Reviewerly/exHarmony huggingface dataset:
Customizing OWASP ZAP: A Proven Method for Detecting SQL Injection Vulnerabilities

As web applications become increasingly popular for offering data and services among businesses and organizations they also become more susceptible to security risks. Many organizations rely today on Web Vulnerability Scanners (WVSs) to identify vulnerabilities in their web applications. However, one of the most prevalent types of web application vulnerabilities, SQL Injections (SQLi), can often go undetected by WVSs. OWASP ZAP is an open-source web vulnerability scanner that allows security professionals to develop rules to improve vulnerability detection capability. In this paper, a new method is proposed to improve the detection capability of OWASP ZAP for SQLi. We aim to accurately detect the four major types of SQLi: Error-based, Union-based, Time-based blind, and Authentication Bypass. We implemented this new method using OWASP ZAP detection policies. The method was tested against two benchmark vulnerable web applications, Mutillidae and WebGoat, and was shown to significantly improve the detection of SQL injection attacks.
Designing augmented reality for makerspaces: Guidelines, lessons and mitigation strategies from 5+ years of AR educational projects

Makerspaces are a relatively recent type of open-ended environment where users learn through authentic problem solving and interactions with peers. Augmented reality (AR) technology can improve learning and collaboration in such spaces, but it is unclear how one might design AR applications suitable for the physical, social and pedagogical richness of these environments. In this paper we present 5 research projects that explored the use of AR in makerspaces, covering various learning topics, physical configurations, and collaborative activities. We discuss lessons learned and distill these into 14 design guidelines. We conclude with a discussion of tensions to consider when designing AR for open-ended learning environments.
Privacy Analysis of Federated Learning via Dishonest Servers

Federated Learning (FL) has gained popularity for its ability to improve model training while protecting user privacy. However, recent studies have shown that FL can be vulnerable to active reconstruction attacks by dishonest servers. Specifically, a dishonest server can obtain users’ private data in numerous ways via gradient inversion based on the core neural network concept of neuron activation. Addressing this style of attack is imperative to preserve user privacy and remains a major challenge due to its sophisticated nature. In this paper, we examine various active reconstruction attacks by a dishonest server and provide comprehensive evaluations to demonstrate their effectiveness and practicality, highlighting the risks associated with FL systems.
Test Maintenance for Machine Learning Systems: A Case Study in the Automotive Industry

Machine Learning (ML) systems have seen widespread use for automated decision making. Testing is essential to ensure the quality of these systems, especially safety-critical autonomous systems in the automotive domain. ML systems introduce new challenges with the potential to affect test maintenance, the process of updating test cases to match the evolving system. We conducted an exploratory case study in the automotive domain to identify factors that affect test maintenance for ML systems, as well as to make recommendations to improve the maintenance process. Based on interview and artifact analysis, we identified 14 factors affecting maintenance, including five especially relevant for ML systems—with the most important relating to non-determinism and large input spaces. We also proposed ten recommendations for improving test maintenance, including four targeting ML systems—in particular, emphasizing the use of test oracles tolerant to acceptable non-determinism. The study’s findings expand our knowledge of test maintenance for an emerging class of systems, benefiting the practitioners testing these systems.
A Hybrid Approach to Osteosarcoma Detection Using Densenet201-SVM Model

Osteosarcoma, also known as osteogenic sarcoma, is the most prevalent primary bone tumor in children and is one of the leading causes of cancer-related mortality. Incidence peaks in adolescence, particularly during a growth spurt. It can occur in any bone, but the majority of cases arise from the metaphyses of long bones, this study, focuses on the development of an automated model which helps in the detection of Osteosarcoma, The dataset used in this research was obtained by a team of clinical researchers from the University of Texas Southwestern Medical Center in Dallas, through the use of archived samples from 50 pediatric patients who received treatment at Children's Medical Center in Dallas between 1995 and 2015.the detection process is achieved by combining one of the pre-trained transfer learning models namely, Densenet201 for feature extraction, and classifying these features in order to detect Osteosarcoma tumor, the proposed model achieved a high performance and accuracy of 90.96%.
Poster: Link between Bias, Node Sensitivity and Long-Tail Distribution in trained DNNs

