Reviewerly/exHarmony huggingface dataset:
Smart Model For Career Guidance Using Hybrid Deep Learning Technique

Career guidance is required mostly for the degree students as they have not clear vision about it. Most students don't know what kind of work they want to do or what degree would be best for them. Considering the user's skills, interests, and preferences, the system will suggest different career paths. The results of this assignment can help students zero in on a subject area that fits their knowledge and interests the best. The Smart Career Guidance System using chatbot, is built using recurrent neural network (RNN) and Long Short Term Memory (LSTM). To proposed Smart Model for Career Guidance Using Hybrid Deep Learning Techniques. Our proposed hybrid deep learning algorithm work on the concepts of RNN and LSTM and Natural language processing (NLP).
A compressive sensing encryption scheme for dual color images based on discrete memristor map and Rubik’s cube scramble

The most common in practical image encryption are multiple color images, however, less attention is paid to inter-layer correlation broken in existing color image encryption algorithms. In this paper, a 2-D compressive sensing dual color images encryption algorithm based on a discrete memristor hyper-chaotic map and Rubik's cube scramble is proposed. First, we propose a new discrete memristor-Ikeda map (DM-Ikeda), the analysis result shows the DM-Ikeda map has extremely high complexity and hyper-chaotic dynamical behavior. Second, to facilitate ciphertext transmission, the images are compressed and encrypted simultaneously by DM-Ikeda map and 2D compressive sensing. Then, we design a new rubik's cube scramble algorithm, which combines the layers of two images into a cube that can be expanded in different orders, and swaps the pixels of rows, columns or faces of each image by rotating the cube to achieve image scramble. This algorithm can break the correlation not only of pixels within layers of two images but also of pixels between different layers of images. Finally, two images are further diffused with the use of hyper-chaotic sequences. The algorithm can achieve both compression and encryption of dual color images. Experimental results indicate that, our scheme has excellent compression, and can break the correlation between different images and layers, which is excellent resistance to statistical attacks and has high security.
The Current State of Software Engineering Employing Methods Derived from Artificial Intelligence and Outstanding Challenges

Although AI has become a buzzword for self-organizing IT applications; its significance to software engineering has not been rigorously examined. To evaluate the current state of advancement, the potential for future development and the risks of AI application to software engineering, this study combines a systematic review of previous research in the field with five qualitative interviews with software developers who use or want to use AI tools in their daily work routines. Research organises findings about the software improvement life sequence. Results show that AI's greatest achievements and potential lie in three areas: a) the systematic evaluation of these data in neural networks; b) the structured analysis of big data pools to discover patterns and novel information clusters; and c) the automated automation of lengthy routine jobs in software development and testing using algorithms, such as for debugging and documentation. As a result, AI shortens the development cycle, reduces expenses, and increases productivity. AI has thus far been dependent on human-made structures and has primarily been reproductive; nonetheless, the automation of software engineering jobs has a major advantage: the creative capacity of human developers is multiplied when using AI tools effectively.
A Survey on FHE Acceleration

Fully homomorphic Encryption (FHE) allows computation on encrypted data without any decryption. However, the time of operation on encrypted data is far from the process on plain data. Hence, the performance gap is still a big concern regarding FHE. Researchers proposed several approaches to speed up operation on FHE at the algorithm level by introducing different schemes with different features and at the hardware level using hardware accelerators and memory optimization approaches. In this paper, we review years of research on FHE hardware acceleration and discuss future directions that could be used to speed up the operation of the different schemes of FHE.
Constellation Founded Fuzzy And Bee Colony Optimization For Route Optimization In Mobile Wireless Sensor Network

Numerous service parameters are impacted by the high node power in Mobile Wireless Sensor Networks (MWSN). There is a loss of energy due to the fact that the sensor nodes were constructed with a certain quantity of power and must expend some of that power with each data transmission. However, the nodes are able to complete any data transmission through cooperative communication. Although there are a variety of routing strategies that can be applied to mobile wireless sensor networks, cluster-based routing has been shown to have the most effect on meeting performance goals. If a node in a network group wants to communicate with a node that isn't part of the group directly, it uses a gateway to do so. New methods for optimizing bee colonies are utilised to help mobile wireless sensor networks adopt optimal routing. Clustering mobile sink nodes, which collect data from the network's sensor nodes, have been taken into account for wireless sensor networks. The sensor nodes provide their most current status updates and can be found in close proximity to one another. After that, a fuzzy logic-based inter-cluster routing mechanism is created so that cluster participants can send and receive messages.
Feature Extraction Based Domain Kickout Method For Fractal Image Compression

