Reviewerly/exHarmony huggingface dataset:
FedComm: Understanding Communication Protocols for Edge-based Federated Learning

Federated learning (FL) trains machine learning (ML) models on devices using locally generated data and exchanges models without transferring raw data to a distant server. This exchange incurs a communication overhead and impacts the performance of FL training. There is limited understanding of how communication protocols specifically contribute to the performance of FL. Such an understanding is essential for selecting the right communication protocol when designing an FL system. This paper presents FedComm, a benchmarking methodology to quantify the impact of optimized application layer protocols, namely Message Queue Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and ZeroMQ Message Transport Protocol (ZMTP), and non-optimized application layer protocols, namely as TCP and UDP, on the performance of FL. FedComm measures the overall performance of FL in terms of communication time and accuracy under varying computational and network stress and packet loss rates. Experiments on a lab-based testbed demonstrate that TCP outperforms UDP as a non-optimized application layer protocol with higher accuracy and shorter communication times for 4G and Wi-Fi networks. optimized application layer protocols such as AMQP, MQTT, and ZMTP outperformed nonoptimized application layer protocols in most network conditions, resulting in a 2. 5x reduction in communication time compared to TCP while maintaining accuracy. The experimental results enable us to highlight a number of open research issues for further investigation. FedComm is available for download from https://github.com/qub-blesson/edComm.
A brief study on Cyber Crime, Laws and Ethics

This era is considered an era of the internet where almost everything happening around us has the internet involved in it from online shopping to online education, online transactions to online communication, and many more. In such an era there are huge chances of "Cyber Attacks", "Cyber Frauds", "Cyber Wars" and "Unauthorized accessing of any account". Such activities are considered under "Cyber Crime" and to stop these activities "Cyber Laws" are regulated. Cyber Law can be defined as a legal system that provides all the information regarding the punishments for offenses, freedom rights on subtopics of the internet, online security, and online privacy. To spread awareness about the right execution of one’s knowledge, the "Cyber Ethics" term is made which is the study of ethics made for Computers covering user controls and for what computers are exactly made to do and also how society and an individual are affected by such activities.
A Proactive Cloud Application Auto-Scaler using Reinforcement Learning

This work explores the use of reinforcement learning to design a proactive cloud resource auto-scaler that is able to predict usage across a distributed microservice application. The focus is on serving time-sensitive workloads, e.g., industrial automation, connected XRNR (eXtended Reality/Virtual Reality), etc., where each job has a deadline and there is some cost associated with missing a deadline. A simple workload model, as well as a microservice application model, is presented. A reinforcement learning agent is trained to identify workloads and predict needed utilization for identified service chains. The results are compared to standard purely reactive techniques.
Towards a Life Cycle Model for the Development of Semantic Web Applications

The semantic web is an extension of the present web in which information is given in a well-defined meaning to improve communication between computers and people. It has been discovered that developing semantic web-based apps cannot be done directly using the traditional software engineering models or the current web engineering models. As a result, it is necessary to modify the current web engineering models in order to construct a model that can be used for the methodical creation of semantic web-based applications. The paper identifies, examines, and presents a life cycle model that can be used to systematically develop semantic web applications. It also identifies and analyses various adaptations that are necessary in the current life cycle models
Performance of Java in Function-as-a-Service Computing

One of the newest forms of serverless computing is Function-as-a-Service (FaaS). FaaS provides a framework to execute modular pieces of code in response to events (e.g., clicking a link in a web application). The FaaS platform takes care of provisioning and managing servers, allowing the developers to focus on their business logic. Additionally, all resource management is event-driven, and developers are only charged for the execution time of their functions. Despite so many apparent benefits, there are some concerns regarding the performance of FaaS. Past work has shown that cold starts typically have a negative effect on response latency (e.g., the initialization could add more than 10× execution time to short Python FaaS functions). However, the magnitude of the slowdown is subject to varying from language to language. This paper investigates how containerization and cold starts impact the performance of Java FaaS functions, and compares with the findings from the prior Python study.We find that containerization overhead slows Java FaaS functions from native execution by 4.42× on average (geometrical mean), ranging from 1.69× up to 15.43×. Comparing with Python in warm containers, Java has more overhead on three of the functions, but faster on the other functions (up to 27.08× faster). The container initialization time for Java is consistently less than half that of Python. However, Java has the additional overhead due to Java Virtual Machine (JVM) warmup which contributes varying amount of latency to the execution depending on the Java function properties. Overall, Java has about 2.60×(2.65×) speedup across seven FaaS functions over Python in cold (warm) start scenarios, respectively.
Image Forgery Localization and Detection using Multiple Deep Learning Algorithm with ELA

