Reviewerly/exHarmony huggingface dataset:
Towards Flow Scheduling in A Quantum Data Center

We propose an architecture for future quantum data centers with the scheduling of quantum computing flows, which integrates quantum processors, quantum switches, and quantum networks. The versatility of these data centers is discussed, highlighting their role in distributed quantum computing and quantum multi-party protocols, which include quantum cryptography and multi-party computation. We address a key challenge within this proposed architecture, namely entanglement flow scheduling which resembles its counterpart in classical data centers. Classical flow scheduling algorithms are examined and benchmarked against our proposed metrics which evaluate fairness and efficiency. We believe the proposed architecture can help to develop quantum data centers that can address the limitation of near-future Noisy Intermediate-Scale Quantum (NISQ) devices. We expect our work can inspire more research on the design of future quantum data centers.
Towards an Algebraic Specification of Quantum Networks

The main attributes of quantum networks are the utilization of quantum phenomena, security guarantees, and availability of their main quantum resource - entanglement. The fundamental differences between classical and quantum information will require joint efforts in physics, engineering and computer science to make quantum networks functional and scalable. A common language must be established between the hardware and software community. We envision a foundational model for quantum network programming languages. Such a model should contain the essential constructs for programming quantum networks, allow for specification and verification of end-to-end entanglement distribution, and provide guidelines for composing network protocols.
Quantum Network Planning for Utility Maximization

Enabling quantum capabilities in future generations of networks requires integrating quantum hardware into existing network infrastructure and developing new protocols for quantum communications. However, the cost and inherent noise associated with quantum hardware necessitate an efficient deployment strategy with a minimal number of quantum repeaters and memories. In this paper, we introduce a comprehensive network planning framework for distributing quantum hardware across existing infrastructure, with the objective of maximizing quantum network utility within an entanglement distribution network. We showcase the results of our framework using a dumbbell network topology as a preliminary example and the SURFnet network as a real-world case study. We explore the impact of quantum memory multiplexing within quantum repeaters, as well as the influence of memory coherence time on quantum network utility. We further examine the effects of different fairness assumptions on network planning, unveiling their ramifications on real-time network performance.
Dynamic Queuing Analysis and Buffer Management for Entanglement Swapping Buffers with Noise

Entanglement swapping is a core operation in a quantum network. It consumes a pair of entanglements to build a remote entanglement between two parties without direct interaction. In a buffered quantum network, unpaired entanglements can be stored in a quantum buffer for future uses. However, suffering from noises in the quantum buffer, fidelities of buffered entanglements degrade exponentially over time. Entanglements with low fidelity are no longer suitable for certain quantum applications and ought to be discarded. This paper analyzes the dynamic queuing process for a pair of link-level quantum buffers in entanglement swapping. By modeling the quantum buffer pair as a double-sided queue, we derive a closed-form buffering time distribution for every incoming entanglement with respect to the real-time buffer backlog. The distribution reveals the probability that entanglement will be discarded due to low fidelity and helps us design an active buffer management policy that controls the buffer backlog with negligible impact on the entanglement swapping throughput. A discrete-time simulator is developed to demonstrate the correctness of our analysis result and validate the effectiveness of our proposed policy.
Key Generation and Identity Verification using Quantum Teleportation

The advent of quantum computers introduces new challenges to the fields of encryption and authentication. Traditional cryptographic systems, such as RSA and ECC, rely on mathematical problems that are computationally hard for classical computers to solve. However, quantum computers have the potential to render these algorithms ineffective by leveraging Shor's algorithm, which can efficiently factor large numbers and solve the discrete logarithm problem. This capability threatens the security of many widely deployed encryption and authentication schemes. There have been a number of post-quantum cryptography methods proposed in the past. However, they are either relatively difficult to implement in current small-scale quantum computers or they do not take noise and error mitigation into account. In this paper, we propose a new framework of post-quantum key generation and identity verification mechanisms which are relatively easy to implement and adapt. Our approaches take the noisy nature of current quantum devices into consideration. As a result, our proposed approach can resist attacks from malicious quantum computers and the successful attack rate could quickly decay to zero as the number of qubits increases.
A Control Architecture for Entanglement Generation Switches in Quantum Networks

