Reviewerly/exHarmony huggingface dataset:
Construction of Evaluation Model of Regional Economic Development Level Based on Fuzzy Clustering Algorithm

In recent years, economic exchanges between China and the world market have become more and more close, and China's economic development has also directly affected the stability of the world economy. It even directly determines the major policies of the country, and its importance is obvious. For example, before China's reform and opening up, it had not yet implemented the transition of its economic system. At this time, more emphasis was placed on military strategy and political considerations. The coordinated development of regional economy does not mean simultaneous development or the same development speed. Rather, it is necessary to evaluate according to a certain index system, establish a scientific and systematic evaluation model, and analyze the data of coordinated regional economic development according to the system model. And it is becoming more and more obvious, manifested in the imbalance of society, economy and ecology in the system of the region itself, the continuous expansion of regional disparity, the serious regional blockade and local protection, the continuous economic friction between regions, and the unreasonable division of labor between regions. At the same time, help develop the central region, improve grain production capacity, make economical and effective use of coal resources in the central region, and promote the transformation of production organization structure in the central region, so as to make the central region develop better. Secondly, the division of economic regions from the perspective is convenient for policy-making departments to better study the regional heterogeneity and spatial spillover effect of policies. How to take effective measures to correctly solve the problem of regional gap and realize the coordinated development of regional economy is an important topic in front of us. This paper mainly focuses on the detailed division of economic regions based on fuzzy clustering algorithm, which is the premise and basis of constructing the coordinated development of regional economy, formulating regional economic development strategy and implementing the accurate regulation of regional economic policies. With the continuous rise of China's total economic output level, the regional division is becoming more and more detailed, and the standards are also changing
Robust Quantum Coding for Thermal Noise via Dissipative Dynamics

This paper proposes an improved quantum coding method based on Markovian dissipative dynamics that is robust against thermal noise. A method for correcting information errors is indispensable for the practical use of quantum information devices, and stabilizer codes for quantum systems have been devised. One of the quantum coding methods, namely, quantum coding based on Markovian dissipative dynamics, has a favorable feature that it does not require strict time control, however it is known that these methods are susceptible to thermal noise. In this paper, we propose a quantum coding method that incorporates a new mechanism to correct the disturbance of the quantum state caused by thermal noise into the quantum coding method using dissipative dynamics. We also analyze the stability of the proposed quantum dynamics in the quantum state corresponding to the target code word. Numerical experiments confirm the effectiveness of the proposed method.
Editable Blockchain for Secure IoT Transactions

In recent years, blockchain has attracted extensive interest due to its decentralised and tamper-resistant nature. However, the immutability of data on the blockchain creates a number of difficulties, such as the inability to eradicate malicious transactions and modify incorrect transactions. To facilitate logical editing, the majority of existing research on editable blockchains concentrates on block-level replacement or the addition of new transactions. However, the former produces unverifiable editing records, whereas the latter lacks a secure access mechanism despite retaining evidence of inaccurate data. In addition, previous research did not completely consider the characteristics of the data to be edited, causing subsequent transactions to be affected and rendered ineffective. A model of an editable blockchain is proposed to resolve these issues. First, the blockchain's underlying data structure is redesigned to include temporal attributes and built-in indexes, using general evidence data as an example. Second, the introduction of new algorithms for submission, amending, and querying enables the logical editing of blockchain data via subsequent appended transactions. This method preserves evidence for erroneous transactions while providing users with a secure interface. To address the issue of subsequent transaction failures, the data structure and associated algorithms for transaction types with significant contextual dependencies are enhanced. These enhancements ensure that data modification does not affect subsequent transactions. The experimental outcomes demonstrate that the proposed model permits blockchain editability while preserving system performance. By incorporating these innovations, it is possible to provide a framework for IoT transactions that is more flexible and secure, thereby circumventing the limitations of traditional immutable blockchains.
Data Transformation in Machine Learning: Empirical Analysis

Data transformation is a critical yet often overlooked preprocessing step in many data science pipelines. A right transformation can significantly improve the performance of the learning algorithm. Conversely, a wrong transformation can skew the data leading to adverse results. To better understand the effects of data transformation and provide best-practice recommendations, this research aims to analyze the efficacy of various data transformation techniques. We consider several commonly used transformation techniques and compare their effects on the learning model. In particular, normalization, unitization, logarithmic, square root, and arctangent transformations are compared. The transformations are applied on continuous-valued data before fitting linear regression. The accuracy of the resulting model is used as the measure of transformation efficacy. The results show that logarithmic transformation produces better results than other popular transformations. Therefore, the logarithmic transformation is recommended as the primary transformation method for preprocessing regression related data.
Asymmetric Actor-Critic with Approximate Information State

