Reviewerly/exHarmony huggingface dataset:
Cross-Chain Interoperability and Collaboration for Keyword-Based Embedded Smart Contracts in the Internet of Things

In recent years, blockchain technology has been widely applied in the Internet of Things (IoT) field, where devices located in different blockchains need to interact with each other cross-chain. Existing cross-chain models have high implementation complexity, long response times, and are difficult to apply in IoT scenarios. In this paper, we propose keyword-based embedded smart contract cross-chain interoperability and collaboration model. This model identifies the status of smart contracts through the keywords of smart contracts, completes cross-chain operations, and achieves smart contract collaboration and asset exchange among devices in different blockchains. To find matching collaborative contracts, contract-specific keywords will be set, including asset amounts and types. In the cross-chain asset exchange scenario of the model, the assets sent by the sender are frozen in the contract. When the contract is successfully executed, the assets will be sent to the receiver, otherwise returned to the sender. In the cross-chain contract collaboration scenario, we identify the number of contract interactions, and when the contract collaboration is completed, the data will be retained, otherwise it will be invalidated. We also use an embedded smart contract method to further optimize IoT application scenarios. This method embeds the steps of deploying smart contracts into the invoked transaction, enabling the deployment and invocation of smart contracts through a single transaction. The experiments show that the keyword-based embedded contract method is 47% faster and 49% less costly compared to Notary schemes solutions in cross-chain scenarios.
A Video Game-Crowdsourcing Approach to Discover a Player’s Strategy for Problem Solution to Facility Location

In recent times, there has been a growing interest in the domain of computationally challenging problem solving within both scientific and organizational contexts. This study is primarily concerned with the extraction and comprehension of the methodologies and strategies employed by individuals when confronted with intricate problems, specifically those falling under the purview of NP-hard problems. The Facility Location Problem (FLP) serves as a prominent exemplar within this study’s framework. Traditionally, the handling of such complex problems has leaned upon intuitive reasoning and visual perception as the primary tools. However, these conventional approaches tend to provide only limited insight into the underlying processes employed in solving such problems. The present research seeks to bridge this knowledge gap through the utilization of advanced machine learning techniques for the purpose of categorizing and scrutinizing the strategies deployed by individuals in their attempts to tackle computationally challenging problems. The analysis conducted as part of this study unveils discernible and well-defined patterns and strategies that are employed by participants, some of whom have achieved notable levels of success. Remarkably, in certain instances, the outcomes achieved by these individuals have demonstrated a competitive edge when compared to the results produced by sophisticated computational methods, such as genetic algorithms. A fundamental component of our research methodology involves the application of heatmaps and clustering techniques. Through the normalization of results, our findings distinctly delineate two primary categories of games: those characterized by uniform player strategies and those characterized by a multitude of diverse and individualized tactics. Furthermore, our research employs a systematic approach to represent games by clustering them based on inherent similarities, utilizing cosine similarity as a metric for this purpose. By computing the averages of vectors within each cluster, we derive centroids that encapsulate the central tendencies exhibited by games belonging to that cluster. These centroids are then visually presented in a three-dimensional format, complemented by proportional spheres. These visual representations serve to vividly illustrate the dispersion and influence associated with each cluster. Our research significantly contributes to the understanding of human problem-solving strategies when confronted with computationally challenging problems. It unearths valuable insights regarding the potential for harnessing human intuition and expertise in addressing complex computational challenges. Through the integration of machine learning methodologies and intuitive visualizations, this work advances our comprehension of the approaches individuals employ to excel in solving computationally intricate problems.
Reliability Evaluation and Fault Tolerant Design for KLL Sketches

Quantile estimation is a fundamental task in Big Data analysis. In order to achieve high-speed estimation with low memory consumption, especially for streaming Big Data processing, data sketches which provide approximate estimates at low overhead are commonly used, and the Karnin-Lang-Liberty (KLL) sketch is one of the most popular options. However, soft errors in KLL memory may significantly degrade estimation performance. In this article, the influence of soft errors on the KLL sketch is considered for the first time. First, the reliability of KLL to soft errors is studied through theoretical analysis and fault injection experiments. The evaluation results show that the errors in the KLL construction phase may cause a large deviation in the estimated value. Then, two protection schemes are proposed based on a single parity check (SPC) and on the incremental property (IP) of the KLL memory. Further evaluation shows that the proposed schemes can significantly improve the reliability of KLL, and even remove the effect SEUs on the highest bits. In particular, the SPC scheme that requires additional memory, provides better protection for middle bit positions than the IP scheme which does not introduce any memory overhead.
Towards End-to-End Verifiable Online Voting: Adding Verifiability to Established Voting Systems