In fractal image compression, similarities between the picture's self-similar components, often known as fractals, are exploited in order to create a novel method of image representation. In order to depict these fractals, it makes use of affine transformations. The significant amount of time needed for the encoding process is this method's most significant drawback. In this paper, we propose a fast fractal image compression system. This system works by reducing the effective number of domain images by using feature extraction, which in turn reduces the computational overheads and ultimately results in a reduction in the amount of time it takes to encode the image.
FB-RO-Offense – A Romanian Dataset and Baseline Models for Detecting Offensive Language in Facebook Comments

In the past decade, social media platforms gained a lot of popularity amongst people all around the globe, some of them seizing this opportunity to proliferate offensive language and hate speech. In addition, platforms that choose not to consider text filtering techniques are being exploited by users who tend to use offensive and abusive language. This paper presents the creation and annotation of a novel Romanian language corpus for offensive language detection, FB-RO-Offense, an offensive speech dataset containing 4,455 organic generated comments from Facebook live broadcasts annotated not only for coarse-grained binary detection tasks but also fine-grained, based on the degree of the offense. We describe the data collection process and the annotation procedure and analyze the content of the corpus. Additionally, we present the results of automatic classification processes using state-of-the-art classification processes and establish a strong baseline for this new dataset including SVM, BERT-based, and CNN architectures, with results that show an F1-score of 0.83 for a four-way classification and an F1-score of 0.90 for the binary classification.
Comparison of OLS and NLS to fit Transformation-Interaction-Rational expressions

Transformation-Interaction-Rational is a representation for Symbolic Regression created with the intent to constrain the search space of mathematical expressions with only simple models. In short, this representation is a rational of two linear models with transformed variables. Even though the representation makes the model nonlinear w.r.t. its numerical parameters, it is possible to rewrite the expression such that the parameters become linear and, thus, can be adjusted using the Ordinary Least Squares method, that guarantees the global optima solution. But, when we are working with a noisy data set, the reparametrization procedure may skew the noise distribution leading to an incorrect value for the numerical parameters. In this work, we test and compare the use of Ordinary Least Square and a non-linear optimization in simulated benchmarks with the presence of noise. The results show evidence that OLS is robust to noise and always returns a similar solution to the non-linear optimization algorithm.
An Effective Analysis of Proficient Two Level Security Contraptions For Loading Data In Cloud

One of the cornerstones of the future generation of computing is cloud computing, a form of internet-based computing. It allows for the instantaneous allocation of Internet-accessible resources whenever they are needed. Services such as software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are made available to people and businesses through cloud computing. By giving customers a low-cost, scalable, and financially-independent place to store their data, cloud storage services have seen rapid revenue growth in recent years. An interoperable cloud computing system is built on open architectures and interfaces that support both private and public cloud services. Many businesses today are making the switch to the cloud because of the numerous advantages it offers in terms of data storage, retrieval, management, and availability. While cloud computing is receiving a lot of attention, it is imperative that security problems be addressed first. This study proposes a crypto-stegno mechanism in the cloud as a solution to cloud security problems. This study proposes a practical method for ensuring data privacy and security in cloud storage. In order to obtain their own public and private key, users must first register their information in the cloud and come up with a unique username and password. Cryptography, a means of concealing information and sending information in an unreadable format, is employed in the next level of security. Secure data transmission and storage on the web, electronic commerce, digital media privacy, and ATM transfer are all dependent on cryptography to some degree. Repudiation, integrity, authenticity, and confidentiality are all possible using today's cryptographic methods. Signcryption is a lightweight cryptographic system that combines encryption and signatures in a logical way to achieve higher security with less computing time.
Image Caption: Explaining Pictures by Text using Deep Learning

In this paper, we propose a novel approach to generate image captions using deep learning techniques. Our model employs a pre-trained visual language model and matches picture label information to generate familiar picture captions that can depict novel articles. We also use a range of pre-training techniques for learning cross-modal representations on picture text sets, which contribute to the model’s ability to predict picture text semantic arrangements. We demonstrate that our model outperforms state-of-the-art models on the Flicker 8K dataset. We also employ a combination of long short-term memory (LSTM) and Convolutional Neural Networks (CNNs) layers to extract image features, which help the model understand and highlight the relationship between image features and caption semantics. Our results suggest that our approach can provide a more effective and resource-efficient solution for generating image captions. Overall, this paper presents a comprehensive investigation into the use of deep learning techniques for image caption generation.
A web version of Tarski, a system for computing with Tarski formulas and semialgebraic sets