Owing to their remarkable learning (and relearning) capabilities, deep neural networks (DNNs) find use in numerous real-world applications. However, the learning of these data-driven machine learning models is generally as good as the data available to them for training. Hence, training datasets with long-tail distribution pose a challenge for DNNs, since the DNNs trained on them may provide a varying degree of classification performance across different output classes. While the overall bias of such networks is already highlighted in existing works, this work identifies the node bias that leads to a varying sensitivity of the nodes for different output classes. To the best of our knowledge, this is the first work highlighting this unique challenge in DNNs, discussing its probable causes, and providing open challenges for this new research direction. We support our reasoning using an empirical case study of the networks trained on a real-world dataset.
Securitizing Microcredits Using blockchain

This study’s primary purpose is to evaluate the viability of securitizing micro-credits using blockchain technology. To answer this question, we conducted a theoretical analysis of the present obstacles in the microfinance industry and proposed an innovative strategy to address the major issues of liquidity and process efficiency. This study underlines, from a safety standpoint, the need to consider the influence of regulatory limits as well as the requirement for an expert’s evaluation of the suggested model.
Multi-level Encryption for Agricultural Data Protection

Big data analysis is an important way for decision making. The development of data storage and computation technology motivate people to store and analyze large amount of data. However, security concerns may arise especially when people want to do data transmission. Current solutions including encrypting the whole data set might be too intensive to use because such encryption process might be time consuming. In this paper, we propose a different approach: using multi- level encryption methods for different parts of the data. This approach can fully utilize the constrained resources to achieve good encryption effects. The analysis and experiment results over agricultural data show the effectiveness of our proposal.
Translation Of Architectural Sketch Images To Building Images Using CycleGAN

Sketching and drawing is the hallmark of an architect's toolkit. Architect needs to somehow show his idea from his sketch and also designing a sketch image from a building image will help the architects to record the different designs. Hence, using image-to-image translation strategies, it is possible to convert architectural vector designs into actual building images and vice versa. Image-to-image translation is becoming more popular as a result of advancements in information technology and computer vision. In a class of vision and graphic problems known as “image-to-image translation”, the objective is to discover the relationship between an output image and an input image. The main objective of the model in this study is to convert architectural sketches into building images and vice versa using Image to image translation on unpaired image datasets to help reduce the communication gap between architects and their clients. A suitable GAN model called Cycle GAN has been used to train on collected unpaired image dataset. Utilizing the trained model, a web application is created that is open for everyone. A Python library called flask is used to create the web application's front end in order to provide a straight-forward and user-friendly interface.
System for Detecting Traffic Sign Boards Using Machine Learning

When the new generation of self-driving cars is fully automated, it becomes crucial for the vehicles to recognize the road signs that are prominently posted on the sides of roads in big cities. To reach level5 of automation, the systems must recognize and adhere to traffic laws. Accuracy in the technology is required when firms like Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, and others are developing autonomous and self-driven vehicles. The vehicles should be able to understand traffic signs and make judgments in accordance with them. The current system allows automobiles to recognize stop signs and road bumps in front of them. It also measures the space between the two vehicles too. Videos with at least 20 different signs will be used to create the graphics, together with local photographs and information from a public database. The photos will be categorized using Keras. The construction of a CNN model will be followed by its training and validation. The graph will be plotted using Matplotlib to determine the model's accuracy. For the interaction, a Python GUI will be constructed. The automated driving system will be able to ensure that the traffic signs are identified and are interpreted by the system with improved performance than the current capacity interaction once the traffic sign boards are recognized.
Sign Language Recognition using Python and OpenCV

Conversing with an individual along with listening to impairment is consistently a primary problem. Authorized foreign language has indelibly come to be the ultimate cure-all as well as is an extremely effective resource for people with listening and also pep talk impairment to communicate their emotions and points of view to the world. It generates a combination method between all of them as well as others that are softer and less sophisticated. Having said that, the creation of an authorized foreign language alone is actually inadequate. Certainly, there certainly are numerous strings attached to this benefit. The legal actions are frequently mixed up and also misunderstood by someone who has never heard of it or even recognizes it in a different language. Having said that, this interaction void, which has actually existed for many years, may currently be tightened along with the introduction of numerous methods to automate the discovery of authorized motions. Within this particular study, our team presented an Authorize Foreign Language acknowledgment utilizing United States Authorize Foreign Language. Within this particular analysis, the customer needs to have the capacity to squeeze pictures of the possession in motion utilizing an internet electronic camera, and the device will forecast as well as show the title of the recorded picture. To find the possession motion and collect the history to dark, our team employs the HSV color protocol. The pictures go through a collection of handling actions that include numerous personal computer sight methods, including the conversion to grayscale, dilation, and mask function. Additionally, the area of enthusiasm, which, in our instance, is actually the possession motion, is actually segmented. The functions drawn out are actually the binary pixels of the picture. This study utilizes Convolutional Neural Networks (CNN) to categorize the image. The proposed model has the capacity to acknowledge 10 United States Authorized Motion Alphabets along with higher precision. The proposed version has actually attained an exceptional precision of over 90%.
Deep Learning-based Image Text Processing Research<sup>*</sup>

