Reviewerly/exHarmony huggingface dataset:
空気噴流を用いた音楽体験の臨場感提示における触覚提示部位の違いによる影響

Several devices had been proposed to improve the musical experience. Most of them use transducers, but the problem with transducers is that the size of the device can be too large when a larger vibration is desired. Therefore, we had been studying research using air-jets. We are feeled powerful since low-frequency sounds resonating through the body at concerts and live music performances. We decided to use air-jets to stimulate the body with the sound of a bass while listen to the music. It had been found that air-jets stimulating the area around the center of the sternum enhance the sense of realism. In this study, we investigated through experiments more suitable locations of the body for the stimulation.
Material and Physical Reservoir Computing for Beyond CMOS Electronics: Quo Vadis?

Traditional computing is based on an engineering approach that imposes logical states and a computational model upon a physical substrate. Physical or material computing, on the other hand, harnesses and exploits the inherent, naturally-occurring properties of a physical substrate to perform a computation. To do so, reservoir computing is often used as a computing paradigm. In this review and position paper, we take stock of where the field currently stands, delineate opportunities and challenges for future research, and outline steps on how to get material reservoir to the next level. The findings are relevant for beyond CMOS and beyond von Neumann architectures, ML, AI, neuromorphic systems, and computing with novel devices and circuits.
Neural approaches for writing assistant tasks

The article is devoted to the analysis of tasks for building a writing assistant, one of the most prominent fields of natural language processing and artificial intelligence in general. Specifically, we explore monolingual local sequence transduction tasks: grammatical and spelling errors correction, text simplification, paraphrase generation. To give a better understanding of the considered tasks, we show examples of expected rewrites. Then we take a deep look at such key aspects as existing publicly available datasets and their training splits, quality metrics for high quality evaluation, and modern solutions based primarily on neural networks. For each task, we analyze its main peculiarities and how they influence the state-of-the-art models. Eventually, we investigate the most eloquent shared features for the whole group of tasks in general and for approaches that provide solutions to them.
Underwater Image Enhancement by Joint Lab and Opponent Color Spaces

Underwater images suffer significantly from severe color distortion and blurring due to the different attenuation of light as well as the scattering of light in underwater environments. To obtain clear underwater images, we propose a novel underwater image enhancement model based on joint Lab color and opponent color spaces. Lab color space can separate luminance and chrominance, and express rich color information based on human visual perception. Opponent color space is based on the biological vision mechanism, which divides colors into three opponent channels: red-green, blue-yellow and light-dark, and can resist the influence of light source variation. Our model utilizes the advantages of these two color spaces to restore the underwater images.We design a dual color space encoder-decoder module that fuses Lab and RGB color spaces to enhance degenerated images. Moreover, we also use an adaptive light source estimation module to learn a light source map from the opponent color space to correct the color deviation. Extensive experiments demonstrate that our proposed method achieves better enhancement results.
A Privacy-Preserving E-voting Scheme with Verifiable Format

Electronic-voting (e-voting) is taking place of the traditional paper voting, due to its efficiency and environmental protection. However, the e-voting must pay attention to the security threat, such as the legality, privacy, integrity, correctness and coercion-resistance, etc. To protect the privacy of the vote, voters usually blind or encrypt their ballots before submitting them. But the blinded or encrypted ballots also hide the format of them and the receiver cannot distinguish if they are legal or not in format. This paper aims to solve such a problem, and proposes a novel e-voting scheme based on homomorphic encryption, that can validate the correctness of counted ballots in format, besides meeting the requirements of other respects in security.
Hiding Sensitive Information in Desensitized Voice Sequences

Voice data is broadly acquired and utilized by consumer services. In order to process such data, most of the raw records are sent to web servers, possibly with dedicated acceleration hardware. However, in this way malicious service providers can identify the users because the raw voice sequences contain rich voiceprint information, which is adequate for deduction of a large amount of private information. In order to mitigate such problem, desensitization methods are employed as secure intermediaries between user and the cloud services. However, if these methods are provided by a third party as a black box, it may not proved to be safe enough. In this paper, we demonstrate and experiment the possibility of hiding information sufficient to extract original voice from in seemingly desensitized voices that may be used for various online services, utilizing StarGAN-based voice transformation and voice-optimized audio stenography technologies.
Blockchain cryptocurrency abnormal behavior detection based on improved graph convolutional neural networks

