Reviewerly/exHarmony huggingface dataset:
A Comprehensive Analysis of Anti-Image Manipulation Techniques: Advancements and Challenges

Every individual relies on technology either directly or indirectly. Living in an Information Era means that even one’s birthday is known to others who haven’t been seen for years, via social media. This is possible because of the data that the social media extracts from the user and keeping it securely in their database as information. Even with the assurance given by the media companies about the information they store, many of us still refuse to share the personal data online because of the factors like Data Breaches, Data phishing, etc. Even if personal data access is granted, the images posted on social media undergo unsightly alterations through various AI modification techniques employed by certain individuals. With these image alterations ranging from subtle enhancements to sophisticated forgeries, the need for robust solutions to detect and mitigate these manipulations has become very crucial. This research study is based on a novel approach to traverse through various approaches in preventing images getting altered through AI.
A Survey on Sharing Cloud Data Securely with Encrypted Indexing and User Identity Verification

With the increasing popularity of cloud storage services, securing external information and ensuring the privacy of data stored on the cloud is becoming a crucial task. The use of encryption techniques, where data is encrypted before it is sent to the cloud, is a solution to this problem. On the other hand, traditional encryption approaches make it more difficult to search and extract specific information from the encrypted data because they require data decryption initially. This drawback limits the effectiveness of cloud storage alternatives. To get over this problem, this study proposes an innovative method for keyword search on encrypted data in cloud storage that use the Advanced Encryption Standard (AES) encryption strategy. The AES algorithm is a symmetric encryption solution that is well-known for its dependability and effectiveness. The proposed solution ensures the privacy of data saved in the cloud by leveraging AES encryption, which also allows for effective keyword-based searches without the need for decryption. The proposed technique combines index systems and various cryptographic techniques, including AES encryption, to enable strong data search operations on the encrypted records. During the encryption process, a secure index is established and this allows for the effective retrieval of encrypted documents connected with specific keywords. Since only the top legal clients have the decryption keys required to access the plaintext records, the encrypted data is kept private. The data proprietor may set access permissions for the period of data outsourcing based on their consumer identification. To access information saved in the cloud, customers must have a decryption key and a unique identity. Here, the data owner can also specify a time limit for cloud data access. The findings of this research study suggest that employing the AES encryption method to execute keyword searches on encrypted data stored in a cloud server can accomplish a trade-off between information privacy and search capabilities. The solution also ensures that the private information is protected from unauthorized access and provide a reliable way to store and retrieve data from the cloud.
An Integration of Indian Philosophy and Machine Learning for Human-like Cognition

This paper introduces a novel framework that melds the profound tenets of Indian philosophy with AI and ML. Our goal is to create models that replicate human cognition, paving the way for robots that think like us. Key concepts from Indian philosophy, including ”buddhi,” ”manas,” ”chit,” and ”ahamkar,” are woven into the framework, enhancing AI’s ability to understand and interact with the world in a more human-like way. The core of the framework consists of ”Buddhi” and ”Manas.” Buddhi, a discriminative AI component, adeptly navigates sensory data, making decisions aligned with human discernment. Complementing it, ”Manas” is a generative core, capable of crafting narratives and generating creative solutions. This synergy creates an iterative loop that encapsulates human thought, enabling machines to think, visualize, and even dream. In addition to advancing AI and ML research, we conducted an experiment using a modified News Aggregator dataset from the UCI repository. The experiment focused on word prediction, and our model, infused with Indian philosophy, outperformed RNN and LSTM models over 30 epochs. This framework has the potential to revolutionize AI and create more human-like machines, ushering in a new era of AI and robotics.
Representative Feature Learning and Task Decoupling for Few-Shot Object Detection

In few-shot object detection (FSOD), transfer-learning approaches are adopted extensively to transfer knowledge from base classes with numerous annotations to novel classes with finite annotations. However, when training with only a small amount of images, two momentous problems arise: 1. The feature distributions of few-shot samples are easily disturbed by noise and randomness, which may cause biased distributions and lead to serious overfitting. 2. Inconsistent preference of features in classification and regression tasks makes it difficult to optimize the model to match the two tasks simultaneously, resulting in a dramatic performance drop. To address the problems, we propose Representative Feature Learning and Task Decoupling (RFLTD). On the one hand, the representative feature learning module captures representative features and calibrates the distribution by effectively distracting class-marginal and class-irrelevant regions, which reduces model overfitting. On the other hand, the Task Decoupling module separates and emphasizes the ROI-Align feature at the channel level, which improves both the classification and regression performance. RFLTD improves the SOTAs with significant margins on PASCAL VOC and MS COCO datasets, demonstrating the effectiveness of the proposed approach.
Unlocking Efficiency - Multidimensional Cost Optimization Strategies for Cloud Infrastructure in Small and Medium-Sized Organizations

