TV) model used in image denoising may produce staircase effect. An improved total variation model by using local gradient threshold is proposed. Based on the analysis of the existing wavelet transform for noise prediction, a more accurate improved prediction algorithm is proposed. Furthermore, the relation equation between local gradient threshold and noise variance through experiments. The estimation method of optimal gradient threshold is proposed also. Finally, the paper gives steps and iterative methods of the improved algorithm. Experiments result show that the proposed algorithm can effectively denoising the image, obtain better edge protection effect, and can suppress the staircase effect in the restored image.
Some Observations on Huffman Trees

Information theory and coding theory are very important in communication engineering. Data is transferred across the network from source to destination via some communication channel. Data streams can be very lengthy. Moreover, if we consider all characters from ‘A’ to ‘Z’ in English, the data stream may or may not contain all possible characters in this character set. Hence, we need not use all the bits used to represent the characters from ‘A’ to ‘Z’. Here, data compression comes into play. Coding theory comes up with various techniques required to create codes for each of the characters in the data stream to represent them with smaller number of bits. Huffman coding is one such important algorithmic technique used to create codes for each of the characters. Apart from compression, they are used in other tasks like cryptography, error detection, and correction. In this paper, our concern is data compression mainly. We recommend to club different techniques like use of special characters, contexts in character stream together with Huffman coding to perform effective compression.
Real-Time Detection of Vehicles on South Asian Roads

Great advances have been made in the field of computer vision especially for object detection in the last two decades. Vehicle detection is fundamentally a task of object detection which assists in traffic control and management via traffic flow analysis. Models trained for vehicle detection on popular datasets containing vehicles as classes perform poorly while detecting vehicles in South Asian countries since the make and model of vehicles in the geographical region is different from those present in most popular datasets. Furthermore, some kinds of vehicles are found only in South Asian countries. In this paper, we propose a method that uses two YOLO models for the detection of various kinds of vehicles found on roads in South Asian countries. The first YOLO model detects vehicles while the second YOLO model detects wheels in case a truck is detected to further classify a truck based on the number of axles. A dataset consisting of images of bicycles, motorcycles, cars, buses, trucks, tempos and rickshaws was collected from surveillance videos, web scraping and subsetting the Indian Driving Dataset (IDD) as well as the Common Objects in Context (COCO) dataset. Another dataset was made which consists of images of trucks with their wheels annotated. YOLOv4 models trained on the latter mentioned datasets performed better than YOLOv3 and achieved a mAP of 71.08 per cent and 94.33 per cent for detection of vehicles and wheels, respectively. The proposed method to determine the number of axles of a truck gave an overall accuracy of 95 per cent.
Static Hand Gesture Prediction Using Inception V3

Sign language is a language for communication that uses the structure of hand, facial expressions, and body language to communicate, and this is a means of communication for deaf and dumb through the use of the hands and other parts of the body. American Sign Language (ASL), which consists of static hand gestures, is one of the widely used sign languages around the world. There arises a communication barrier, when hearing people who do not understand the sign language wants to communicate with deaf and dumb people, which is a major problem in the present society. Hence, this work is focused on developing the static hand gesture recognition system, which acts as a bridge of communication between hearing people and deaf and dumb people. In the proposed work, deep learning and artificial neural network concept are used, since it provides a wide range of algorithms that can recognize objects by learning the features of the object. The proposed work is to predict the static hand gestures of ASL alphabets using Inception v3 model, which uses transfer learning to train the model.
Breast Cancer Prediction Using Different Machine Learning Algorithm

In the Current era, due to explosion of population, early disease diagnosis has become a major concern medical research field. Due to breast cancer possibility of death is growing increasingly now days. From the previous analysis it has been disclosed that breast cancer has become second-most-severe disease as compare to other diseases. In early diagnosis and discovery of several diseases machine learning algorithm plays important role so that chance of patient’s survival will get increased. A reliable, effective, and quick reaction while also lowering the danger of death can be achieved using automated detection system which can be implemented using artificial intelligence or machine learning technique. In this research, we compare logistic regression, decision tree, random forest and K-nearest neighbor with the Neighbor Component Analysis (NCA) approach, which are all supervised machine learning techniques. In comparison with other algorithms, the automated system using KNN with NCA approach obtains high accuracy which is approximately 98.5%.
negSPUC: Trees-Based Single-Phase High-Utility Itemset Mining Algorithm with Negative Profit Values