Online voting for independent elections is generally supported by trusted election providers. Typically these providers do not offer any way in which a voter can verify their vote, and hence the providers are trusted with ballot privacy and in ensuring correctness. Despite the desire to offer online voting for political elections, this lack of transparency and verifiability is often seen as a significant barrier to the large-scale adoption of online elections. Adding verifiability to an online election increases transparency and integrity, as well as allowing voters to verify that the vote they cast has been recorded correctly and included in the tally. However, replacing existing online systems with those that provide verifiable voting requires new algorithms and code to be deployed, and this presents a significant business risk to commercial election providers, as well as the societal risk for official elections selecting for public office. In this paper we present the first step in an incremental approach which minimises the business risk but demonstrates the advantages of verifiability, by developing an implementation of key elements of a Selene-based verifiability layer and adding it to an operational online voting system. Selene is a verifiable voting protocol that publishes votes in plaintext alongside a voter's tracker. These trackers enable voters to confirm that their votes have been captured correctly by the system, such that the election provider does not know which tracker has been allocated to which voter. This results in a system where even a “dishonest but cautious” election authority running the system cannot be sure of changing the result in an undetectable way, and hence gives stronger guarantees on the integrity of the election than were previously present. We explore the challenges presented by adding a verifiability layer to an operational system. The system was used in two initial trials conducted within real contested elections. We conclude by outlining the further steps in the road-map towards the deployment of a fully trustworthy online voting system.
A Transformer-Based End-to-End Automatic Speech Recognition Algorithm

End-to-End (E2E) automatic speech recognition (ASR) becomes popular recent years and has been widely used in many applications. However, current ASR algorithms are usually less effective when applied in specific applications with terminologies such as medical and economic fields. To address this issue, we propose a powerful Transformer based ASR decoding method for beam searching, called soft beam pruning algorithm (SBPA). SBPA can dynamically adjust the width of beam search. Meanwhile, a prefix module (PM) is added to access the contextual information and avoid removing professional words in the beam search. Combining SBPA and PM, the proposed ASR can achieve promising recognition performance on professional terminologies. To verify the effectiveness, experiments are conducted on real-world conversation data with medical terminology. It is shown that the proposed ASR achieved significant performance on both professional and regular words.
A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing Demands

Abstract—Automatic Modulation Classification (AMC) is a technique used to identify signal modulations in applications like IoT devices, cognitive radar, software-defined radio, and electronic warfare. These applications could be applied to IoT devices. With future wide applications of IoT devices, AMC algorithms need to be more compact yet suitable for embedded devices with limited resources and remain acceptable accuracy. Although current AMC algorithms deliver high accuracy, they require substantial computing power, making them unsuitable for IoT devices. This paper introduces the novel Chessboard-based Automatic Modulation Classification (CAMC) algorithm, which has dramatically high accuracy. Test results reveal that CAMC achieves 99%* accuracy under a 3dB SNR condition and 100% above 5dB SNR. Meanwhile, this algorithm is scalable and demands less computing power. It offers better accuracy results compared to state-of-the-art AMC algorithms, classifying mainstream modulations in IoT devices like BPSK, QPSK, 8PSK, and 16QAM, but requires less computing power than existing algorithms. Additionally, CAMC is hardware-friendly due to its inherent parallelism and scalability.
Examining the 'hyper' status of Varèse's 'prism'

This paper offers an interpretation of the composition Hyperprism by Edgard Varèse. It provides an explanation of the work's possible meanings, placing emphasis on the investigation of the "hyper" status of the work as a "prism". Relying on the composer's poetics and bearing in mind his scientific approach to sound and sound articulation, this paper proposes the interpretation of the meaning of Hyperprism from the perspectives of geometry, optics, and mineralogy.
Enacting musical maquettes: A cognition-inspired compositional approach

This paper is a practice-led case study on Fred Lerdahl's "Cognitive Constraints on Compositional Systems". The model attempts to define an artificial compositional grammar in terms of a "universal listening syntax" based on Lerdahl's co-authored A Generative Theory of Tonal Music. Through demonstrating the practical application of the constraints, the author reflects on the model's usefulness in light of the contemporary compositional context. Notably, the theory presents abstracted pitch and rhythmic material as an aesthetically neutral syntax, therefore it can only provide stylistically ambiguous infrastructures akin to a musical maquette that needs to be further enacted at the composer's discretion.
Assessing the Solvency of Virtual Asset Service Providers: Are Current Standards Sufficient?