Cloud computing has become integral to the operations of small and medium organizations, driving cost-efficiency, scalability, and flexibility. However, the dynamic and multidimensional nature of workloads in cloud environments poses significant challenges in cost control. Traditional one-dimensional optimization strategies, which focus on individual resource dimensions like CPU or storage, often fall short in addressing these complexities. This research study presents a comprehensive study on “Cost Optimization in Cloud Infrastructure” tailored to the needs of small and medium-sized organizations. It outlines a multidimensional resource optimization framework that encompasses compute, storage, and network resources, providing a holistic approach to cost management. Two real-world case studies from diverse industry domains showcase the practicality of the proposed strategies. In Case Study 1, a small e-commerce startup achieved a 30% reduction in cloud infrastructure costs while improving performance through predictive analytics, storage tiering, and CDN integration. In Case Study 2, a medium-sized healthcare provider realized a 25% cost reduction while ensuring HIPAA compliance and enhanced resource utilization. The evaluation and results section quantifies the cost reduction percentages and resource utilization efficiency achieved through multidimensional optimization, comparing them favourably against one-dimensional approaches. The paper concludes with practical implications, recommendations for organizations and cloud service providers, and guidance for future-proofing cost optimization strategies in an ever-evolving cloud landscape.
A Comparative Analysis of LSB &amp; DCT Based Steganographic Techniques: Confidentiality, Contemporary State, and Future Challenges

In order to maintain anonymity and security, the steganography is the technique of cloaking confidential data within what seems like harmless digital material. Several steganographic methods have been established devised over time, but those centered around the discrete cosine transformation (DCT) and the least significant bit (LSB) have drawn the most consideration. In this study, two common steganographic methods are compared and contrasted with an emphasis on the secrecy they can keep, the usage they are now receiving, and any potential difficulties in the future. As an alternative, the DCT-based method uses the frequency domain properties of cover media to obfuscate hidden information. Since it spreads the concealed information across several frequency coefficients, it provides greater security than LSB-based techniques. The resilience and imperceptibility of the concealed data are improved by a variety of DCT-based algorithms, such as the modified quantization and matrix encoding approaches, which we explore in detail. We also give a general summary of both approaches' current state in terms of their application, constraints, and areas in which they may be used. We evaluate the benefits and drawbacks of each approach, considering elements like payload size, computing difficulty, and detection resistance.
Detecting Insider Threats Using VADER and NAÏVE BAYS Algorithms

Nowadays larger organizations are dealing with enormous amount of emerging Security Big Data, with millions of security events being collected, processed, analyzed and responded every day, that makes it humanly impossible to detect complex zero-day cyber and insider threats. Detecting insider threats is very challenging as it deals with detection of hidden negative indicators related to the disgruntle employees with approved and authorized access to all enterprise assets. Therefore, in order to discover such complex insider threats, Natural Language Processing (NLP) & Machine Learning (ML) Algorithms can play a key role to supercharge detection and analysis of employee sentiments from textual security datasets; based on general mood, attitude, and opinions of users. This research paper analyzing and comparing two of the most common Sentiment Analysis algorithms, VADER and Naive Bayes; based on their accurate detection, real-time performance, complexity, and cost.
An Innovative Method for Election Prediction using Hybrid A-BiCNN-RNN Approach

Sentiment, volumetric, and social network analyses, as well as other methods, are examined for their ability to predict key outcomes using data collected from social media. Different points of view are essential for making significant discoveries. Social media have be en used by individuals all over the world to communicate and share ideas for decades. Sentiment analysis, often known as opinion mining, is a technique used to glean insights about how the public feels and thinks. By gauging how people feel about a candidate on social media, they can utilize sentiment analysis to predict who will win an upcoming election. There are three main steps in the proposed approach, and they are preprocessing, feature extraction, and model training. Negation handling often requires preprocessing. Natural Language Processing makes use of feature extraction. Following the feature selection process, the models are trained using BiCNN-RNN. The proposed method is superior to the widely used BiCNN and RNN methods.
Machine Learning Algorithm for Sentiment Analysis on Tweets