High-utility itemset mining (HUIM) extracts novel, non-trivial itemsets by incorporating the revenue generated by the purchased items from voluminous customer transaction databases. Although, most of the tree-based algorithms in the literature are two-phased, recently a single-phase algorithm called single-phase utility computation (SPUC) has been proposed. However, such conventional algorithms mine only a subset of HUIs when certain items bear negative profit values in the database. In this study, negSPUC algorithm has been proposed where the two compact tree structures—Utility Count Tree and String Utility Tree—are restructured to handle items bearing negative profits. negSPUC with path-based and overestimated utility pruning strategies can mine a complete set of HUIs when transaction database contains both positive and/or negative profit values. Further, experimental evaluation on synthetic and real data sets demonstrates the efficiency of negSPUC over HUINIV-Mine algorithm.
Feature Selection-Based Spam Detection System in SMS and Email Domain

Spam exists in several domains including SMS and Emails which are usually targeted by spammers to steal personal information, money, data, etc. There are several models exist for SMS and Email spam detection out of which supervised learning-based model is mostly efficient. However, the comprehensive study for spam detection with consideration to multiple domains simultaneously, is missing. In this paper an experimental evaluation on the effect of using feature selection techniques in the domain of SMS and Email spam detection is conducted. Parameters such as ROC and Train/Test time along with common parameters are used to evaluate performance of spam detection models. The experimentation results shows that the choice of feature selection technique has profound effect on the performance of the spam detection model and can be seen in the result generated using different evaluation measures out of which some are not used in the both domains previously.
Load Balancing Algorithms in Cloud Computing

The evolving nature of cloud computing in recent years has increased the demand for accessing resources online. This has resulted in a need to manage more traffic and a greater number of requests for cloud servers. This continuing growth of requests necessitates a more effective load balancer which distributes the workload across various instances. It reduces the risk of experiencing performance issues, the response time of a task can be minimized, and the resource utilization can also be improved. Many cloud-based companies are using both static and dynamic load balancing algorithms. This paper critically reviews various types of load balancing algorithms based on performance metrics.
Introduction

The Fourth Industrial Revolution, also known as Industry 4.0 or 4IR, could represent the twenty-first century’s rapid transformation of technologies, industries, and societal patterns and processes as a result of growing interconnectedness and intelligent automation. In relation to its technological aspect, the 4IR concerns automation and data exchange in various technologies and processes (Schwab, 2018). These consist of Artificial Intelligence (AI); Advanced Robotics; Cyber-Physical Systems; online social and gaming platforms; the Internet of Things (IoT) including the Internet of Military Things and the Industrial Internet of Things; Cloud Computing; Cognitive Computing; and many other technologies. Whilst these technologies have numerous potential “to raise global income levels and enhance the quality of life for populations” globally (Schwab, 2018), they simultaneously present a myriad of challenges that impact businesses, governments and the wider populations. Arguably, even more importantly, this technological revolution could have a significant impact on the national and international security landscape, influencing the likelihood and character of both cyber and conventional warfare. Likewise, as more powerful technologies emerge and become easier to utilise, individuals and groups such as terrorists or organised criminals will gain even more advanced capabilities to cause mass devastation and chaos (Schwab, 2018). Consequently, as cyberspace becomes more complex and ubiquitous, so do the tactics of modern cybercriminals and terrorist groups. The aforementioned technologies combined with anti-forensic tools and methods will all contribute to the offline and online arsenal of state and non-state adversaries and criminals.
Neural Networks from Biophysical Applications in Microelectronics Parameters Measurements

Apply of artificial neural networks (ANN) in technology has started at the second half of the twentieth century. This arose as a consequence of extensive research in the field of biophysics carried out in the middle of the twentieth century. These biophysical researches have led to knowledge how a neuron as a basic element of a living being nervous system receives and processes signals. Living organisms use no mathematical model, the neurons processes signals by a simple algorithm, signals are massively parallel processed, the output is the superposition of all parallel processed signals. Artificial neural networks which are formed on these principles are used solving various problems such as pattern classification, clustering, function approximation and optimization. They can replace theoretical models that are hard to be presented analytically with data obtained using these models. In this paper we analyzed electrophysical parameters based on Murata powders and processing consolidated samples technology. Between different electric, dielectric and ferroelectric characteristics we applied our neuro network approach on tgδ (loss factor) and for predicting the grain sizes in the function of T and time (τ) as distribution function between, the experimentally applied sintering temperatures (for example from 1190 to 1370 °C). Here, we continue to use neural networks to predict microelectronic parameters on the thin films around the grains based on the bulk surface measurements. All these extended applications are the quite new tool and method for getting the distributed parameters values at the real microstructure level what is opening new perspectives in microelectronics miniaturization designing.
Paraphrase and Ambiguity