In today’s technical world, social media may help an individual grow significantly. On the other side, we must not overlook the reality that it is also a large platform for criticism. With recent advancements, approaches for creating and manipulating multimedia information may now deliver a highly sophisticated level of realism. There has been a blurring of the line between real media and fake media in recent years. Creative arts, film production, advertising, and video gaming are among the industries that could benefit from this technology. However, it poses significant security risks. Software tools freely accessible on the internet enable anybody, with no particular abilities, to generate extremely convincing phony images and films. A dataset composed of real photographs and false images is used in this article to identify images of modifications using Deep Learning algorithms with Error Level Analysis on each image. Our experiment yielded the accuracies of 93.5%, 89.1 and 92.4% in ResNet50, Vgg16 and CNN respectively for 50 epochs.
Benchmark Dataset Creation for Intelligent Surveillance System under Dense Environment (BDISSDE)

Surveillance has always been of utmost importance but most of the time we see a person looking at CCTV footage for security or no one watching them at all. Present systems have helped to identify the criminals after the crime has been committed, but have not yet been able to take an immediate action. Up until now the researches have either manually created a benchmark dataset of classified actions or used half manual half automation approach. Despite advancement in image processing the challenges have hardly been touched in some of the scenarios. Like low visibility due to mist/fog or night, live multiple moving objects, rainy condition or even snow. The authors propose a fully automated mechanism to create a digitized benchmark dataset which would help to identify a moving object under any condition and its action as well. Preprocessing of each frame is carried out using a model which helps to extract the features and create a classified dataset which could be used to identify the action. Bag of features, SIFT, Optical flow and saliency features have been extracted and stored to prepare a benchmark dataset. The dataset has been tested for robustness using benchmark algorithms like SVM, CNN, KNN. KTH, 20 bn datasets have been used for the benchmark action videos, other videos, under different conditions, have been shot by the author. It is found that BDISSDE performs exceptionally well under various algorithms.
Hypertuned Convolutional Neural Network Residual Model Based Content Based Image Retrival System

Content-based Image Retrieval (CBIR) Framework is to find related photographs in an enormous data set. The ordinary technique is to remove a few significant qualities from the question image and recover them. Images with a comparative arrangement of qualities are recovered with high similitude scores. Framework’s prosperity and undeniable level attributes are important to close the semantic hole. In this paper two CNN models, ResNet50 and VGG16 have been considered for an enormous image order issue. Hyperparameter tuning and execution assessment is performed on the CINIC-10 dataset.
An Electronic Health Record Management System Based on Blockchain Technology

In the digital age, all the data has been moved to an electronic form and so are the electronic health records. The patients tend to have their records in various health organizations in different parts of the world. This becomes the issue of the accessibility of these records at very crucial times and to collect these fragmented parts of the records makes for an entirely new problem. For storing the patient data and to make it accessible as well, the Blockchain smart contracts can be utilized to provide a regulatory solution. With the implementation of smart contracts which will make this a self-sufficient system, any third party dependency would be easily removed. On the Blockchain all: invocation, record creation, and validation will take place. This article focuses on a non-interpolatable, self-driven, and permanent system using Blockchain, which by definition of the Blockchain make it decentralized and patient-centric, where smart contracts can be added in the future to increase data interchange potential.
An Evaluation of Sentiment Analysis Techniques, Processes and Challenges

Sentiment analysis is a technique that is occasionally used to examine information in textual form and extract thoughts from the text. The goal of sentiment analysis is to determine if users have a good or negative impression about a given topic. Online communication platforms like Twitter, Facebook, YouTube, and others have become quite important in modern society. People discuss their feelings or ideas on it. We tend to emphasise on opinion mining or feeling evaluation in this review paper, which is a field of web data mining and machine learning. The evaluation of several methodologies has been done in this study, which presents the machine learning and natural language processing (NLP) methods employed by earlier academics in sentiment analysis.
Sentiment Analysis on Amazon Dataset using Transfer learning

