Reviewerly/exHarmony huggingface dataset:
Auto Detection of Personal Protective Equipment on Human

In our Daily life cycle, we come across many situations where being cautious and safe is the priority. Often these situations occur at a particular and Defined place, like Hospitals, Airports, Food Sector, etc. Not only for a Personal view but this can affect a group of people too. The Fastest way to spread of Bad Bacteria and Viruses is Airborne i.e. by contact with the surrounding air. This includes a significant aspect of the spread of the virus through CO2 released by Human beings, Sneeze, coughing, etc. At those times, wearing a Mask comes in it for the best precaution. When it comes for Food preparation and packaging, head caps or surgical caps are one of the identified asset. Wearing a surgical cap prevents hairs and bacteria in it to mix with the food. There are many Algorithms and methodologies to Provide Object Detection but this paper has the Efficient working Approach. Our Model will detect Human beings at the Entrance of Respective Areas where Supervision and Care should be taken without Fail. The Screen will show as well as a Voice will Convey whether the Person is being cautious and wearing a Mask and cap before entering a Cautious place or not.
Analysis of Linux Server Performance

This report presents an analysis of how performance tuning on Enterprise Linux impacts the performance of different server programs. While researchers historically measure performance based on one or more of the following parameters: memory, I/O, networking or CPU, these are just some of the server programs that should be considered when performance are analysed. Naturally, these four parameters are influenced differently by the impact of tuning and tuning which, in turn, sets the acoustics curve of each one of the server programs being measured.
Data Hide Security Using Image Object

The records leak of touchy facts on structures has a critical chance to company facts protection. Statistics display that the mistaken encryption of documents and communications because of human mistakes is one of the main reasons for records loss. The fast improvement of facts through that made it simpler to ship the facts correctly and quicker to the vacation spot. There are many transmission media to switch the facts to vacation spots like e-mails; at the identical time, its miles can be simpler to adjust and misuse the treasured records through hacking. In order to securely transmit information to its destination without any alterations, there are various techniques such as cryptography and steganography that can be used. Steganography refers to the technique of concealing a message or information within another file, text, or media, in a manner that makes it undetectable to those who are not privy to its existence or significance.. By using steganography, it is possible to covertly transmit information without drawing attention to the fact that it is being transmitted. Current developments prefer the usage of virtual photo documents as the quilt report to cover any other virtual reportthat consists of the name of the game message or records This paper the usage of k-means and encryption set of rules wherein clustered the facts the usage of a clustering set of rules after which the output of the clustering has then hidden the usage of photo steganography to offer a protection layer to that file.
Detecting Fake Profiles on Social Networks: A Systematic Investigation

Social media platforms are an excellent way to remain in touch with loved ones. Still, they can also be a breeding ground for fake accounts. These accounts may be used to spread malicious content or disinformation or to manipulate people's thoughts and feelings. One way to identify fake social media accounts is to use machine learning algorithms. Machine learning enables computers to learn without being explicitly programmed, automatically recognizing patterns in data. This paper examines several methods implemented and tested by machine learning and deep learning algorithms. We have also looked into the numerous aspects associated with user profiles on social networks.
Incubation of a Metaheuristic Searching Approach for Intrusion Detection into A New Constrained Crow Search Algorithm

Numerous search techniques have been created to address diverse optimization problems in a variety of domains, grabbing the interest of many author/researchers. Optimization algorithm is frequently influenced by the environment/nature inspired or involves modifying current algorithms. The novel methods are currently configured and concentrated on reaching the intended optimization objective. While this is productive and beneficial in the short term, it is insufficient in the long term because it needs to be repeated for all novel issues that appear in addition to specialized challenges, completely consistent technique can be utilized for all real-world situations. In this research, we have to improve its global optimization search space for optimal selection is a modern meta-heuristic algorithm based on crow intelligence large dimensions of constraint variables. The experimental findings demonstrate that when compared to other feature selection algorithms, the feature subset obtained by Constrained Crow Search Algorithm (CCSA) has higher classification accuracy and higher dimensional compression capabilities, indicating that Constrained Crow Search Algorithm (CCSA) has the ability to deal with feature selection problems with strong competitiveness and sufficient superiority.
A Secure Electronic Medical Record Authorization System in Clouds