In natural language communication, the speak mode maps cognition-internal content into raw surface data,1 while the hear mode maps raw surface data into content. The speaker may have a choice between different surfaces for the same content, called paraphrase, and the hearer may have to choose between different contents for the same surface, called ambiguity (FoCL 11.3).
Recursion and Grammatical Disambiguation

The grammatical structure of natural language signs includes recursion. This raises the question of whether unbounded recursion may cause an unbounded increase of ambiguity. If true, it would be an obstacle to successful communication. Also the construction of a linguistic example is most challenging.
Agent-Based Memory as an On-Board Database

Today’s databases are computer memories used for storage in, and retrieval from, large collections of systematic information, such as bibliographical, medical, trade, banking, insurance, and tax data. In natural agents, cognitive content is stored in, and retrieved from, the brain’s memory. In artificial DBS agents, natural memory is reconstructed as an on-board database.
Outline of DBS

DBS models the cycle of natural language communication as a transition from the hear to the think to the speak and back to the hear mode (turn taking). In contradistinction to the sign-based substitution-driven approaches of truth-conditional semantics and phrase structure grammar, DBS is agent-based and data-driven. The goal is an efficient computational theory of natural language communication suitable for a talking autonomous robot.
Secure and Efficient Certificateless Authentication Key Agreement Protocol in VANET

An authentication key agreement protocol is designed to realize mutual verification and generate symmetric key shared by participants in symmetric cryptosystem. Aiming at the security problem of an authentication key agreement protocol using certificateless aggregate signature in VANET, an improved certificateless authentication key agreement protocol based on elliptic curve is proposed, and then the security of the protocol is analyzed. The analysis results show that the protocol can make up for the security loopholes of the existing schemes, meet the resistance to temporary secret key leakage, resistance to man-in-the-middle attack, the forward security, anti-impersonation attack and other security properties. Compared with the security and efficiency of congener schemes, this scheme provides higher security at the same time has the same operation efficiency of original scheme, which can be used conveniently in resource-constrained VANET.
Impactos na vida acadêmica pela pandemia da COVID-19 em estudantes de enfermagem de uma universidade particular do Rio de Janeiro

The research aims to analyze the impacts of the COVID-19 pandemic on the academic life of nursing students at a private university in Rio de Janeiro. The methodological approach was qualitative, with the participation of 23 nursing students from the 6th to the 10th period. Data collection was carried out at the educational institution through an open questionnaire, from September to November 2022, with ethics opinion No. 5,558,461, which generated the thematic unit: Impacts on academic life due to the COVID pandemic -19 in nursing students at a private university in Rio de Janeiro and was divided into categories. Category 1: Characteristics of the difficulties that the COVID-19 pandemic brought to academic life; category 2: Academic performance during the pandemic; Category 3: Seeking psychological help during the semesters during the COVID-19 pandemic; Category 4: The academic today after going through the pandemic in the years 2020 and 2021; and Category 5: The positive and negative points generated by the COVID-19 pandemic in academic life. It is concluded that the pandemic affected nursing students at different levels, but in the face of all the adversities, students found ways to face them and move towards academic training.
Evaluation model of distribution station area based on multi-source heterogeneous data fusion

Aiming at the massive distribution big data of various types, attributes and different time scales in the distribution station area, an evaluation model for the operation status of the distribution station area based on the fusion of multiple heterogeneous data is proposed. The basic operation status of the distribution station area is analyzed, including the operation evaluation characteristic index of the distribution station area, and the normalization processing method of quantitative data and textual qualitative data is proposed based on heterogeneous data. A random forest algorithm is proposed for state evaluation, and the corresponding indicators are classified and predicted. The process is given for the overall evaluation model. A specific case is used to analyze the effectiveness of the model proposed in this paper.