In this paper, we have proposed several Machine Learning, Deep Learning and Transfer Learning model which is totally based upon new cutting-edge technology i.e., Sentiment analysis. With the continuous growth in the technology field Artificial Intelligence is on its way to change the motive of technology. So, this project is not only applicable for the above-mentioned dataset but it’s applicable for every dataset which consists of customer review. We all know that after the corona wave every market is switching into Online mode. There are a numerous number of website or company that are using customer sentiment technique to get the insight about their thought for particular product. In our project we use Classification technique to identify whether the sentiment for the particular product is positive or negative. So, based upon the existing dataset we follow the proper NLP pipeline so that we can extract all the features and get a clean dataset. ML and DL has huge algorithm support so using different classification algorithm we apply the NLP vectorization technique. Above all we also apply transfer learning model that is built on the top of Deep Learning model, which enhance the accuracy of the model because it’s developed on pre-built model. The ultimate goal for a developer is to get best accuracy, precision, recall and F1 Score. So, judging on the above-mentioned parameter and on behalf of industrial sight we compare different model and try to prove which model is the best model for this particular task.
A Real-Time Vehicle Indoor Positioning Algorithm Based on Multi-Camera Perception

In warehouses, it is very important to obtain positions of vehicles. However, existing algorithms usually use a single camera to detect positions of vehicles, which has many limitations. First, if some degree of occlusion occurs to the target vehicle, it might not be detected. Second, a single camera’s view is limited. Therefore, we propose a real-time vehicle indoor positioning algorithm based on multi-camera perception. Our work is as follows: 1) Compare performance differences of various types of object tracking algorithms and make a tradeoff between speed and precision to find a tracking algorithm that is real-time and precise. Then combine the tracking algorithm with vehicle detection. 2) Collaborate between multiple cameras, including multi-camera positioning and cross-camera tracking. 3) Use self-built multi-camera vehicle indoor positioning system for test and analysis. Experimental results show that our algorithm has good performance.
A comparative review analysis for load balancing techniques in Cloud Computing using Machine Learning

Load balancing (LB) is a task to manage the performance and efficiency of multi-attribute, multi-variant cloud computing (CC) resources. CC system is more effective when its whole resources are employed in best probable manner and maintaining its load in proper accessing of its resources. Load includes the CPU load, network load, data traffic load, client requests load etc. So, load balancing process includes the management of all these loads as per the availability of resources. This paper deals with the multiple load balancing techniques and present them in hierarchical form and analysis them in performance and efficiency dynamics. This paper also discusses the comparative analysis of performance of existing techniques related to LB in CC using Machine learning (ML). A brief about hybrid methods will also be discussed through this paper. In conclusion part on the basis of analysis done through this study paper also suggests some new insights for LB in CC.
Improved Security and Privacy in Cloud Data Security and Privacy: Measures and Attacks

The advantages of the cloud environment for data processing and sharing are utilized by millions of people worldwide. A cloud system must inevitably provide data security and privacy. Users' widespread use and sharing of information creates security gaps. This study aims to discuss the cloud environment, its benefits, difficulties, and upcoming research trends pertaining to safe data processing and exchange. The widespread issue is caused by the increased adoption of cloud computing by several enterprises. As a result, utilizing any device to load and receive data from the cloud providers' facilities raises various security and privacy risks, such as data modification, data loss, and theft. Unauthorized access by insiders is one of the significant problems that might develop. Although there are various ways to prevent cloud administrators from gaining illegal access, such methods haven't been successful in keeping them from gaining access to client data in the cloud.The degree of protection a system may offer to the CIA triad—a paradigm that includes the information security qualities confidentiality, integrity, and availability—is how information security is assessed. In this paper, we have analyzed such scenarios. This study analysis provided dangers to cloud data security, cloud assaults, and found vulnerabilities for several factors affecting cloud computing.
Virtual Hands: Real Time Keyboard, Desktop &amp; Application Navigation using Gestures

In computer system keyboard is the most prominent input medium of all time. But lately, human community is living in an era of global pandemic being afraid of suffering from Coronavirus (Covid-19) and hence each and every person avoids touching anything. This is because of the fear of contracting this contagious virus and their mutants. So, to mitigate this issue, we present a method "webcam based virtual keyboard interface" to interact with a computer system. The code of this method is written using pre- built modules like OpenCV, MediaPipe, PyVDA, Win32API, etc. and Python 3.9. This approach uses matching the index finger and middle finger on the specific key. After that the virtual desktop switching mechanism is done by PyVDA. The PyttSX3 library plays the sound whenever any key is pressed or when a desktop switch is initiated, corresponding to the key pressed or the desktop switched. In this approach no additional hardware device other than the webcam, that is already available in the system, is required. This approach is also useful for those persons who wants the access the system, even when their hands are dirty.
Evolution and comparative analysis of different Cloud Access Security Brokers in current era