We report on successfully porting and making available online Tarski, a system for computing with Tarski formulas (Boolean combinations of polynomial sign conditions over the real numbers). Tarski provides operations like formula simplification and quantifier elimination, and is able to read and write SMT-LIB syntax. The web version of Tarski was created by using the Emscripten C++ to JavaScript/WebAssembly compiler. A terminal-based interpreter is provided via jquery.terminal. Tarski is also available as a JavaScript library. We report on an experimental use in the dynamic geometry software GeoGebra.
Camera and LiDAR Fusion for Robust 3D Person Detection in Indoor Environments

In order to guarantee a harmonious coexistence of robots and humans, it is vital for the former to be able to navigate the environment in a safe manner, taking into consideration the people that populate it. This requires the robot to be capable of identifying where the surrounding people are, through adequate processing of the sensory data acquired. In this paper we present a 3D person detection system for indoor settings which fuses RGB and LiDAR data, simultaneously using the semantic information available from the camera and the precise and long-range data acquired by the LiDAR. The proposed approach utilizes SVM with various feature vectors to classify the point clusters, and it also introduces planarity as another feature, improving the overall detection performance considerably.
Design of Quantum Computer Antivirus

The development of quantum computers has been advancing rapidly in recent years. In addition to researchers and companies building bigger and bigger machines, these computers are already being actively connected to the internet and offered as cloud-based quantum computer services. As quantum computers become more widely accessible, potentially malicious users could try to execute their code on the machines to leak information from other users, to interfere with or manipulate results of other users, or to reverse engineer the underlying quantum computer architecture and its intellectual property, for example. To analyze such new security threats to cloud-based quantum computers, this work first proposes and explores different types of quantum computer viruses. This work shows that quantum viruses can impact outcomes of Grover’s search algorithm or machine learning classification algorithms running on quantum computers, for example. The work then proposes a first of its kind quantum computer antivirus as a new means of protecting the expensive and fragile quantum computer hardware from quantum computer viruses. The antivirus can analyze quantum computer programs, also called circuits, and detect possibly malicious ones before they execute on quantum computer hardware. As a compile-time technique, it does not introduce any new overhead at run-time of the quantum computer.
Robust Intelligent Posture Estimation for an AI Gym Trainer using Mediapipe and OpenCV

Robust Intelligent Posture Estimation is an important aspect of an AI Gym Trainer that can help fitness enthusiasts improve their workout technique and prevent injuries. This research presents an approach to achieve accurate posture estimation using Mediapipe and OpenCV. Mediapipe is a machine learning framework that provides pre-trained models for human posture estimation, while OpenCV is a popular computer vision library that offers a range of functions for image and video processing. The proposed solution integrates the strengths of both tools to develop a robust posture estimation system. The system first captures the user’s video feed and passes it through MediaPipe to detect the human body landmarks, then, OpenCV is used to calculate the angles between the detected landmarks in order to analyze the posture. The system provides real-time feedback to the user on their posture and suggests reparative measures. The use case that has been used for this research was repetitions for bicep curls. The proposed system can be tested on various exercises, such as squats, push-ups, and lunges. It can accurately estimate the posture of the user in different lighting conditions and is robust to occlusions and background clutter. The system can be deployed as an AI Gym Trainer and can help fitness enthusiasts improve their form and technique while reducing the risk of injury.
Machine Learning-Based Timeseries Analysis for Cryptocurrency Price Prediction: A Systematic Review and Research

A virtual currency known as cryptocurrencies holds all business online. It’s virtual money that wouldn’t materialize like complicated conventional paper currency. Thus, this study emphasizes a distinction between distributed paper currency and cryptocurrencies, where these individuals may access information without outside interference. Because of its considerable market swings, such cryptocurrencies have an influence upon commerce as well as foreign diplomacy. Virtual currencies which are available in the market, such as Bitcoin (BTC), Ethereum (ETH), Terra (LUNA), Solana (SOL), Cardano (ADA), Tether (USDT), Binance coin (BNB), USD coin, XRP coin, Avalanche coin (AVAX) and Lite coin (LTC), etc. This study focussed on a detailed analysis of the literature about Machine Learning (ML) methods used for predictions. This proposed work also focused on implementing an efficient Machine Learning (ML)-based time series model for predicting BTC cryptocurrency prices. Long Short-Term Memory (LSTM) forecasting theory was established to accommodate the fluctuation of bitcoin prices and achieve great precision. The effectiveness of the LSTM in predicting the price of a cryptocurrency is demonstrated by this suggested study’s comparison between it and comparable time-series models.
Efficient Diffie Hellman Two Round Secret Key Agreement Protocol