As cryptocurrency is widely used in the financial field, detecting anomalous trading behavior of blockchain-based cryptocurrencies has become increasingly important. Researchers have utilized graph convolutional neural networks (GCN) for detecting anomalous cryptocurrency transactions. However, GCN fails to fully capture the spatial information correlation between neighboring nodes when processing graph data, which limits the utilization of structural features in the model. Therefore, the performance of GCN may be constrained when dealing with complex, high-dimensional graph data. In this paper, we propose a cosine similarity-based graph convolutional neural network for detecting anomalous cryptocurrency transactions. Compared to traditional GCN models, our method can better utilize both the network structure features and spatial information correlation, and it performs better in processing infinite-dimensional graph data. Experimental results show that our model can effectively detect anomalous cryptocurrency transactions, thereby improving the security and reliability of cryptocurrency, and it has good application prospects.
An Assessment of Capabilities Required for Effective Cybersecurity Incident Management - A Systematic Literature Review

Reports from multiple independent sources reveal that a lack of preparation/readiness for cybersecurity incidents detrimentally affects business continuity and delays the recovery of operations. The current cybersecurity paradigm suggests that any organization can have cyberattacks anytime, regardless of the security principles applied. Therefore, the preparation/readiness phase is vitally essential to respond to these attacks adequately. We first identified a report on major cybersecurity incidents that is compiled by the Center for Strategic & International Studies, from which we examined major cybersecurity incidents, and we then established its credibility, non-partisan, global outreach, and attack coverage by cross-referencing it with Data Breach Investigation Report (DBIR). Given this context, this study conducts a systematic literature review to understand the body of knowledge that highlights administrative and technical capabilities of organizations to respond to a likely cybersecurity attack. Study findings show that about 72 percent of surveyed papers suggest that more organizations embrace administrative capabilities, especially compared to technical skills. More organizations also recognize the importance of having Cybersecurity Incident Response Team (CSIRT) abilities to enhance readiness to combat potential cybersecurity incidents - this is validated by the 88.89 percent score identified in this study. We emphasize the significance of organizations’ readiness to conduct cybersecurity incident triage and or thorough investigation based on the fact that major incidents will always occur as we validated in this study. As a result, using the National Institute of Standards and Technology (NIST) framework as an example, we suggest that organizations should determine which stages of the NIST framework are applicable.
A Machine Learning Methodology for Detecting SQL Injection Attacks

In today's environment, we all rely on web applications. The number of web application users is growing by the day. Most organizations use databases to hold data about their users or information about the services they provide. Structured Query Language is frequently used for database communication. During a SQL Injection attack, the attacker runs a malicious SQL statement on the database. Since SQL injection can be used to alter database values, wipe out the entire database, and steal database content, it poses a serious security risk. Attackers can acquire unauthorized access to the entire database by using SQL injection. SQL Injection attacks are feasible when a web application fails to check or filter user- entered input. SQL injection threats are both detected and analyzed using machine learning methods such as naive Bayes, Gradient Boosting, Support Vector Machine, Decision Tree, and Convolution Neural Network.
K-time Redactable Blockchain with Decentralized History Rewriting

Blockchain is a breakthrough computing paradigm with distinctive immutability, which has gained increasing attention in academia and industry. While immutability ensures the reliability and trustability of blockchain networks, it may lead to serious security issues, such as illegal storage of arbitrary contents and conflict of smart contract versions. To mitigate these issues, redactable blockchain is envisioned, which has the ability to rewrite histories. However, most existing approaches are vulnerable to single point of failure caused by over-centralization, as well as the Distributed Denial of Service (DDoS) attack resulted from large-scale redactions within a short period of time. In this paper, we propose a K-time redactable blockchain with decentralized history rewriting. Specifically, we use multiple authorities to control the redaction power, coping with single point of failure. Subsequently, we set limitations on the frequency and number of redactions to prevent malicious users from initiating massive redactions in a short period, which avoids DDoS attacks. In addition, we conducted a thorough analysis and numerous experiments to illustrate the security and efficiency of our proposal.
Intelligent Contract Vulnerability Detection Method Based on Bic-RL

Blockchain technology is praised for its decentralization, anonymity, and tamper-proof nature, but its complex architecture has led to numerous attacks, particularly against smart contracts. These attacks have caused significant economic losses and hindered the development of blockchain technology. Therefore, research on smart contract vulnerability detection methods has significant practical and application value. Existing detection technologies (such as symbolic execution, formal verification, and so on) have limitations, including heavy reliance on contract source code and incomplete feature extraction. This paper proposes a fully automated smart contract vulnerability detection method based on Bic-RL(Smart contracts Detection Based on Bicubic interpolation and Deep Residual Neural Networks - Long short-term memory) to address these issues. This method not only simplifies the data processing process but also realizes the complete feature extraction and ensures the high efficiency of detection performance. Performance evaluation shows this method has higher detection accuracy and generalization ability, with an accuracy rate of 84.5%.
A Blockchain-based Electronic Data Forensics System Design and Implementation