Since there is so much user-generated content and there is a need to derive valuable insights from it, sentiment analysis on Twitter has become a hot study topic. This study introduces a sentiment analysis machine learning system created especially for Twitter tweets. The suggested approach uses machine learning techniques to automatically categorize tweets into categories with positive, negative, or neutral sentiment. The preparation phase of the algorithm first cleans and normalizes the tweets by removing noisy elements such special characters, URLs, and hashtags. The tweets are then represented as numerical feature vectors using techniques for feature extraction. To extract the semantic and contextual data included in the tweets, a variety of lexical and syntactic features are used, such as bag-of-words, n-grams, and part-of-speech tags. Next, a supervised learning approach is utilized to train a sentiment classifier using a training dataset of labeled tweets. In order to find the best classifier for Twitter sentiment analysis, experiments are conducted with a number of well-known machine learning techniques, including Random Forest. Methods for picking the most informative features are being investigated to enhance the performance of the classifier's
Enhancing Surveillance Systems with YOLO Algorithm for Real-Time Object Detection and Tracking

A Virtually Impaired Person (VIP) is unable to identify objects when they cannot recognize where the object is placed. The researchers are working on it to enhance object detection and help VIP. The challenges faced by researchers are performing detection under low-resolution images, insufficient sensors, portability, and cost. Making a compact device and alerting them is required. By considering the above-mentioned difficulties, an innovative solution is described in this research work. The growth of image processing and deep learning techniques has simplified the complexity of processing data and provided accurate results within a limited time period. The suggested technique presented is a deep learning algorithm called the YOLO algorithm, which is combined with the web to predict objects accurately. For this approach, a dataset with a total of 500 images was chosen and trained. The proposed classifier result is satisfactory, and it achieved an overall accuracy of 94% Furthermore, this proposed technique provides enough output in comparison with several other machine learning and image processing algorithms.
Breaking Barriers in Text Analysis: Leveraging Lightweight OCR and Innovative Technologies for Efficient Text Analysis

Text detection and classification play vital roles in various applications such as document analysis, scene text recognition, and image-based information retrieval. In recent years, deep learning approaches combined with Optical Character Recognition (OCR) engines have shown promising results in text-related tasks. This research study investigates text detection and classification using popular OCR libraries, including Tesseract, Keras, Easy OCR, and Paddle OCR A comprehensive evaluation is conducted on a diverse dataset encompassing different text types, languages, and image qualities. Each OCR model’s performance is measured in terms of accuracy, precision, recall, and F1 score. The paper provides sample inputs and corresponding outputs for each model, allowing users to mention the strengths and limitations of each OCR approach. This study aims to assist the practitioners and researchers in selecting the most suitable OCR model for their specific text-related applications. The research findings demonstrate the potential and versatility of deep learning-based OCR solutions for text detection and classification tasks, paving the way for further advancements in this domain.
Ethical Hacking and Demonstrating Network Exploits using Wireshark

Internet traffic analysis is a practice that records information about user behavior on the network. In order to determine how many people access the internet and consume bandwidth, this research seeks to collect data regarding traffic outcomes in a graphical format. Researchers in this study also recorded the peak internet usage hours. The study’s results were obtained using the packet sniffing approach. Data packets from the http protocol application can be filtered by researchers. Since then, user activity on the Internet has become more dominating in terms of locating and downloading sites. Wireshark is the programme utilized; it has several capabilities that are quite beneficial and simple to use.
Algorithm for High-Dimensional Data Based on Comprehensible Deep Progressive Fuzzy Network

Aiming at the “dimension explosion” challenge of fuzzy systems in high-dimensional data processing, this paper proposes an algorithm of DPFS (Deep progressive fuzzy system), whose structure is the connection of many low-dimensional fuzzy systems. Through the comparison of experiments with different data sets, it is found that: 1. the algorithm retains the advantages of strong interpretability of the fuzzy system. 2. the DPFS algorithm, characterized by its progressive structure, exhibits a superiority of 7.9% over machine learning algorithms like BP and RBF. Moreover, it outperforms fuzzy system algorithms such as DNFS by more than 31.5% in terms of accuracy on the BF dataset. The research demonstrates that DPFS serves as an effective fuzzy system inference algorithm capable of effectively handling high-dimensional data, showcasing robust comprehensive capabilities.
Research on the Quality Improvement of Art Design Professional Course Resources Based on Adaptive Recommendation Algorithm in Smart Environment

To further improve the quality of learning resources obtained by art and design students during online learning, this paper proposes an adaptive learning resource recommendation method based on graph embedding and deep reinforcement learning. Among them, the recommendation method is constructed in the form of knowledge graph, and the recommendation quality is further improved by constructing the learner knowledge tracking model. The experimental results show that compared with other types of knowledge tracking models, the improved learner knowledge tracking model has higher prediction precision, and the evaluation index AUC reaches 0.859. Compared with other recommendation methods, the designed adaptive learning resource recommendation method based on graph embedding and deep reinforcement learning can recommend learning resources with higher quality, and its MAE and RMSE indexes are 0.799 and 1.118. Therefore, the designed learning resource recommendation method has good recommendation effect, and it can be applied to the quality improvement of art design professional course resources, which has high feasibility.
Dynamic Obstacle Avoidance Path Planning of Unmanned Vehicle Based on Improved APF

