Reviewerly/exHarmony huggingface dataset:
Joint Optimization of Resource Provisioning In Cloud Computing

Virtualization is utilized by cloud computing to effectively supply resources.Virtual Machines (VMs) are requiring more and more bandwidth, but prior study has not adequately addressed the issue of both VM and bandwidth allocation.A combined strategy combining bandwidth distribution and virtual machines (VMs) is needed to effectively supply resources.Demand in real life is also unpredictable.Resource reservations are permitted by service suppliers.However, we employ stochastic programming to account for this risk into consideration due to risks associated with over-and under-provisioning.We use a scenario tree reduction method to condense the problem space in order to increase the tractability of the stochastic optimization while maintaining its effectiveness as a heuristic.We also conduct a sensitivity analysis to determine how sensitive our answer is to parameter variations.We are using a deterministic equivalent formulation to analyses past demand data and discover that our answer is both optimum and responsive to alterations in parameter values.Additionally, this research demonstrates how consumers and suppliers can both benefit from price sensitivity analysis to maximise cost effectiveness.
An Efficient method to identify the Similarity K-mean Clustering Based On Information Passing

The clustering issue is addressed by KMean Clustering.There are two suggested clustering methods.Using the Clustering Algorithm, messages are sent from the client to the server.By calculating the mutual information among actual cluster labels and the clustering outcomes, it assesses the efficacy of clustering methods.A more concrete metric that reflects the potency of clustering algorithms is accuracy.To group objects, a dynamic clustering algorithm is applied.And discover the similarity approach.Other dynamic clustering issues are also quite significant.To utilize two concepts to further dynamic data clustering, let's finish.According to my understanding of the term, clustering, two or more objects are grouped together in order to take use of their combined power for improved performance and data storage.A crucial stage in the study of scientific data and the design of engineering systems is the clustering of data based on a measure of similarity.Using the data to train a set of centres so that the sum of squared errors between data points and their nearest centres is minimal is a frequent strategy.The evolutionary algorithm known as "k-means" gets its name from how it functions.Observations are divided into k groups by the algorithm, where k is an input parameter.Next, based on how close each observation is to the cluster's mean, it allocates each observation to a cluster.The initial cluster centres are chosen by the algorithm at random from a set of k locations.The average of the points in each cluster is recomputed as the cluster centre.
The Blockchain Technology Integration with Internet of Things for Digital supply Chain Transformation

Numerous global supply chains lack the tools necessary to survive in the world. Because supply chain managers must change their focus from cost-cutting to enabling new processes, as well as making businesses more connected and flexible, in order to create value across the enterprise. Digitalization controls the networks that transforms today’s supply chains. However, supply chain management consists of intricate systems and procedures that span numerous ecosystems, partners, and geographical regions and are managed by a number of different stakeholders. The complexity of current supply chain management systems has resulted in a lack of effectiveness along the whole value chain, which delays transactions and erodes customer and organization trust globally. Therefore, in order to consider this concept to be examine, empirical research was conducted to assess the blockchain technology integration and internet of things and their impact on digital supply chain transformation. This research used a quantitative approach, descriptive and analytical methods used for assessment as well as a convenient random sampling technique. Data of 187 respondents used after screening 390 received responses from hardware manufacturing companies located in Dubai UAE. Data was tested using SmartPLS 4.0 by applying, reliability, validity, discriminant validity and hypothesis testing. The findings revealed a positive significant relationship between blockchain technology and digital supply chain transformation utilizing internet of things in an upgrading way.
Detection of Facial Forgery in Digital Images

Detection of facial forgery in images and videos is a complex and challenging task. For the last years, different methods have been employed to detect facial forgeries. However, facial forgeries are still very challenging to detect. In this paper, a modern facial forgery detection method is presented which automatically detects facial forgeries in videos and images. The proposed method classifies fake and original videos and images. To classify the images, deep learning model, Inception-ResNet has been adopted. The proposed method is validated on publicly available Deepfake TIMIT dataset and effective results have been reported.
Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions

In this article, we offer a method to successfully enhance a traffic classifier based on Naive Bayes Prediction with a limited collection of training samples.Here, you should put forth a fresh traffic classification strategy to make use of data from correlated traffic patterns produced by an application.Traffic patterns are represented by statistical characteristics that are extracted.To implement Correlation based feature selection to eliminate pointless and superfluous features from the feature collection with low intercorrelation and high class-specific correlation.The classification ability of different traffic classification methods can be successfully demonstrated by Naive Bayes prediction.In comparison with current state-of-the-art traffic classification methods, suggested strategycan produce significantly improved classification results.Getting a high-performance analytic characteristic is one of the system's goals.
An Analysis of Browser and Machine Fingerprinting Techniques

There has been exponential growth in using the World Wide Web (WWW) since 1994. As e-business or target advertisement is growing exponentially, personalization directly impacts the retention and usability of customers. Through web user profiling, this research can precisely determine end-users' interests, provide them with a better user experience, and play with their digital identities over the web without their consent. Through the user's behavior, this research can efficiently capture the end-users interest. If we look at it from another perspective, this research can help determine the source of any criminal activity. There are various ways, but one way is to establish the profiles of anonymous users. It is assumed that all web clients are different because they all have different fingerprint values, and their fingerprints remain stable or never change. This research uses every web user's unique fingerprint to categorize a user's web usage. Web personalization is the knowledge gained by websites through user navigational behavior and interests. User profiling is the collection of the information of each unique user, either explicitly or implicitly. When the page/domain is viewed helps create a user profile.
A Recursive Distributed Linear Bias Model

Associating sets of observations is fundamental to multi-sensor data fusion. If sensor bias is relevant to the system, algorithms that model the error structure accurately are needed for optimal performance. The global nearest pattern (GNP) framework assumes a linear relative bias exists in the state space of two sets of tracks. This model effectively forces linearization of sensor registration errors about the mean target location, treats the effect of any bias as identical across all tracks, and is capable only of estimating relative bias, not absolute bias per sensor. Furthermore, as we will show, the GNP assignment costs cannot consume the cross-correlated tracks that are the result of optimal fusion given this bias model. Here, a new linear bias model that uses per-track Jacobians of track error against an arbitrary set of bias parameters is described. This work presents a new, much more flexible distributed linear bias (DLB) model for track fusion and shows that this bias model supports recursive minimum mean squared error (MMSE) optimal fusion for an arbitrary number of sensors. In addition, this model can support track fusion for non-linear bias models where the bias is dispersed across separated targets.
Extended Abstract of SeCNN: A semantic CNN parser for code comment generation

Code comments are essential for software development and maintenance, as they provide natural language descriptions of the code that help developers understand the program and reduce the time spent on comprehension. However, writing code comments can be tedious and time-consuming, and many software projects lack comprehensive and up-to-date comments, which can impair the readability and maintainability of programs.
Extended Abstract of E-SC4R: Explaining Software Clustering for Remodularisation

Maintenance of existing software requires a large amount of time for comprehending the source code. The architecture of a software, however, may not be clear to maintainers if up-to-date documentations are not available. Software clustering is often used as a remodularisation and architecture recovery technique to help recover a semantic representation of the software design. However, due to the diverse domain and structure of software systems, the suitability of different clustering techniques for different software systems are not investigated thoroughly. Research that introduce new clustering techniques usually validate their approaches on a specific domain, which might limit its generalisability. If the chosen test subjects only represent a narrow perspective of the whole picture, researchers risk not being able to address the external validity of their findings. This work aims to fill this gap by introducing a new approach, Explaining Software Clustering for Remodularisation (E-SC4R), to evaluate the effectiveness of different software clustering approaches. This work focuses on hierarchical clustering and Bunch clustering algorithms and provides information about their suitability according to the features of the software, which, as a consequence, enables the selection of the optimum technique for a particular software system. The E-SC4R framework is able to characterise both the strengths and weaknesses of the analysed software clustering algorithms using software features extracted from the code. The proposed approach also provides a better understanding of the algorithms’ behaviour by showing a 2D representation of the effectiveness of clustering techniques on the feature space through the application of dimensionality reduction techniques.
Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees

An ɛ-approximate quantile sketch over a stream of n inputs approximates the rank of any query point q—that is, the number of input points less than q—up to an additive error of ɛn, generally with some probability of at least 1 — 1/ poly(n), while consuming o(n) space. While the celebrated KLL sketch of Karnin, Lang, and Liberty achieves a provably optimal quantile approximation algorithm over worst-case streams, the approximations it achieves in practice are often far from optimal. Indeed, the most commonly used technique in practice is Dunning's t-digest, which often achieves much better approximations than KLL on real-world data but is known to have arbitrarily large errors in the worst case. We apply interpolation techniques to the streaming quantiles problem to attempt to achieve better approximations on real-world data sets than KLL while maintaining similar guarantees in the worst case.
The Metaverse Technology Empowered English Education—“Meta-course”

A metaverse is a new form of the Internet in the future that integrates many new technologies. According to some practical applications of metaverse-related technologies in education, we innovate a new English education model- “meta-course.“ The Meta-course is a model that can diversify teaching, provide digital services, and meet individual needs through feedback and assessment in the form of nodes. It is divided into three parts: preparation before class, course of teaching, and feedback after class. Compared to traditional education methods, meta-course integrates immersive learning, personalized design, and intelligent feedback after class to solve the current problems of single-mode English education, lack of practice, and low efficiency.
A SURVEY PAPER ON LIVE CRIMINAL DETECTION USING FACIAL RECOGNITION AND TRACKING ALGORITHMS

To address the increasing crime rates and limited police presence, an automatic facial recognition system utilizing cascading classifiers has been proposed as a solution.The system employs automated surveillance cameras to capture real-time video streams, and applies facial recognition algorithms to detect and identify human faces.The proposed system utilizes a cascading classifier approach that leverages Haar features to identify facial features in the video streams.This method is capable of accurately identifying and tracking individuals, even in challenging lighting and environmental conditions.To identify criminals, the system compares processed facial images against a database of known criminals.If a match is found, the system alerts authorities in real-time with the offender's location, allowing for quick response and intervention.The proposed system has a matching rate of over 80%, making it a highly effective and efficient method for criminal recognition in public places.By utilizing advanced facial recognition algorithms, the system can significantly improve security and public safety by identifying and tracking individuals in real-time, thus deterring crime and enhancing law enforcement efforts.make this short
REAL TIME SIGN LANGUAGE DETECTION

This project focuses on the development of a sign language detection system using computer vision techniques.Sign language is a vital means of communication for the deaf and hard-of-hearing community, and this system aims to bridge the communication gap by automatically recognizing sign language gestures and translating them into text.The proposed system uses a camera to capture images of a person signing, and then processes the image frames to detect and recognize hand gestures.The system utilizes algorithms to classify the detected hand gestures and map them to corresponding words or phrases in sign language.
OT BASED SMART DOOR FOR COVID-19 PREVENTIVE SYSTEM FOR WORK ENVIRONMENT

Our proposal is for a low-cost IoT-based system that ensures compliance with COVID-19 standard operating procedures (SOP).It does so by counting the number of people entering and leaving an area, monitoring body temperature, ensuring physical distancing, and warning attendees and managers of any violations.This system consists of multiple sensor nodes that communicate with a centralized server.The server's stored information has a multitude of applications, including the ability to conduct audits ensuring adherence to regulations, monitoring in real-time for imminent events or trends and use as an aid for effective planning.The system does not collect personal information or provide contact tracing data.To achieve this goal, we have developed a security-based hardware system that utilizes an 8-bit single-chip microcontroller in conjunction with sensor technology, lighting, and alarming actuators.The system's primary purpose is to ensure the security and privacy of a dedicated area by detecting unauthorized human intrusion, hazardous gas leakage, extreme prolonged temperature changes, atypical smoke or vapor content, and abrupt drops in illumination.The proposed system can detect any physical intervention or hazardous anomalies in the reserved space.To operate the system, we have written programs in C++ using the Arduino Integrated Development Environment with a special rule of code structuring.The system is designed as a small container that encloses all the respective sensor modules, microcontroller board, and open connections for actuators.The suggested mechanism exhibits an accessible interface, requiring no manual intervention to function.It can be installed with minimal changes to the existing infrastructure.
ONLINE VOTING SYSTEM