Entanglement between quantum network nodes is often produced using intermediary devices - such as heralding stations - as a resource. When scaling quantum networks to many nodes, requiring a dedicated intermediary device for every pair of nodes introduces high costs. Here, we propose a cost-effective architecture to connect many quantum network nodes via a central quantum network hub called an Entanglement Generation Switch (EGS). The EGS allows multiple quantum nodes to be connected at a fixed resource cost, by sharing the resources needed to make entanglement. We propose an algorithm called the Rate Control Protocol (RCP) which moderates the level of competition for access to the hub's resources between sets of users. We proceed to prove a convergence theorem for rates yielded by the algorithm. To derive the algorithm we work in the framework of Network Utility Maximization (NUM) and make use of the theory of Lagrange multipliers and Lagrangian duality. Our EGS architecture lays the groundwork for developing control architectures compatible with other types of quantum network hubs as well as system models of greater complexity.
Leveraging Quantum Key Distribution for Securing MACsec Communications

Somewhat ironically, advances in quantum computing are both the cause of, and solution to, the observation that the dependence of public-key cryptography (that underpins much of today's network security) on the intractability of particular mathematical problems may not be future-proof. Since quantum computing rests upon the inviolability of the laws of quantum physics, network security approaches based on quantum computing ought to be more durable.
RLID-V: Reinforcement Learning-Based Information Dissemination Policy Generation in VANETs

Ciphertext policy attribute-based encryption (CP-ABE) is popularly used to implement secure and accurate access control of disseminated information in vehicular ad hoc networks (VANETs). Nevertheless, how to improve the policy generation of CP-ABE for accurate information dissemination in the dynamic VANETs remains a challenge, as there are several access control policies rising from moving vehicles and road side units (RSUs) with different sensing boarder regarding to a specific event, such as moving vehicles and road side units (RSUs). To solve this problem, this paper proposes a reinforcement learning-based information dissemination policy generation scheme in VANETs, named RLID-V. The scheme firstly combines multiple attribute-based access control policies and resolves policy conflicts between vehicles and RSUs. Then, a manual feedback policy construction method is designed by applying decision tree to the collected feedback from all receivers. Finally, we employ reinforcement learning to dynamically update the confidence weights of different policy sources. The experiments are conducted in two classic VANETs scenarios, traffic guidance and accident warning, demonstrating that RLID-V achieves better performance in the accuracy and effectiveness of information dissemination compared with three existing schemes. Otherwise, RLID-V outperforms the compared schemes in robustness with 20% error feedback and takes a negligible cost of less than 1% of the overall delay overhead for policy generation.
Dependency-based Multi-granularity Code Retrieval

In contemporary times, the technique of code retrieval, aimed at extracting code fragments or files pertinent to a specific query from a code repository, is widely employed in software engineering. This technique significantly assists developers in locating particular code or resolving distinct code issues. However, a major challenge in code retrieval is the disparity between the source code and query statements, which often results in a substantial semantic gap, proving difficult to reconcile. With the advent of the deep learning era, numerous deep techniques are being explored to manage the swelling libraries of code files. However, conventional deep models typically utilize semantic representation grounded on word sequences, neglecting to consider the unique structural data inherent to the code, leading to less-than-optimal performance outcomes. A profound expression based on code abstract syntax trees parsing is code dependency information, which forms the foundation of code execution and aids in capturing the semantics of code efficiently. Consequently, a dependency-based multi-granularity code retrieval model is proposed in this study. This model deftly integrates dependency data into semantics and parses code into statement-level and token-level granularities, thus exploring the semantic relationship between different code granularities. Experimental outcomes demonstrate that the innovative method of integrating dependencies and extracting multi-granularity semantic data noticeably outperforms the results achieved by the presently prevalent baselines.
Enhancing Fintech Security - A Comparative Analysis of Advanced Security Algorithms

Financial technology, widely known as FinTech, is a new form of the financial industry focused on using technology to improve financial services. The idea of FinTech revolves around the innovative idea of improving financial services by adding technological solutions according to business situations. FinTech aids organizations, consumers, and business owners to effectively manage or control their financial transaction processes. The challenge in FinTech arises from data privacy and security concerns. FinTech companies collect and store sensitive financial information that poses a potential risk for users if the data is not correctly secured. Most security and privacy-related concerns can be mitigated with the effective implementation of advanced security algorithms into fintech services. The paper presents a comparative analysis between advanced security algorithms, namely Secure Hash Algorithm (SHA), Advanced Encryption Standard (AES), Rivest-Shamir-Adleman (RSA),Elliptic Curve Cryptography (ECC), and Encryption with Verification Code (EVC) based on the level of authentication and privacy achieved.
VHDL Implementation of Low Power Sequential Circuits using Reversible Logic