Deep learning is a powerful multi-layer architecture that has important applications in image processing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neural networks and recurrent neural networks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neural networks, recurrent neural networks, and other text classification methods. Finally, the development trend of deep learning in the field of text and image processing and the difficulties to be further researched are summarised and prospected.
Autonomous Driving Using YOLOP

A panoptic driving system generates autonomous driving directives only from images/frames captured by a camera. For autonomous driving, the network must make judgments at fast speed, with high precision, and in real time. The paper proposes Autonomous Driving with You Only Look Once for Panoptic Driving Perception (YOLOP). This network comprises of a common encoder and four distinct decoder heads that handle individual tasks such as traffic and object identification, drivable area segmentation, lane line segmentation, and traffic sign detection and recognition. The proposed network produces cutting-edge results on the Berkely Deep Drive (BDD100K) dataset, as well as the German Traffic Sign Recognition Benchmark (GTSRB) and German Traffic Sign Detection Board (GTSDB) datasets for traffic signs. The model is then implemented on a Raspberry Pi 4 Autonomous bot, which has the potential of making autonomous judgments on numerous characteristics such as left- and right-hand turns, speeding up or slowing down in accordance with the speed limit traffic sign, and halting when it encounters an obstacle. The network achieves a mean average precision (mAP) @ 0.5 of 76.5% for traffic object identification and recognition, an Intersection over Union (IOU) of 91.5% for navigable area segmentation, an accuracy of 70.5% for lane detection, and a mAP @ 0.5 of 99.4% for traffic sign detection and 94% for traffic sign recognition.
Prediction of Bitcoin Price using Optimized Genetic ARIMA Model and Analysis in Post and Pre Covid Eras*

Predicting Bitcoin price is a universal research area as it attains significance in predicting the market way of its rate so that, investors could procure profits. Concurrently, with the evolution of Machine Learning (ML), researchers attempted to use ML based algorithms for forecasting the Bitcoin price. However, these researches have resulted in inefficient prediction due to error rate. For alleviating such pitfalls, this study intends to forecast the Bitcoin price by comparing its deviations pre and post Covid using suitable ML algorithms. To achieve this, the study proposes Auto Regressive Integrated Moving Average (ARIMA) with Optimized Genetic Algorithm (OGA). In this case, ARIMA model is considered as it possess the innate ability in capturing standard temporal reliances which is distinct to time-series data. Further, hyperparameters are selected by GA based on the fitness function. Based on this, hyperparameter tuning is performed which assist to improvise the model performance. For determining if there exists any deviations in Bitcoin price (pre and post Covid), Augmented Dickey Fuller (ADF) test is considered. Further, comparative analysis is regarded in accordance with performance metrics to validate the performance of the proposed system which proves its effectiveness in predicting Bitcoin price.
A Super-Resolution Technique With An Ensemble Deep Learning Based For Cervical Cancer Detection System

A novel way for classifying cervical cells using Convolutional Neural Networks (CNN) is presented in this work. Features are learned automatically in this technique. Classification accuracy and model diversity can be enhanced using ensemble of CNN. This is a multi-step process and it requires the training of various CNN models and they must selected accordingly for ensemble. So, for resolving these issues and for creating expert medical decision making systems, Convolutional Neural Network and Random forest with Boosting called CNNRFB is used in current research work. For cervical cancer Prediction, an optimal prediction model can be constructed by medical practitioners using this solution. One dataset with all relevant information about various cervical cancer is created initially. In an image, without information loss, image pre-processing is done using super resolution technique. Self-similarity based Non-Local-Mean (NLM) metric is used as a base for this Super-Resolution (SR) algorithm. The NLM weight is used as a self-similarity metric under noisy environment for computing best self-example accurately. Deep ensemble learning methods are used for performing image classification and cancer prediction. System's accuracy are decided by dataset values and deep learning training technique. Python code is used for executing and simulating all the techniques. The experimental results shows the accuracy, recall, f-measure and precision produced by proposed technique shows better results when compared with the traditional approches.
Reversible Data Embedding Scheme In Encrypted Image Using Arnold Transform for Satellite Images