With rapid growth in technologies the old voting methods can change to advanced voting methods.Online voting software is a modern solution that can efficiently and securely facilitate the voting process for various groups and organizations.The use of such software eliminates the need for physical polling stations, as voters can cast their ballots from anywhere with an internet connection.The benefits of using online voting software are many; it increases accessibility, saves time and resources, ensures accuracy and transparency, and supports a more democratic decision-making process.Eligibility verification and accurate voter information are essential components of a successful online voting platform.While several countries have already implemented online voting software, this approach still faces challenges and limitations that must be addressed before universal adoption.In the following sections, we will delve further into the various types of electronic voting methods and examine successful global examples of online voting.We will also discuss current trends and future developments in online voting software provide a comparison between online and traditional voting methods.
STRUCTURE FROM MOTION BASED IMAGE UNWRAPPING AND STITCHING FOR SMALL BORE PIPE INSPECTIONS

Project aims to create an autonomous robot used for in-pipe inspection.The mechanism used involves a central rod upon which a translational element is fitted which in turn is connected to three frames of links and wheels.DC motors are attached to the wheels to achieve the drive required.The mechanism allows for small accommodation in pipe diameters.An electronic circuit consisting of three relay switches is used to control the entire circuitry of DC motors, camera and translational element.The camera is mounted on the top of the assembly, which in itself can be rotated thus giving a wide field of view in the pipe.The robot allows for detection of cracks, buckle, corrosions, pitting and many others.
Implementasi Sistem Pencatatan Saldo Sedekah Jumat di Masjid Ar-Rahmat Berbasis Web

Ar-Rahmat Mosque has played an important role in the management of Muslim places of worship. However, to improve financial management, a more modern financial recording system is urgently needed. As a concrete step, the author carried out community service activities with a focus on implementing a system for recording Friday alms balances at the web-based Ar-Rahmat Mosque. The aim is to make it easier for the mosque's prosperity council to submit information on Friday alms balance reports and to facilitate the mosque's financial communication with the community. The author uses data collection techniques such as observation, interviews, and library research to design a system that is easy to use and can display accurate data. The designed information system includes financial data of the Ar-Rahmat Mosque. The author also tests the quality of the system by distributing questionnaires with a Likert scale of 1-5 to respondents. The calculation results show that the average score is 4.5 which indicates that the respondents agree with the application designed.
Controlled Entanglement Source for Quantum Cryptography

Quantum entanglement has become an essential resource in quantum information processing. Existing works employ entangled quantum states to perform various tasks, while little attention is paid to the control of the resource. In this work, we propose a simple protocol to upgrade an entanglement source with access control through phase randomization at the optical pump. The enhanced source can effectively control all users in utilizing the entanglement resource to implement quantum cryptography. In addition, we show this control can act as a practical countermeasure against memory attack on device-independent quantum key distribution at a negligible cost. To demonstrate the feasibility of our protocol, we implement an experimental setup using just off-the-shelf components and characterize its performance accordingly.
AUTHENTICATED SUBGRAPH SIMILARITY SEARCH IN OUTSOURCED GRAPH DATABASES

Data mining is a computer process that combines techniques from artificial intelligence, machine learning, statistics, along with database systems to find patterns in big data sets.The process's objective is to take the information from the data collection and organise it so that it may be used in other ways.It includes features of database and data administration, pre-processing of the data, model and inference considerations, interestingness measures, complexity concerns, post-processing of the structures that are identified, visualisation, and online updating.Data mining is the practise of using using data mining methods to sample areas of a bigger population data collection that are too small to allow for statistically significant inference of any patterns that may have been found.It is used to examine data kept in data warehouses and choose marketing plans for their offering.The outsourcing of databases for bioinformatics and chemoinformatics has expanded recently.The outcome of the query graph is the comparable sub graphs.But, it's crucial to authenticate in order to check this info.GMTree, or the authentication-friendly metric index.The clients query receives the verification objects.The GMTree uses a pivot selection technique based on sampling.
THE ALGORITHMIC TASKS FROM THE STATE GRADUATION EXAMINATION IN THE PROFILING SUBJECT OF INFORMATICS FOR 2022

In the academic year 2021/2022, the State Matriculation Exam (the examination taken at the end of Secondary education to qualify for entry into University) was held for the first time for the students studying the profiling subject of Informatics in the Bulgarian secondary school. The article examines the tasks from the two sessions of the exam, which are supposed test the ability of high school students to select/create algorithms. The conditions to which a task of this type must meet and to what extent the tasks of the two exam topics satisfy these conditions are examined in detail.