In current healthcare environments, healthcare carriers are extra inclined to shift their digital clinical report structures to clouds. Instead of constructing and keeping committed records facilities, this paradigm allows to reap decrease operational fee and higher interoperability with different healthcare carriers. However, the adoption of cloud computing in healthcare structures may additionally boost many safety demanding situations related to authentication, identification control, get right of entry to manipulate, consider control, and so on. In this paper, we recognition on get right of entry to manipulate problems in digital clinical report structures in clouds. We advocate a scientific get right of entry to manipulate mechanism to help selective sharing of composite digital fitness facts (EHRs) aggregated from diverse healthcare carriers in clouds. Our technique guarantees that privateness issues are accommodated for processing get right of entry to requests to sufferers' healthcare statistics. We additionally display the feasibility and performance of our technique through imposing a proof-of-idea prototype in conjunction with assessment results.
Novel Deep Neural Network for Suspicious Activity Detection and Classification

Implementing preventive measures against gunshots and terrorist attacks in public areas with heavy foot traffic is crucial. Although security cameras are already commonplace, real-time reaction and 24/7 monitoring needed for automated detection techniques. As opposed to a weapon alone, a weapon in the hands of a human is thought to pose a larger threat. This paper has introduced a novel algorithm for multiple gun detection with an innovative deep learning neural network (DNN) model. The parameter for human suspicious activity in this paper is a person with a weapon(gun) and a person wearing a helmet with a weapon(gun). The temporal complexity of the Proposed System Architecture (PSA) on various hardware platforms is also explored. The algorithm gives 99% accuracy as compared with other existing methods.
CISER: Customized Institute Specific Search Engine for Retrieving Research Papers

This paper proposes a methodology of a search engine system for searching research papers customized to our institute students. Most of the courses are associated with course projects where students face difficulties in finding the best research papers associated with the course. So, here we propose a customized mechanism to search the research papers published by the faculties of the institute. The input for the proposed search engine can either be the course name or the topic itself. We give users two options: search by course name and topic. If the course name is given as input, we get the corresponding keywords for the course, and then we implement semantic similarity on the Author Keywords. If the user searches by topic, we perform semantic similarity using the given topic and the Author Keywords of the research papers. We have also created a web interface using Django.
Blockchain Based Record Management System in Hospitals

Every industry is expanding too quickly and adjusting to this new technology as it develops. Since it has the potential to deliver more precise and economical patient care, healthcare data management has recently attracted a lot of attention. Even today, many hospitals hold their own autonomous record management system, which causes security issues. In centralized record management systems, data privacy, centralized data stewardship, and system vulnerability problems affect traditional client-server-based and cloud-based health data management systems. Blockchain technology has a promising future in the healthcare industry because of its immutability, transparency, privacy, and security properties, which can address certain critical problems with the health management system. A more patient-oriented approach in healthcare systems is required to improve the accuracy and transparency of medical data. In healthcare systems, health records are the most sensitive asset that must be unique and protected across the system. Our objective is to showcase the potential use of blockchain technology in health record management systems in hospitals. In this paper, we demonstrate a health record management system that uses blockchain technology to store the medical records of a patient across multiple hospitals. The proposed system will mainly help in maintaining consistency issues related to data along with improved security in the system.
A Truncated SVD Framework for Online Hate Speech Detection on the ETHOS Dataset

Hate content on social media is currently one of the most significant risks, where the victim is either a single individual or a group of people. In the current scenario, online web platforms are one of the most prominent ways to contribute to an individual's opinions and thoughts. Free sharing of ideas on an event or situation also bulks on the web. Information sharing is sometimes a bane for society if primarily used platforms are utilized with some lousy intention to spread hatred for intentionally creating chaos/ confusion among the public. Users take this as an opportunity to spread hate to get some monetary benefits, the detection of which is of paramount importance. This article utilizes the concept of truncated singular value decomposition (SVD) for detecting hate content on the ETHOS (Binary-Label) dataset. Compared with the baseline results, our framework has performed better in various machine learning algorithms like SVM, Logistic Regression, XGBoost, and Random Forest.
Pedestrian Direction Estimation: An Approach via Perspective Distortion Patterns