Diffie Hellman algorithm exchanges the secret key among the two parties over a public channel using asymmetric encryption. Whitfield Diffie modeled it, along with Martin Hellman in the 1970s, and named it Diffie Hellman. Diffie Hellman comes under the public key cryptosystem and is mainly used in SSH, IPsec, TLS, and PGP security protocols. We aim tosecure this algorithm from the threat of an implementation attack, a man-in-the-middle attack, and insider and outsider attacks. This paper made the secret key more secure by two-time key generation. We get our second shared secret key after applying the first secret key generation operation and taking its primitive root. Then we exchange both user's public keys so they can calculate their secret keys. This two-round key generation procedure will make more secure data transactions.
Advantages of a neuro-symbolic solution for monitoring IT infrastructures alerts

The classification and at the same time the inter-active characterization of both bad connections, called alerts or attacks, as well as normal connections, is a must for monitoring network traffic. For this specific task, we developed in this study a neuro-symbolic predictive model based on Logic Tensor Networks. Moreover, we present in detail the advantages and disadvantages of using our hybrid system versus the usage of a standard feed-forward deep neural network classifier. For a relevant comparison, the same dataset was used during training and the metrics resulted have been compared. An overview shows that while both algorithms have similar precision, the hybrid approach gives also the possibility to have interactive explanations and deductive reasoning over data.
EC-CFI: Control-Flow Integrity via Code Encryption Counteracting Fault Attacks

Fault attacks enable adversaries to manipulate the controlflow of security-critical applications. By inducing targeted faults into the CPU, the software’s call graph can be escaped and the control-flow can be redirected to arbitrary functions inside the program. To protect the control-flow from these attacks, dedicated fault control-flow integrity (CFI) countermeasures are commonly deployed. However, these schemes either have high detection latencies or require intrusive hardware changes. In this paper, we present EC-CFI, a software-based cryptographically enforced CFI scheme with no detection latency utilizing hardware features of recent Intel® platforms. Our EC-CFI prototype is designed to prevent an adversary from escaping the program’s call graph using faults by encrypting each function with a different key before execution. At runtime, the instrumented program dynamically derives the decryption key, ensuring that the code only can be successfully decrypted when the program follows the intended call graph. To enable this level of protection on Intel® commodity systems, we combine Intel®’s TME-MK with the virtualization technology to achieve function-granular encryption. We open-source our custom LLVM-based toolchain automatically protecting arbitrary programs with EC-CFI. Furthermore, we evaluate EPT aliasing with the SPEC CPU2017 and Embench- IoT benchmarks and discuss and evaluate potential TME-MK hardware changes minimizing runtime overheads.
Structural Harmonization of Digital Media Communications: Methodological Analysis

The ontological complexity and discursive diversity of digital media communications requires adequate methodological approaches of research and analysis. In this article, to describe the structural organization of the system of digital media communications, the author focused on the application of N. Bor's general scientific principle of additionality. Structural harmonization and sustainability of digital media communications involves finding a certain ratio (measure) of structural order (order) and structural diversity (chaos) in modern media systems. The different communication tools used by media communication actors in the digital environment, which seem to be incompatible, in fact cannot be directly related to each other in the usual way, but can be seen as complementary to each other. In this case, the result of their use will be determined more holistically. The law of harmonization of opposing natural and social formations in the context of the theory of communication and media communication is objectively considered through the prism of the concept of “information” and its basic properties affecting communication processes. In this regard, the author considers modern quantitative theories of information (A.D. Ursul, R. Hartley, C. Shannon, L. Brillouin, W.R. Ashby, N. Wiener, V.B. Vyatkin, etc.) to conduct a methodological analysis and form the preconditions for structural harmonization in digital media communication systems. Structural harmony of digital media communications system not only provides its structural integrity, but also affects the property of stability of such a system. Stability of media communication system implies the ability of system objects to preserve the integrative quality of the system, even if the system undergoes influences from external and internal environments. Integrative quality of the system is determined by such an interconnection of the structural elements of the system, which generates new properties of the elements of the system compared to those that they had this relationship. The stronger the links between the elements, the more stable the system and is able to maintain its integrity. Maintaining these integrative parameters within acceptable limits by controlling them and, consequently, preserving the system is called system homeostasis. System development and existence is possible as long as the most important integrative parameters maintain homeostasis or stability.