Entities like centralized cryptocurrency exchanges fall under the business category of virtual asset service providers (VASPs). As any other enterprise, they can become insolvent. VASPs enable the exchange, custody, and transfer of cryptoassets organized in wallets across distributed ledger technologies (DLTs). Despite the public availability of DLT transactions, the cryptoasset holdings of VASPs are not yet subject to systematic auditing procedures. In this paper, we propose an approach to assess the solvency of a VASP by cross-referencing data from three distinct sources: cryptoasset wallets, balance sheets from the commercial register, and data from supervisory entities. We investigate 24 VASPs registered with the Financial Market Authority in Austria and provide regulatory data insights such as who are the customers and where do they come from. Their yearly incoming and outgoing transaction volume amount to 2 billion EUR for around 1.8 million users. We describe what financial services they provide and find that they are most similar to traditional intermediaries such as brokers, money exchanges, and funds, rather than banks. Next, we empirically measure DLT transaction flows of four VASPs and compare their cryptoasset holdings to balance sheet entries. Data are consistent for two VASPs only. This enables us to identify gaps in the data collection and propose strategies to address them. We remark that any entity in charge of auditing requires proof that a VASP actually controls the funds associated with its on-chain wallets. It is also important to report fiat and cryptoasset and liability positions broken down by asset types at a reasonable frequency.
FPGA-based Hardware Architecture For Montgomery Modular Exponentiation Algorithm*

With the explosive growth in data communications and internet services, public key cryptography such as RSA is crucial for information privacy and security. As an important research topic, RSA can support confidentiality, authentication, data integrity and non-reputation for networks. In the process of the RSA algorithm, modular arithmetic is a fundamental operator, which requires a long time to execute, especially since the size of modular is at least 256 bits for long-term security. Montgomery Modular Multiplication (MMM) algorithm and Montgomery Modular Exponentiation (MME) algorithm are the two most popular solutions to implement modular arithmetic efficiently. But the execution time of modular arithmetic is still too long under some of the conditions. To further accelerate the modular arithmetic, this paper proposes a FPGA-based hardware architecture for MMM and MME algorithm, in which the size of the modular is 256 bits. The delay of the adder’s long carry propagation in MMM algorithm is eliminated by using carry-lookahead adder. In addition, the architexture contains a finite state machine to implement the MME algorithm and repeatedly execute the MMM algorithm. To verify the performance of the hardware architecture, the hardware design is mapped to a special device. And execution time is compared between FPGA and CPU. The result shows hardware implementation is executed 1752.5 and 628.9 times faster than the software implementation for MMM algorithm and MME algorithm respectively.
Lightweight Saliency Detection Network-based Target Recognition Method for Livestock Carcass Chopping Robot

Existing methods for salient object detection mainly concentrate on how to improve the prediction quality. The computational overhead and hardware requirements of the model are ignored. The result is that it cannot be effectively applied in practice on industrial equipment. To solve this problem, a lightweight saliency detection algorithm for livestock carcass chopping robot target detection is proposed in this paper. First, a redundant feature generation module is introduced in the encoding stage. To ensure that richer salient features are extracted with fewer parameters. Secondly, a selection residual module is introduced in the decoding stage. Compress the number of channels while maximizing the use of multi-scale features. Finally, a hybrid distance loss function is designed in this paper to facilitate the model to focus more on the overall salient regions. Experimental results on public datasets and self-made livestock carcass dataset showed that. The proposed method uses a smaller number of parameters and achieves higher detection accuracy than other lightweight saliency detection models. And the target recognition of livestock carcasses can be effectively performed on the self-made livestock carcass dataset.
TBANet: Three Branches with Asymmetric Convolutions and Pooling for Real-time Semantic Segmentation

Computational pressure limits the application of semantic segmentation in road scenes. We propose the lightweight network TBANet that makes a trade-off between accuracy and speed to deal with this problem. TBANet takes advantage of three branches (a fine-grained branch, an adaptive branch, and a coarse-grained branch) to obtain detailed and abstract information and adaptively supplement useful features for semantic segmentation. In order to exploit the information in the horizontal and vertical directions in images and remove redundant information to speed up, we propose the Asymmetric Pyramid Pooling Module (APPM) and the Asymmetric Factorized Module (AFM). TBANet with only 1M parameters processes 512×1024 images at 143 FPS and achieves 71.6% mIoU on the Cityscapes test set. TBANet stands out from other state-of-the-art networks in terms of accuracy and speed.
A Fish Image Segmentation Method Based on Image Enhancement and U<sup>2</sup>-Net