Knowledge of pedestrian's walking direction is very crucial in multiple domains of video processing. This paper proposes a graph based, robust and light weighted model for direction estimation of pedestrian's walk by using the property of perspective distortion. Here perspective distortion pattern is used as an advantage in estimation of direction. The graph-based solution uses 3 parallel approaches for estimating the direction: Perspective Distortion Graph, Centroid Displacement and Clustering of Vanishing point. A pedestrian in a frame can be identified by bounding boxes. The temporal dimensional features of bounding boxes are height and width and these features changes for a particular object from frame to frame as the objects moves. These changes are unique for each direction for each object. These changes in dimension along with clustering of vanishing point and centroid displacement is used for the assesment of the pedestrian's walk direction. All the existing approaches need some sort of pre-processing on the frames, which makes the model more complex and time consuming. In the proposed model, the video sequence is applied on YOLO V4 algorithm and bounding boxes are obtained. By analysing the changes from frame to frame for the dimensions, graphs are plotted and minimum and maximum extremas are detected form the graph by eliminating soft extremas. After that envelope is placed for the graph and an average line is drawn based on the envelope, which will give the inference about the direction of walk of the pedestrian. The perspective distortion graph will not give accurate estimation for all directions. So, Centroid displacement and clustering of vanishing point are also used for direction estimation. The result obtained from the three methods are combined and form a robust model. For accurately estimating walk direction, the movement is limited to 8 different directions. For experiment, NITR Conscious Walk dataset and self-acquired dataset are used. With balanced accuracy of 97.003% and 96.25% and a false positive rate of 0.63% and 0.65%, respectively, the model produces good results for the above dataset.
Tiny Face Presence Detector using Hybrid Binary Neural Network

Face Detection plays a key role in “always-on” applications such as mobile phone unlock or smart doorbells. Deep learning-based face detection solutions have demonstrated state-of-art performance in terms of accuracy; however generally, the improved accuracy comes with a large computation and memory requirement overhead. This can result in high energy consumption which is a significant cost that can overrun the energy budget especially in battery powered systems. Recent solutions to this problem have advocated the use of a low power always-on sensor running a rudimentary algorithm that can merely indicate the ‘presence’ of a face with low accuracy and in turn ‘wake-up’ a more powerful device executing a high accuracy face detection algorithm. In this paper we present the design of two deeply quantized (binarized) light weight face presence detection deep learning based models that can function as wake up models. The models achieve high accuracy> 98% with a corresponding memory footprint being limited between 3KB and 100KB allowing them to be deployed in highly resource constrained ‘always-on’ embedded platforms.
Feature Drift Detection using Overlapping Window and Mann-Whitney U Test

As data is ubiquitous in several real-world problems, data stream mining is a rapidly growing research area. It is expected that data stream sources will undergo changes in data distribution due to their ephemeral nature, which is called concept drift. There has been a very scant study of one particular type of drift, namely feature drift, so this paper aims to explore that type of drift. As a result of feature drift, learners must detect and adapt to changes in the relevant subset of features and the changing nature of the learning task itself. An approach to detecting feature drift was developed in this work. We used overlapping landmark windowing to keep the previous data's properties windows while analyzing the most recent data. Using the Mann-Whitney U test, we compare and store the distribution of each feature in two consecutive windows. Whenever the statistical properties of the window exclude a particular boundary from the distribution, drift is detected. We validated the effectiveness of our proposal by conducting experiments on real data.
Blockchain based Secure Data Storage Verification Algorithm for Smart City Environment

Blockchain based distributed ledger mechanism has got a wide range of applications in this era. The degree of security measurement is always a bottleneck. Since there are technologies to break it. Data sharing through cloud for smart cities, collaborative actions, remote activities based on the data at the source, etc., need to be secure and free from masquerading and tampering. In most of the cases the data is pushed into the cloud from access points, sensors, or remote access centers. Preventing the data access and identifying anonymous access to these sensors require an enhanced security mechanism that prevents the inconsistent data to be transferred to the cloud. We propose a blockchain based enhanced security system that protects the data from the access point it leaves for the cloud using a distributed ledger. The consensus mechanism ensures the trust of existing sources during the data transfer from the source to the cloud. The trust generated by the subsequent data blocks with the security hash key ensure the integrity of the data and validity of the actual source. This prevent the illegal access to the data sharing points. We have verified the degree of security offered by our proposed model using informal analysis. We found that our method has improved the security of data access.
Mutable Blockchain for Identity Management

Identity management solutions are generally designed to facilitate the management of digital identities and operations such as authentication, and have been widely used in real-world applications. In recent years, there have been attempts to introduce blockchain-based identity management solutions, which allow the user to take over control of his/her own identity (i.e. self-sovereign identity). In this paper, we provide a short review of existing blockchain-based identity management methods. Based on the existing methods, we identify potential gaps and opportunities, and proposed an idea to overcome them using mutable blockchain concept[7] [10]. We have used two algorithms-creation of new identity and updation of existing identities on the blockchain. The main contribution of the proposed algorithm is to store the personal data securely in local database and the address of the corresponding data is stored in the blockchain in hashed format. The results for the proposed approach demonstrate that the mining difficulty and mining time varies with the number of transaction in per block.
Recognition of Characters using PCE based Convolutional LSTM Networks from Palaeographic Writings