With the rapid development of the digital economy, in judicial practice, the type of evidence is developing from physical evidence to electronic evidence. Compared with traditional physical evidence, electronic evidence is vulnerable to external network attacks and tampering by internal practitioners in the process of collection, fixation, storage, transmission, etc. Therefore, determining the authenticity of electronic evidence is costly and difficult to admit, which directly affects the proportion of electronic evidence admissible in litigation. The unique characteristics of blockchain technology, such as tamper-proof, traceability, and multi-party participation, naturally fit with the demand for electronic data deposition. The electronic data deposit based on blockchain technology can avoid evidence forgery and reduce the impact of network attacks, ensure the authenticity of electronic evidence, and improve the admissibility rate of electronic evidence. This paper proposes a blockchain smart contract-based deposition system, which can realize the digital fingerprint deposition of documents by users, and can also display the timestamp of the evidence while the evidence is on the chain as well as the forensic evidence, thus ensuring the traceability and security of the evidence on the chain. Improve the efficiency and security of electronic data deposition.
Equitable Consensus: A PoS Mechanism based on Group Polynomial Election

PoS is a consensus mechanism where the probability of generating a block is directly proportional to a participant’s stake, but block generation rights concentrated in rich participants can lead to unfairness. Existing solutions mainly use linear consensus mechanisms where block generation rights are linked to the stake, but since these solutions fail to extend block generation rights to the majority of participants, compounding may occur. To address the problem, we propose a block producer selection method that extends block generation rights, called Grouped Polynomial Elections-based PoS (GPE-PoS). Specifically, GPE-PoS groups participants with similar stakes into independent groups, and performs polynomial calculations on the nodes in each group. Then GPE-PoS calculates the probability of block generation for each participant based on the polynomial calculations results and selects the participant with the highest probability as the block producer. At the same time, the Gini coefficient is used to measure the fairness of the blockchain system, where a Gini coefficient closer to 0 indicates higher fairness. Simulation results show that compared to linear consensus mechanisms based on stake, GPE-PoS has a stable Gini coefficient of around 0.1, which is an 83.33% reduction compared to traditional PoS.
Unraveling Ambivalence: A Novel Approach for Sarcasm Detection in Sentiment Analysis

Emotion ontologies have been developed in order to capture affect, a comprehensive construct that encompasses discrete emotions and feelings. The utilization of these ontologies holds significant relevance within the realm of sentiment analysis research, which seeks to examine a consumer's perception and disposition towards a corporation or a specific product. However, there have been limited attempts to modify and utilize these ontologies. This study undertakes a thorough examination and integration of prior research on emotion ontologies with the aim of developing a filtering framework for the identification of ambivalence in reviews, specifically for the purpose of sarcasm detection. The objective of our study is to improve the precision and dependability of sentiment analysis outcomes through the effective management of ambivalent reviews. These reviews contain mixed and contradictory emotions, which can potentially include sarcasm. Sentiment analysis models can better read complex emotional expressions using the filtering framework, improving consumer attitude analysis.
Evaluation of Search Engine Optimization Based on Blockchain Technology

The process of improving a website's design, making it visible to search engines, increasing its ranking, and getting more visitors is known as search engine optimization (SEO). There are various ways to use SEO and implementation of how website is linked with other. The main goal of SEO is to present the qualitative result of first page. The working of some optimization methods was so impressive that they are constantly used to serve as SEO. But SEO with Blockchain (BC) is a cheaper way to attractive relevant traffic to as website (WS). In this paper application of BC was presented to show its utility, how it can be utilised in search engine and outlining the benefits of BC technology based crucial paradigm for search ecosystem. Further the brief description of existing work has been presented in this paper, moreover the characteristics of BC & how it can solve the problem of existing centralized SE are also discussed here.
Secure Decision Tree Classification with Decentralized Authorization and Access Control