An Satellite images are transmitted through the network undergoes security issues, losses in data and high confidentiality which concern the authenticity, integrity and security. In this paper, a secure data transmission in an encrypted image for the application of satellite images is discussed. Initially, the Arnold transform is utilized to obtain the encrypted image of the original image with a key. Then, to embed the message data, reversible data embedding scheme is applied. The text message is converted into a sequence of binary data and then embedded in an encrypted Image using the histogram shifting scheme. The reverse process is applied to retrieve the message and Image. The result shows that there is no distortion in the recovered image after extracting the secret message. Also it is observed the recovered message and identical to original message
Human Activity Recognition Using LRCN

Using the capabilities LRCN it obtained to increase the multimedia, recognize human movement in videos. HAR is a one ongoing research field. On various benchmark datasets, new records have been established using video as a series of frames. Convolutional neural networks (CNN) and long short-term memories were proposed in this (LSTM). CNN network is applied to get feature vectors for each videos and LSTM network is use to classify the videos. Standard activities contains many activities horse racing, taichi, walking with dog, basketball, swing, etc. In this proposed system we have achieved 99.5% of Accuracy.
FLICM clustering with matrix factorization based course recommendation in an E-learning platform

Technology-enabled learning has progressively grown for research areas with wide application of information and communication technologies for numerous standard-compliant Learning and Open Educational Resources. This provides formidable support to users for the selection of courses when they want to develop the course with available learning materials. But selecting a course via searching learning objects is an inherently complex operation having various repositories. In an E-learning Platform, many complexities arise due to various software tools and specification formats that hinder the success of the course. In this paper, many obstacles in the E-learning platform are eradicated by utilizing Fuzzy Local Information C-Means (FLICM) clustering with matrix factorization for the selection of courses. The dataset utilized in this work is E-Khool review data, from which an agglomerative matrix is generated. Here, the agglomerative matrix consists of the learner series matrix and course series matrix along with their binary matrix. After this process, course grouping is carried out by FLICM clustering with matrix factorization. Moreover, group course bilevel matching, followed by relevant learner retrieval and group user is done by Minkowski and Chebyshev distance. From this learner’s preferred course is retrieved and then a recommendation using matrix factorization is carried out. Finally, the course is recommended for the user based on maximum rating. Furthermore, the performance of developed FLICM_matrix factorization is achieved by performance metrics, like precision, recall, and f-measure with values 0.915, 0.850, and 0.882, accordingly.
Video Image Information Mining Algorithms for Badminton on-the-Spot Tactics Analysis

Due to the changes in the camera angle or the working direction of the athlete in the sports video, this method cannot perform an effective regional analysis. Hence, the efficient analysis of images is essential for the action recognition, hence, this paper studies the video image information mining algorithms for badminton on-the-spot tactics analysis. The designed system contains the image segmentation, action recognition and the badminton on-the-spot tactics analysis. To perform image segmentation, the FCM model is considered To perform action recognition, based on the obtained skeleton data, the model is trained by the action label data to classify it. An efficient action representation can capture both static and kinematic information. Then, the system is verified through the real-time data sets. The proposed model is proven to be efficient.
People Tracking and Counting using Jetson TX2 Kit with Tracking Algorithm

The population growth has risen across the globe. The number of people gathering in public places due to population growth has increased. Based on the timings and number of customer visit, people tracking and counting system give the tally of the number of people in specific place. Here Nvidia Jetson TX2 is utilized with tracking algorithm. Hence this system provides solution to the problem. Understanding the flow of population and generating the heat maps is an important key role. Deep learning models will help to give the better performance with respect to object detection. But especially in crowded areas where the people movement is often more there will be a problem is detection of faces because of occlusion. Here in this paper we have used the deep learning techniques to resolve these issues. We have applied the feature extraction techniques to facilitate the correctness in the detection process. The NVDIA Jetson TX2 board can be used for the real time operation of the people tracking using the embedded board. This method will give the accuracy better than the traditional way of face detection using the facial recognition algorithm.