Almost every organization in the world is using cloud, its services and hosting their application on cloud. With time Cloud computing will play a more important role in our life. For enhancing user experience on an application maximum organization uses cloud services. Organization also chooses to deploy their own Cloud services and whether the service is for their internal cloud or for their user the most important things are services to user and security. In this paper we will discuss Cloud Access Security Broker (CASB), how this technology evolves and how it works. Beside that the other focus will be on Data Leakage and how Machine Learning and Deep Learning can help in data leakage issue. Also, how CASB can be used in Data Leakage issues.
Review on Digital Watermarking Techniques and Its Retrieval

The goal of the current review is to explore how to protect users' security and privacy when using digital watermarking, as well as to cover the research component. A digital watermark is a signal that transmits data about the creator or supplier of the media. The placement of a digital watermark on digital media makes it visible to computers but not the human eye. In digital watermarking embedded the watermark in original images or audio-video data by using embedding algorithm and then extract the watermark image from original image by using extracting algorithm. In this more than 40 research papers have been reviewed in the period of 2000 to 2022 and found three issues namely Perceptual Transparency, Payload of the Watermark and Robustness. These challenges led to the discovery of the findings, strengths, weaknesses, and solutions approaches.
A Study on Multi-pose Face Recognition Based on Improved Local Convolutional Neural Networks

In view of the problem of attitude change affects face recognition rate, a modified local convolutional neural network is proposed. Firstly, a new convolutional network based on Resnet50 is built to maximize the extraction of image features, and then the data transmission rate is used to reduce, and the number of sampling points to prevent the distortion caused by aliasing. Finally, the method of mapping posture correction is used to obtain the local texture information of the positive face picture obtained in the top-level reconstruction. The experimental results show that the algorithm has significantly improved the recognition accuracy by 1.13% on the face data set LFW compared with the improvement, which makes the face frontal recognition more flexible and accurate.
UNEG: A Description-Enhanced Graph-based News Recommendation Method

Modeling news and users accurately became more and more crucial in personalized news recommendation. Exploring rich relational information with graph-based representation learning methods is challenging. Existing graph-based methods learn the representations of news items and user interests with external knowledge graphs or user-news bipartite graphs. However, they rarely link two news through the entities they refer to in common and fail to consider their correlations. In this paper, we propose a novel User-News-Entity Graph, named UNEG, which associates news items with their mentioned entities and users with the news articles they browsed. In our practice, we use category-aware graph attention networks to model representations of high-order relationships between nodes in UNEG. Furthermore, we introduce entity descriptions as auxiliary information to enrich the initial representations of entity nodes, which provide additional text information on entity labels. We conduct experiments on a real-world dataset, and the results validate the effectiveness of our approach.
HDR image identification network based on edge difference

Due to its ability to represent a greater range of colors than conventional low dynamic range (LDR) imaging, high dynamic range (HDR) imaging has been widely applied in the multimedia community. However, to our best knowledge, forensic studies tailored for the authenticity recognition of HDR images are lacking. This paper proposes a network called SAA-Net, which aims to differentiate HDR images from LDR ones based on a spatial rich model (SRM) edge enhancement block and the dual attention mechanism. First, we leverage the edge enhancement block to enhance the subtle jitter characteristics of HDR images. Next, with the double attention mechanism, the network is guided to pay attention to the abnormal information and location information of the strong contrast edge of the image. The proposed network shows superior performance over the state-of-the-art in identifying both real HDR images and synthetic HDR ones.
Research on Education Cost Prediction Based on Improved BP Neural Network

In order to better predict the education cost and improve the utilization rate of university funds, an education cost prediction model based on K-means clustering improved PSO-BP neural network was proposed. Firstly, aiming at the problem that BP neural network is prone to trap into local optimization and its convergence speed is slow, PSO was proposed to optimize the parameters of BP. Considering the limitations of PSO, the K-means clustering algorithm was added to cluster particle swarms, thereby enriching the diversity of the population. Finally, the improved particle swarm optimization algorithm was used for the education cost prediction of BP neural network. The results showed that the optimization performance of the improved particle swarm optimization algorithm is more stable and the convergence speed is faster. At the same time, in terms of prediction accuracy, the education cost prediction accuracy of the improved PSO-BP is higher.
Big Data Storage Index Mechanism Based on Hierarchical Indexing and Concurrent Updating

In modern war, it is of great practical significance for the military powers to improve the command efficiency of commanders on the battlefield, it is necessary to use real-time, efficient, and reliable big data storage index method. The application of Hibase-based hierarchical index technology and hot data cache replacement technology can effectively improve the query and storage efficiency of big data, and solve the shortcoming of non-primary key data requiring full table scan. ZooKeeper distributed read-write lock can effectively solve the problem of data consistency caused by frequent concurrent updating of big data information. Finally, the experiment proves that Hive’s query efficiency is improved by nearly 12%.