Outsourcing decision tree classification services to the cloud is becoming increasingly popular. However, the high value and sensitivity of data lead to problems with access control, data security, and centralized authorization for the cloud. In this paper, we present a Secure Decision Tree Classification with Decentralized Authorization and Access Control (SDTCDAAC). We first instantiate a Paillier cryptosystem based on revocable identity-based broadcast proxy re-encryption (RIBBPRE), which enables authorized users can participate in encrypted data computing to achieve access control. We further devise a scheme that decouples encrypted data storage logic and computation logic to improve the scalability of the model. Finally, we design a decentralized authorization service that key encrypted information sharing on the chain. The security analysis shows that the proposed scheme can generate a correct classification and meet the indistinguishability under chosen-plaintext attack (IND-CPA).
Effect of Gender Variability in Speaker Recognition

Nowadays, because of the speech recognition system, there is much interaction between humans and machines. The gender identification system is utilised in a wide range of applications, including call centres, robots, artificial intelligence, and security systems. This research describes a novel technique for identifying gender as male or female by extracting information from audio speech. In this study, gender is predicted from an audio data set using neural networks and a logistic regression structure. This methodology gives an accuracy of 97.8% in gender prediction. The most effective ten data features in the proposed approach were first determined. Afterwards, a neural network was developed as a classifier. The performance comparison of the classification and accuracy value was calculated. In addition, the suggested method's accuracy was compared to the classifier accuracy values generated by conventional machine learning techniques. With a 97.8% success rate, the study was successful in predicting gender.
Privacy-Aware Knowledge Distillation Based on Dynamic Sample Selection

Deep neural networks (DNNs) used in deep learning are usually designed to be complex, which makes them difficult to apply to resource-constrained mobile devices. Even if mobile devices can meet the resource requirements of these DNN models, they cannot be directly applied. Because such models contain privacy information, the direct application will pose a risk of privacy leakage. Differential privacy technology can be used to protect the private information in the model, but too many queries will weaken the degree of data privacy protection. In order to alleviate the above problems, this paper proposes a dynamic sample selection method, which can be used to select high-quality samples during model training, and the selected high-quality samples will be dynamically reduced as the training progresses. Therefore, the number of queries can be reduced by reducing the number of samples. To further achieve model compression, this paper proposes a privacy-aware knowledge distillation based on dynamic sample selection, which achieves model compression through knowledge distillation and uses dynamic sample selection to reduce the amount of samples, thereby reducing the number of queries and reducing privacy loss. Specifically, the student model trained in the self-learning stage is used to select high-quality samples, and then only high quality samples are used for distillation learning, thereby reducing the amount of samples for distillation learning. Since this paper uses differential privacy protection on the batch loss of distillation learning, and the batch size has been fixed, the smaller the number of samples, the fewer queries students need for distillation learning, thus providing stronger privacy protection for sensitive data. Experiments on the CIFAR-10 dataset show that the student model trained using the proposed method can achieve a compression ratio of 65% and an accuracy of 78%.
Secure Data Sharing and Mining in Distributed Environments

In today's interconnected world, distributed systems have emerged as the backbone for facilitating global collaborations and real-time data exchanges. As more organizations migrate to distributed architectures, the ability to share and mine data securely across disparate networks becomes paramount. Secure data sharing ensures that information is accessible to only authorized entities, while secure data mining aims to extract meaningful patterns from vast datasets without compromising privacy or integrity. This paper delves into the intricacies of implementing robust security protocols in distributed environments, emphasizing the Role-Based Access Control (RBAC) and transaction processing mechanisms. Through a comprehensive exploration, we aim to highlight the importance of marrying security with functionality to harness the full potential of distributed systems without sacrificing confidentiality and trust.
Dual-Model Active Federated Learning Based on Homomorphic Encryption

With the rapid growth of data and the improvement of privacy protection requirements, federated learning and homomorphic encryption technology has become an important means to solve data cooperation and privacy protection. This paper proposes a dual-model active federated learning method based on homomorphic encryption, which aims to integrate uncertainty and diversity (representativeness) data selection and protect data privacy. This method utilizes two types of clients. One type of client uses uncertainty-based query strategies to select the data to be marked by evaluating the uncertainty of prediction results; the other is based on diversified query strategy, which selects the data to be marked by calculating the difference between the marked data and the marked data, and uses the data for local model training and parameter updating. Then, the model parameters of the two types of clients are encrypted using homomorphic encryption technology, and are safely uploaded to the server for model parameter aggregation. The server realizes comprehensive model update by aggregating the model parameters of the two types of models, and uses homomorphic encryption technology to protect data privacy. Through iterative training and model updating, we achieve an efficient active learning process that improves model performance while ensuring data privacy.