The historic paleographic writings that contributes to cultural heritage of India were inscribed on various materials such as stone inscriptions, rock carving, palm manuscripts, pots, coins, copper plates etc. Archaeological departments throughout the world have undertaken massive digitization projects to digitize the historical contents. But it is highly complicated as it involves images with complex backgrounds, noises and various illumination conditions. The paleographic writings are camera captured and processed for recognition of characters. A character recognition system is an inevitable tool to offer global visibility to the paleographic writings. Automatic character recognition is a challenging problem as in the proposed work it needs a cautious blend of image enhancement, patch extraction, feature extraction, classification and recognition techniques. This involves extracting the sequence of image patches and feature vector of the patches using Convolutional Neural Network and feeding the feature vectors using attention mechanism to recognize the character with LSTM model. As paleographic writings have lengthy sequence of characters which requires special attention during character recognition. The proposed work is an attempt to identify and recognize the historical Tamil paleographic writings by extracting the sequence of patches from the image and feeding them into a CNN-LSTM framework. The proposed method mainly consists of pre-processing, feature extraction, and character-level recognition. The LSTM network is built and the sequence of feature vectors is fed to the network and trained. The sequence of characters is recognized. The performance of the proposed method recorded an character recognition accuracy of 97.9%.
Optimal hardware implementation for end-to-end CNN-based classification

Convolutional neural networks (CNN) show promising results in many fields, especially in computer vision tasks. However, implementing these networks requires computationally intensive operations. Increasing computational workloads makes it difficult to use CNN models in real-time applications. To overcome these challenges, CNN must be implemented on a dedicated hardware platform such as a field-programmable gate array (FPGA). The parallel processing and reconfigurable features of FPGA hardware make it suitable for real-time applications. Nevertheless, due to limited resources and memory units, various optimizations must be applied prior to implementing processing-intensive structures. Both the resources and the memory units used in hardware applications are affected by the data types and byte lengths used to display data. This study proposes arbitrary, precision fixed-point data types for optimal end-to-end CNN hardware implementation. The network was trained on the Central Processing Unit (CPU) to address the classification problem. The CNN architecture was implemented on a Zynq-7 ZC702 evaluation board with a target device xc7z020clg484-1 platform utilizing high level synthesis (HLS) for the inference stage, based on the calculated weight parameters, and predetermined hyperparameters. The proposed implementation produced the results in 0.00329 s based on hardware implementation. In terms of latency metrics, the hardware-based CNN application produced a response approximately 18.9 times faster than the CPU-based CNN application in the inference phase while retaining the same accuracy. In terms of memory utilization and calculation units, the proposed design uses 52% fewer memory units and 68% fewer calculation units than the baseline design. While the proposed method used fewer resources, the classification success remained at 98.9%.
Analysis of User Experience on The Government Application of Indonesian Higher Education Institutional Information Systems Using Usability Method

The Ministry of Education, Culture, Research, and Technology is a ministry within the Government of Indonesia which administers affairs in the fields of preschool children's education, elementary education, secondary education, vocational education, higher education, cultural management, research, and the growth of technology. Universities that are going to open a new study program must submit a proposal to the Ministry of Education, Culture, Research, and Technology. The process of submitting this proposal is submitted through a system called SIAGA. This information system is used by universities to submit proposals and reviewers to evaluate and provide an assessment of the proposals submitted. The purpose of this research is to evaluate the usability of the SIAGA system. The evaluation is focused on from the reviewer's side because the reviewer is the main user of this system. The result of this study is performance assessment technique demonstrate that application pages are generally effective and pleased with the application system, but application is still inefficient in terms of evaluating characteristics. Improvements in this study focused on improving page layout and information navigation instructions when using the application.
Phishing Perception and Prediction

A practice known as URL phishing involves cybercriminals creating fake websites in order to lure victims and steal sensitive information. The attacker disguises themselves in an email, instant message, or text message, pose as a reliable source to persuade the recipient to open it. Once the recipient clicks the link, they realize they were deceived into clicking a potentially dangerous link. In this case, ransomware can be set up on the recipient's PC using a malware that can be used to lock it down or even worse the private data can be released. In spite of the fact that fraudulent websites often resemble the real thing, checking for warning signals alone is not enough to prevent URL phishing. A study found that around 90% of the data breach is due to phishing. The variety of phishing schemes has expanded over the years and they may now be more dangerous than ever before.