Reinforcement learning (RL) for partially observable Markov decision processes (POMDPs) is a challenging problem because decisions need to be made based on the entire history of observations and actions. However, in several scenarios, state information is available during the training phase. We are interested in exploiting the availability of this state information during the training phase to efficiently learn a history-based policy using RL. Specifically, we consider actor-critic algorithms, where the actor uses only the history information but the critic uses both history and state. Such algorithms are called asymmetric actor-critic, to highlight the fact that the actor and critic have asymmetric information. Motivated by the recent success of using representation losses in RL for POMDPs [1], we derive similar theoretical results for the asymmetric actor-critic case and evaluate the effectiveness of adding such auxiliary losses in experiments. In particular, we learn a history representation-called an approximate information state (AIS)-and bound the performance loss when acting using AIS.
Deep Neural Networks for Financial Transaction Risk Reduction in SMEs

The credit risk of an applicant is the major consideration that financial institutions look at when deciding whether to accept an application for factoring. The effect that blockchain technology has on credit risk should be given the utmost consideration in the decision-making process of financial institutions because it is of the biggest importance. This paper presents a case study of a small and medium-sized enterprise (SME) that satisfies the requirements of an incentive compatibility mechanism. The results show that the incentive subsidy that is provided by an incentive-compatible mechanism has a negative correlation with the effort level coefficient, the absolute risk aversion coefficient, and the external uncertainty of SMEs, while it has an effect on the maturity of its BCT and the operational capability of SME in a setting that is informationally symmetric and constrained by the goal of maximising the expected returns for all parties. When it comes to differentiating between different types of SMEs that have different capacities for their operations and providing different incentives for SME, the differential incentives with asymmetric information are more effective than the equal incentives. However, the effect of blockchain technology on the credit risk is the biggest consideration that the SCF must deal with that in order to find the better solution.
Design of Financing Strategy Model of Financial Management Based on Data Mining Technology

With the continuous development of India's market economy, the competition between enterprises is becoming increasingly fierce. In order to better deal with the fierce market competition, enterprise managers begin to pay more attention to internal management. Compared with traditional financial analysis methods, data mining technology can deeply understand the potential and deep-seated information of massive financial data, assist the company's financial analysts, investors and decision-makers to deeply understand the company's financial situation and make correct decisions on relevant problems. The enterprise financial data and non-financial data output by the traditional accounting information system have excess data and insufficient information. In the face of these structured or semi-structured massive data, the data mining technology is applied to the system to fully and effectively predict the future development trend of the enterprise. If enterprises want to achieve better results in the fierce market competition and achieve their own better development at the same time. We should recognize the important role of financial management and financing and data mining technology and apply data mining technology in financial management and financing. As an important part of financial management, fund-raising management is of great significance to the majority of enterprises. On the one hand, financing management is directly related to the financing process of small and medium-sized enterprises and determines the scale and speed of enterprise financing. On the other hand, through rational allocation and fund-raising, enterprises can realize the normal operation of various activities and provide a solid foundation for debt repayment financing. This paper will analyze and study the financial management and financing of enterprises based on data mining technology.
Blockchain-enabled Transparent and Trustworthy Regulation System for Smart Cities

The development and growth of the shared economy in the smart cities are significantly influenced by technology. An online community-based ecosystem that enables peer-to-peer trade between buyers and sellers as well as share- and rental-based access to goods and services. Lack of trust and transparency is the main problem in the shared economy system. A blockchain-enabled architecture is created for the shared economy ecosystem to address this problem. In this study, product registration and product services policies are implemented using smart contracts. This technology aids in distinguishing between genuine and counterfeit goods while also giving consumers access to the product’s metadata via QR codes. An experimental testbed that makes use of the polygon network is taken into consideration in order to validate the suggested framework.
Data-driven Abstractions via Adaptive Refinements and a Kantorovich Metric