Aiming at the issue that the artificial potential field(APF) does not consider the velocity information of dynamic obstacles, which may lead to the issue that the obstacle avoidance path is too long when the speed of the obstacle is close to the unmanned vehicles, this paper designs a new artificial repulsion potential field function, which introduces the velocity information of the obstacle, so that the unmanned vehicle is more inclined to avoid from the opposite direction of the velocity of the obstacle. In addition, a distance factor is introduced on the basis of the new repulsion potential field function, to overcome the issue of unreachable targets in traditional algorithms. Finally, the simulation shows that the improved algorithm makes the unmanned vehicle choose a more reasonable route when avoiding dynamic obstacles, and achieve good dynamic obstacle avoidance effect when there are multiple different obstacles.
Sorting an Array with Backtrack &amp; Comparison with Bubble Sort

An algorithm to address the shortcoming of Bubble Sort.The short coming of bubble sort is that it is inefficient for large dataset and provides more execution time. The backtracking variable is used as a decision parameter. If the backtracking variable is greater than or equal to zero then shifting is used. The first element will be pointed by a backtrack variable, the second element by an array [i], and the third element by an array $[i+1]$, and two cases of shifting are applied. If the element to be shifted lies in the middle of the backtrack and array of $[i+1]$ and if the element to be shifted lies ahead of both array of $[i]$ and $[i+1]$. After completion of each pass, the two largest elements are placed at the end of an array in sorted order. ECLIPSE has been used for the code of sorting an array with Backtrack implemented in Java and MATLAB for analysis of the number of loops with respect to the number of elements for both algorithms. The result after implementing the algorithm proves that the backtracking method is much more efficient than bubble sort as the total number of iterations, execution time, space, and time complexity is much less as compared to bubble sort with random input sequences.
An Empirical Study in Transaction Data Augmentation Based on Fuzzy Set Theory for Association Rule Mining

Frequent item-sets are sparse in real life transaction datasets, and data prerecession including data augmentation skills are necessary in order to carry out efficient mining. For example, the raw data (e.g. Power Mart transaction data set) contained the data field of price (Numerical), where a keyboard of price USD ${\$}$29.1 was mapped to price level of low to middle (e.g. “L2M” = 1), and product type of “KB”. Adopting the mapping for association rule mining, the top frequent keyboards of low to middle price level generated had a very low support = 0.12. In this work, we adopted the proposed data augmentation to map a numerical price to fuzzy price levels, where the keyboard of price USD ${\$}$29.1 was described by the different price levels at the interval [0, 1] (e.g. “LOW”= 0.0, “L2M” = 1.0, “MID” = 0.2, “M2H” = 0.0, and “HIG” =0.0). The advantages of making use of fuzzy set theory lied in two aspects: firstly the ambiguity of the vague boundaries among different price levels were handled, and secondly the experimental results showed the effectiveness of the fuzzy logic (e.g. much larger sets of frequent item-sets were generated, and more association rules could be mined in data analysis). The experimental results showed that the no. of frequent item sets and no. of association rules both increased greatly. The trapezoidal and linear membership functions were chosen, based on market research, objective preference, as well as the Power Mart transaction data set.
Proof of Elapsed Storage and Time - Data Storage as a Service:Secure Multi-Tenant Database Management System

Blockchain (BC) based cloud computing is an innovative approach that combines the principles of BC technology with traditional cloud computing infrastructure to offer enhanced security, transparency, and decentralization in cloud services. A cutting-edge solution in the realm of Multitenant data storage, utilizing BC technology to establish a highly secure and tamper-proof environment for managing multi-tenant data is proposed in this research. Multichain, an open-source private BC platform, is introduced offering users unprecedented flexibility and control. The Data Storage as a Service (DSAAS) application is presented as a versatile platform that harnesses BC technology for secure data storage and sharing among participants in the BC network. The Proof of Elapsed Storage and Time (PoEST) consensus mechanism, is a unique algorithm employed primarily in permissioned BC networks, combining elements of Proof of Elapsed Time (PoET) and Proof of Storage (PoSt) to ensure robust data security and consensus in the system. This amalgamation of technologies showcases the potential for enhanced data security and management in the evolving landscape of the Internet of Things (IoT) and BC applications. The performance of the proposed method shows a good performance of 86.93% Genuine User Rate, 1.05ms Responsiveness, and 78.05ms Time Complexity.