In the last few decades, reversible logic has become a rapidly developing field of study which holds significant promise across a wide range of applications, particularly in low-power CMOS, micro and optical computing. A key feature of reversible logic is its compatibility with future computing technologies that are designed to produce minimal amounts of heat and consume very low power. Incorporating reversible logic into such technologies could potentially revolutionize the computing industry by making devices more energy-efficient and cost-effective in the long term. The research presented in this paper aims to overcome the traditional speed pace exchange by using the concept of reversible logic bringing quantum computing devices one step closer to reality. In this study, different basic reversible gates and D F/F using VHDL. The total power dissipation for the D F/F is 0.042W. Xilinx 14.5 software is utilized for simulating the design.
E-Commerce Recommender System based on Customer Reviews Analysis using Deep Neural Network

In recent days, users' preference for purchasing products through the web is increased drastically. Users can refer the recommendation about particular products based on other users' experiences on the web in various forms such as comments, blogs, reviews, etc. Before buying a product online, users read the review about the products which is based on the experience of others. A few people may leave reviews to boost the product's offer or to withdraw from it. Customers who trust reviews to decide whether or not to buy a product may be confused by this. As a result, it is necessary to find honest reviews and remove fraudulent reviews that have been added by malicious or fraudulent users. The proposed system comes up with a solution to this problem. The time interval between the reviews was calculated using driving times. The proposed system used Recurrent Neural Network (RNN) to precisely discover progressive fraud; the suggested approach mines dynamic periods, such as driving meetings. These drive meetings can be useful for discovering local inconsistencies in product reviews rather than global abnormalities. After that, examine the product's rating, reviews, and progression to identify evaluation-based facts, review-based facts, and chain of command facts. In addition, this work employs an optimization-based aggregation technique for coordinating real-world components in fraud detection. The evaluations of this optimization are based on obtained engineered Amazon product review dataset. The classification of product review data gives online users the information they need to understand review content in a short amount of time.
E - Commerce Product Recommendation System using Collaborative Filtering

This research study intends to develop an interface for product recommendation system using collaborative filtering. Product recommendation system used for recommending to the users about the products based on their previous purchases and search results. The final product is a fully functional interface that demonstrates the potential of collaborative filtering and its application in building the product recommendation system.
Artificial Intelligence and Machine Learning Combined Security Enhancement Using ENIGMA

Enigma is a relatively new and emerging field that has the potential to bring significant benefits to the way contracts are executed and managed. The integration of Artificial Intelligence (AI) into smart contract technology can automate repetitive tasks, reduce the need for human intervention, improve decision-making, and provide transparency and trust. It can also provide more flexibility, handle more complex tasks, learn from past experiences, have predictive capabilities, and have human oversight and intervention. All these features make Enigma contracts more advanced than traditional smart contracts. AI-powered smart contracts, or Enigma contracts, can also improve contract execution, increase efficiency, facilitate better negotiation, and facilitate automated dispute resolution. However, as the technology is still in its early stages, major challenges and risks can adopted but the need for robust security. The potential for AI is to make decisions that are not in the best interests of its parties. Despite these challenges, the potential benefits of AI-powered smart contracts make them an area of on-going research and development that is worth exploring further. Enigma can be used or applied in various fields, and can be used to secure the sensitive information by applying robust security system. Enigma contract is a AI powered smart contract which is used to automate decision-making processes and improve its efficiency, Enigma as the name suggest it is a complex security network which has the potential to revolutionize the security system by increasing efficiency.
Key Challenges and Limitations of the OSINT Framework in the Context of Cybersecurity