We introduce an adaptive refinement procedure for smart and scalable abstraction of dynamical systems. Our technique relies on partitioning the state space depending on the observation of future outputs. However, this knowledge is dynamically constructed in an adaptive, asymmetric way. In order to learn the optimal structure, we define a Kantorovichinspired metric between Markov chains, and we use it to guide the state partition refinement. Our technique is prone to data-driven frameworks, but not restricted to. We also study properties of the above mentioned metric between Markov chains, which we believe could be of broader interest. We propose an algorithm to approximate it, and we show that our method yields a much better computational complexity than using classical linear programming techniques.
Service Discovery Mechanism for Micro Services in Cloud Computing: Comparative Study

Cloud computing is an online service that is provided as services over the internet. Finding and using the services that are available, which is a crucial component for service-oriented applications, is one of the major challenges in cloud computing. This paper ranks service discovery mechanisms into three major classes: centralized, decentralized, and hybrid. In order to determine and contrast the usefulness and efficiency of various service discovery techniques for cloud computing's microservices, this comparative study looks at several service discovery strategies. It examines existing research papers and studies in order to compare different approaches used for cloud service discovery.A comparison of these methods shows that the decentralized technique depends on a distributed network while the centralized relies on the central registry and the hybrid is a combination of both approaches. This review paper helps researchers who would like to compare analyses of service discovery mechanisms in cloud computing.
The Quantum Graph Recurrent Neural Network

Machine learning that combines the power of graph neural networks has its own significance in developing quick report analysis for various dynamic data which traditional statistical models may not be able to accomplish. Alongside the use of Quantum Computing in Neural Networks have become increasingly important over the years due to their ability to learn and process complex patterns. The Importance of QNN lies in the approaches it follows like entanglement, interference multiple super positional states, parallel computing, and backpropagation trainings which cannot be possible for Classical Neural Networks to work with. Also, the QNN, GNN integrates with the Quantum Machine Learning techniques like PennyLane that acts as an OpenShift framework to execute the optimized, trained algorithms for quick computational outcomes. This paper contains the use of Quantum Neural Networking, Quantum Machine Learning.
Adversarial Attacks and Defences of Various Artificial Intelligent Models

Adversarial attacks are becoming increasingly common and sophisticated as time passes and new defenses are being researched and developed. Adversarial machine learning is one of the techniques used to attack artificial intelligence models by feeding deceptive data. Therefore, in this paper, adversarial attacks against machine learning and deep learning are studied, and some defense strategies are also proposed against them. Two attack strategies; Evasion and Poison are used to attack the Support Vector Machine (SVM) model and Convolution Neural Network (CNN) model, respectively. Some defense mechanisms are applied to safeguard the models from such attacks. The models are applied and tested on the MNIST dataset. The results obtained from the models, before and after the defenses are applied are compared.
Preserving Data-Privacy Cooperative Control of Discrete-Time Multi-Agent Systems

With the rapid advancements of communication technology, distributed cooperative control has emerged as a promising approach, enabling participants to perform control based on their neighbouring agents, thereby facilitating a faster response and more flexibility. However, the privacy concerns must be addressed not only on the external adversaries but also on the internal adversaries, to encourage the participant to join this cooperative network. In contrast to existing literature, our study considers the scenario where participating agents are unaware of whether their neighbouring nodes inject noises, leading them to directly use the received data in control. We first design the noise injection scheme to ensure the mean-square consensus while preserving privacy in discrete-time multi-agent systems (MASs) and then derive the upper and lower bounds of the convergence rate. After that, we study the covariance matrix of the maximum likelihood estimate on the initial state of other agents based on the internal adversary's local information. The feasibility of (ε,δ)-differential privacy is characterized. Simulations of a practical cooperative adaptive cruise control illustrate the effectiveness of the Privacy-Preserving Cooperative Control (PPCC).
Smart Attendance Marking System Based on BLE

Attendance tracking is crucial for organizations and institutions, as it connects to various resources like wages and performance indicators. Conventional attendance marking methods are either manually taken or have drawbacks concerning range, setup ease, cost, accuracy/security, scalability, and accessibility/connectivity. However, Bluetooth Low Energy (BLE) technology emerges as an ideal solution. The proposed system utilizes BLE to address previous shortcomings, providing a comprehensive solution. It offers a suitable range, easy setup, affordable, secure, scalable, and easily integrable solution that offers offline functionality. This system also leverages the power of data by enabling the effortless collection of attendance information, offering valuable insights at different levels. The system utilizes a mobile application to transmit BLE beacons. An ESP (Espressif) - based (low-cost Wireless Fidelity (Wi-Fi) /Bluetooth microchip) device then scans for signals in the room and uses a deduping algorithm to eliminate duplicate IDs (identification numbers) and upload attendance data to an online database for dashboard visualization.
BERT Based Models for Sentiment Classification for Generating Insights from Olympics