Underwater target monitoring is of great importance for the aquaculture industry, and using deep learning algorithms to assist in monitoring is a feasible and efficient approach. This paper implements a fish segmentation method in low-contrast, color-biased underwater environments using Auto-MSRCR image enhancement and U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net semantic segmentation algorithm. The experimental results show that this method can accurately segment the mask information of underwater fish and reduce the impact caused by the underwater environment. Compared with the model without image enhancement, the accuracy of the model trained by this method is improved by about 5%.
Violence Content Detection in Videos

Social networks are not safe, because of the amount of violent content shared on these platforms. That’s why, filtering such content using a violence detection system powered by Artificial Intelligence is necessary and has become a growing research domain. In this paper, we propose a deep-learning-based approach to address this issue. We use a two layered model: First, a deep representation-based model that uses transfer learning concept to recognize violent content in a video. Second a text classifier to detect verbal violence using the audio cue. The result reports show that our approach is outperforming state-of-the art accuracies by learning most discriminating features achieving 90% as accuracy on the test set for physical violence detection and 89% for verbal violence detection.
Improved Mayfly Algorithm Based on Chaos Mapping and Attenuation Factor

This study presents an enhanced version of the mayfly method in order to address its deficiencies, such as insufficient population variety and slow convergence time. First, in the population initialization phase, tent mapping is introduced to increase population diversity so that the initial population is evenly distributed in the search space. Second, the reverse learning mechanism is introduced to update the initial population, and the algorithm population is enhanced based on the tent mapping. Diversity facilitates subsequent iterative optimization of the algorithm. Then, the attenuation factor is introduced in the position update of the population individual so that the individual’s position can be updated adaptively with each iteration, thereby enhancing the algorithm’s convergence speed and solution capability. The performance of the updated algorithm is then assessed using 15 benchmark functions, and the rank sum test is conducted to validate the strategy’s viability and efficacy. Experiments demonstrate that the modified algorithm improves solution precision and convergence rate more than the original technique.
Disease Text Classification Model Based on Knowledge Graph and Neural Network

Accurate classification of medical disease text plays an influential role in promoting the development of medical informatization. In view of the sparseness, real-time and non-standard characteristics of disease text, this study proposes a disease text classification model that combines knowledge graph and neural network. The model extracts relevant disease knowledge from the disease text through a medical domain knowledge graph. Meanwhile, it combines the performances of convolutional neural network, recurrent neural network and attention mechanism to obtain semantic features from disease text. In addition, the introduction of attention mechanism in BiLSTM (Bi-directional long short-term memory network) can improve the efficiency of extracting effective information from disease text. Finally, we can obtain a classification model trained with a combination of semantics and knowledge. Experimental results show that the proposed model achieves more remarkable classification performance compared to other disease text classification models.
Indeterminate-oriented to rational-oriented: John Cage, paper imperfections, and graphic notations

Around 1904, the sociologist Max Weber observed the lifestyle of Protestants and proposed a theory of rationality focusing on the relationship between individuals' actions and their choices, value standards, and purposes. He then extended this theory to music, positing that the rational features of music are structural, systematic, intentional, functional, and interactive. However, these reasonable features appeared unreasonable when music entered its avant-garde phase-when music became unpredictable, chaotic, and open. Does this mean that avant-garde music was no longer rational? To unpack this question, this paper applies the theory of rationality to John Cage's Solo for Piano (1957-58) and delves into the compositional process to present rational features within indeterminacy. In the compositional process, Cage employed the graphic compositional system, including a drawing process and a means of translation. With this system, we may discover the process of rationalization, meaning that when chance-oriented material gradually transforms into the value-/purposive-oriented material-from paper imperfections to points, from points to notes-it presents a rationalization of the compositional material. In short, this paper applies the theory of rationality to Solo for Piano to discover its rational features and dissects the transformation of the compositional material to present rationalization.
Inference of Ensemble Method Using Homomorphic Encryption

According to the recent amendment of data-related bills, active efforts are being made to conduct big data analysis for hyper-personalization services. Hyper-personalization services involve several data integration, including external data. However, it also inherently possesses limitations in usage due to concerns about private information leaks, which can lead to damages to individuals and businesses and unnecessary societal losses. One approach to mitigate such security risks is to use homomorphic encryption, which protects private information. In particular, homomorphic encryption fundamentally prevents private information leakage during data integration and analysis processes and retains original data. In this study, we develop an algorithm for performing inference using an ensemble learning method with homomorphic encryption and apply it to real financial complaint data. We demonstrate the equivalence between scores obtained from random forest and XGBoost in plaintext and those derived from the homomorphic ciphertext. Empirical results show that the algorithm achieves inference computation times in approximately 0.4 seconds, confirming the potential utility of the tree-based ensemble learning method.