In today's world, Open-Source Intelligence (OSINT) has gained popularity as a method of gathering data, both for cyber-attacks and detecting cyber threats. It involves using publicly available information from the internet to identify and evaluate potential cybersecurity risks. Despite its usefulness, the OSINT framework is not without its challenges and limitations. The objective of this research study is to examine the key challenges and limitations of the OSINT framework specifically in the context of data gathering for cybersecurity purposes. This study relies on a comprehensive review of relevant literature on OSINT and its applications in cybersecurity, as well as case studies. Some of the primary challenges and limitations of the OSINT framework that have been identified include issues with data quality, data quantity, data integration, analysis and interpretation, privacy, and ethical considerations. To tackle these challenges, this research study proposes a range of potential solutions, such as the development of more advanced analytical tools and techniques, the integration of machine learning and artificial intelligence algorithms, and the implementation of responsible and ethical data collection and analysis practices. Overall, this research provides valuable insights into the challenges and limitations that need to be addressed when using OSINT as a method of data gathering for cybersecurity purposes.
Developing an Explainable AI Model for Predicting Patient Readmissions in Hospitals

The objective of this study is to develop an AI model that can correctly identify which patients are most likely to require hospital readmission within a predetermined window of time after being discharged. Given that readmissions are linked to higher healthcare costs and poorer patient outcomes; this is a crucial problem in healthcare. The model must, nonetheless, also be explicable, which means that healthcare professionals must be able to comprehend the rationale behind why it made certain predictions. This is essential for establishing the model's credibility and making sure it is being used properly. To do this, the study may employ a range of machine learning methods renowned for their interpretability, like decision trees or random forests. Additionally, the study could investigate how to generate feature importance plots or partial dependence plots to visualize the model's decision-making process. Overall, by enhancing patient outcomes and fostering openness and confidence in the use of AI, this research subject has the potential to have a significant impact on healthcare.
Building a Light Weight Intelligible Text-to-Speech Voice Model for Indian Accent Telugu

Text-to-speech (TTS) synthesis involves creating machines that can talk. It is the process of automatically converting written text into spoken speech. TTS systems are important in human-computer interaction, and they have become more prominent with the advancement of computer technology. There has been a growing demand for speech applications in various areas such as telemedicine, robotics, Google Assistant, and other fields, especially during the Covid-19 era. It is clear that the existing speech technology is not good enough for users. Many speech applications still use pre-recorded prompts instead of text-to-speech because the quality of text-to-speech isn't good, and it can't pronounce non-standard words. In this study, a dependable and effective approach is proposed for creating speech synthesis voices that are specialized for specific domains and can handle non-standard words. This involves developing lexicon rules for non-standard words. By constructing databases that are closely related to the specific domain of the speech application, concatenative unit selection synthesis techniques, implemented using the festival/festvox tool, can be utilized to achieve high-quality, intelligible, and natural speech synthesis within the targeted domain. And the quality of proposed voice model is measured using Melcepstral distortion as an objective metric and Mean Opinion score as a subjective metric.
Natural Language Processing Models: A Comparative Perspective

Natural Language Processing is a thriving branch of artificial intelligence with diverse applications across multiple domains. In recent years, advances in machine learning models for NLP tasks have resulted in a parallel development in NLP methodologies. These models are capable of performing complicated NLP tasks such language translation, sentiment analysis, text categorization, and text production. This study reviews the NLP models by analyzing the traditional models, such as rule-based systems and statistical models, and then move on to the recent neural network and deep learning models. Natural Language Processing (NLP) is a branch of artificial intelligence with diverse applications across multiple domains. In recent years, advances in machine learning models for NLP tasks have resulted in a parallel development of NLP methodologies. These models are capable of performing complicated NLP tasks such as language translation, sentiment analysis, text categorization, and text production.
Information Retrieval Framework Based on Filtering-HMM Algorithm

Today, with the rapid development of the Internet, text information is increasing rapidly. Therefore, there is a need to mine text information so that the relevant information can be detected and organized in the text. Especially, the multimodal information retrieval is mainstream, to solve the “heterogeneous gap” problem in cross-modal retrieval, most of the existing main methods rely on the idea of representation learning. Specifically, cross-modal retrieval algorithms generate isomorphic unified high-level feature representations for different modal data by setting up a model framework that can establish the semantic relationships between heterogeneous data. Hence, in this study, the filtering algorithm is firstly applied to clean the data sets, then, the modified HMM model is applied to achieve the information retrieval framework. In the experiment, from the aspects of accuracy and time usage, the proposed model is tested compared with the SVM and traditional HMM, the results show the proposed algorithm performs much better.