The COVID-19 pandemic forced the Olympics to be held without spectators. As a result, most people tuned in to watch the Olympics from their homes. People expressed their emotions regarding sporting events through micro-blogging sites like Twitter. Even though research on the sentiment classification of sporting events has been done in the past, they usually focus on a single sport. The amount of material regarding sentiment classification and analysis of multi-sporting events is sparse. This necessitates the development of an efficient model which can accurately classify sentiments in multi-sports tweets. A sentiment classification of the recent Olympics would give an understanding of how people reacted to the Olympics and what influenced their reactions the most.In this paper, we focus on the recently held Tokyo 2020 Olympics. The dataset has been gathered from Twitter during the Olympics. The data has been classed into 3 emotion categories: Happy, Neutral and Sad. We develop several classification models on the BERT embeddings and conduct experiments to arrive at the most effective model. Upon sentiment classification of the entire dataset, a temporal analysis of the annotated corpus is performed to reveal how the change points in the data correlate to real-world events. This demonstrated how offline sporting events like the Olympics correlate with emotions in the virtual space.
Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks

The reliable deployment of neural networks in control systems requires rigorous robustness guarantees. In this paper, we obtain tight robustness certificates over convex attack sets for min-max representations of ReLU neural networks by developing a convex reformulation of the nonconvex certification problem. This is done by “lifting” the problem to an infinite-dimensional optimization over probability measures, leveraging recent results in distributionally robust optimization to solve for an optimal discrete distribution, and proving that solutions of the original nonconvex problem are generated by the discrete distribution under mild boundedness, nonredundancy, and Slater conditions. As a consequence, optimal (worst-case) attacks against the model may be solved for exactly. This contrasts prior state-of-the-art that either requires expensive branch-and-bound schemes or loose relaxation techniques. Experiments on robust control and MNIST image classification examples highlight the benefits of our approach.
Using Joint Training Speaker Encoder With Consistency Loss to Achieve Cross-Lingual Voice Conversion and Expressive Voice Conversion

Voice conversion systems have made significant advancements in terms of naturalness and similarity in common voice conversion tasks. However, their performance in more complex tasks such as cross-lingual voice conversion and expressive voice conversion remains imperfect. In this study, we propose a novel approach that combines a joint training speaker encoder and content features extracted from the cross-lingual speech recognition model Whisper to achieve high-quality cross-lingual voice conversion. Additionally, we introduce a speaker consistency loss to the joint encoder, which improves the similarity between the converted speech and the reference speech. To further explore the capabilities of the joint speaker encoder, we use the Phonetic posteriorgram as the content feature, which enables the model to effectively reproduce both the speaker characteristics and the emotional aspects of the reference speech.
Towards Matching Phones and Speech Representations

Learning phone types from phone instances has been a long-standing problem, while still being open. In this work, we revisit this problem in the context of self-supervised learning, and pose it as the problem of matching cluster centroids to phone embeddings. We study two key properties that enable matching, namely, whether cluster centroids of self-supervised representations reduce the variability of phone instances and respect the relationship among phones. We then use the matching result to produce pseudo-labels and introduce a new loss function for improving self-supervised representations. Our experiments show that the matching result captures the relationship among phones. Training the new loss function jointly with the regular self-supervised losses, such as APC and CPC, significantly improves the downstream phone classification.
Prompt Pool Based Class-Incremental Continual Learning for Dialog State Tracking

Continual learning is crucial for dialog state tracking (DST) in dialog systems, since requirements from users for new functionalities are often encountered. However, most of existing continual learning methods for DST require task identities during testing, which is a severe limit in real-world applications. In this paper, we aim to address continual learning of DST in the class-incremental scenario (namely the task identity is unknown in testing). Inspired by the recently emerging prompt tuning method that performs well on dialog systems, we propose to use the prompt pool method, where we maintain a pool of key-value paired prompts and select prompts from the pool according to the distance between the dialog history and the prompt keys. The proposed method can automatically identify tasks and select appropriate prompts during testing. We conduct experiments on Schema-Guided Dialog dataset (SGD) and another dataset collected from a real-world dialog application. Experiment results show that the prompt pool method achieves much higher joint goal accuracy than the baseline. After combining with a rehearsal buffer, the model performance can be further improved.