Reviewerly/exHarmony huggingface dataset:
Enhancing Scalability with Payment Requests Aggregation in Lightning Network

Lightning Network is the promising off-chain pay-ment solution that resolves the performance and scalability issues on Bitcoin, which is the trustless e-cash system based on blockchain. However, Lightning Network provides a limited scalability, bound by channel liquidity and payment request bandwidth (currently 483 for each direction), which is too small for practical applications. In this paper, we propose a scheme aggregating payment requests to extend the bandwidth. The proposed scheme main-tains a two-layer structure instead of the original one with only one commitment transaction layer. The scheme aggregates a set of conditional outputs (i.e., in-flight payment requests) into a single aggregated output in the commitment transaction. The conditional outputs used for the aggregation are moved to the second-layer commitment transaction, termed the Stash transaction. We also propose another condition for redeeming the aggregated payment requests altogether with the aggregated redeeming transaction to reduce the on-chain processing fees when the payment channel is unilaterally closed. We evaluate the increased bandwidth of the proposed scheme and the reduced fees when the proposed scheme adopts the aggregation strategy of distributing payment requests into sev-eral aggregated outputs to increase the probability of making aggregated redeeming transactions. The scheme increases the bandwidth to accommodate more than l00K payment requests for each direction. The overall on-chain transaction fees are reduced by 44.8% when a few payment requests are within the aggregated output under a high success ratio of payment requests. One disadvantage of the proposed scheme is that it increases the number of signings for every channel update, which is also evaluated.
Mosaic - A Blockchain Consensus Algorithm Based on Random Number Generation

Consensus algorithms can be considered as the core of blockchain technology. Different algorithms provide various advantages and disadvantages to the platform, which can cause the variation in the aspects of scalability, reliability, security, and performance. Deploying a blockchain platform with a consensus algorithm that is not suitable for the situated scenario may lead to low efficiency, waste of energy, unfairness and more. As more businesses are trying to introduce blockchain into their system, there is a demand for selecting consensus algorithms that are suitable for a private chain. Most of the current consensus algorithms are Voting-based or PBFT-based. However, Voting-based algorithms may suffer much from leader-crash, while PBFT-based algorithms may experience network congestion when implemented in a huge network due to its broadcast-like confirmation steps. In this paper, we aim to propose a new type of consensus algorithm based solely on Random Number Generation, called Mosaic. The selection is not only due to its low computational cost compared to cryptographically proof-based algorithms, but also because of its acceptable unpredictability that makes the election fairer. In the evaluation, we demonstrate that Mosaic is more efficient than PBFT-based schemes in large-scale while also providing fault tolerance compared to Raft.
An Encoder-Decoder Network with Residual and Attention Blocks for Full-Face 3D Gaze Estimation

This paper proposes a novel end-to-end network to improve the accuracy of gaze estimation task with full-face image as input. We first explored the possibility of using the encoder-decoder network to reconstruct the input face image, then we used U-Net with residual blocks to retain eyes features hidden in high resolution feature map layers, which are often lost during down-sampling and convolution layers. Finally, we applied spatial and channel-wise attention blocks to our model to better consider the relations among different regions globally and enhance the contribution of valuable gaze-related regions. We conducted experiments on the ETH-XGaze dataset. The results turned out that our proposed model is very competitive compared with existing state-of-the-art methods for person-independent gaze estimation.
Advanced Ledger: Supply Chain Management with Contribution Trails and Fair Reward Distribution

We have several issues in most current supply chain management systems. Consumers want to spend money on environmentally friendly products, but they are seldomly informed of the environmental contributions of the suppliers. Meanwhile, each supplier seeks to recover the costs for the environmental contributions to re-invest them into further contributions. Instead, in most current supply chains, the reward for each supplier is not clearly defined and fairly distributed. To address these issues, we propose a supply-chain contribution management platform for fair reward distribution called ‘Advanced Ledger.’ This platform records suppliers' environ-mental contribution trails, receives rewards from consumers in exchange for trail-backed fungible tokens, and fairly distributes the rewards to each supplier based on the contribution trails. In this paper, we overview the architecture of Advanced Ledger and 11 technical features, including decentralized autonomous organization (DAO) based contribution verification, contribution concealment, negative-valued tokens, fair reward distribution, atomic rewarding, and layer-2 rewarding. We then study the requirements and candidates of the smart contract platforms for implementing Advanced Ledger. Finally, we introduce a use case called ‘ESG token’ built on the Advanced Ledger architecture.
Hand Gesture Recognition Based on Keypoint Vector

Human-computer interaction (HCI) is usually associated with using popular input devices such as a mouse or keyboard. In other cases hand gestures can actually be useful for human-computer interaction when hand gestures are needed to make the game controls more interesting. There are three basic controls as input mouse: move, click, and drag. Hand gestures and hand shape are different for each person. This becomes a problem during automatic recognition. Recent research has proven the success of the Deep Neural Network (DNN) for representation and high accuracy in hand gesture recognition. DNN algorithms can study complex and nonlinear relationships between features by applying multiple layers. This paper proposes hand feature based on the normalized keypoint vector using DNN. The model was trained on 2250 hand datasets which were divided into 3 classes to identify the mouse movement. The network design uses multilayer with neuron sizes (13, 12, 15, 14) with 500 epochs and achieves the best accuracy of 98.5% for normalized features. The important work in this research is the use of keypoint vector from hand gestures as features to be fed to the DNN to achieve good accuracy.
An Integration of Deep Learning Approach for prediction of Consumer Reviews using Sentiment Analysis

There is a massive increase in consumer reviews posted on social media, both in terms of the volume of evaluations and their relevance. This is why sentiment analysis is becoming increasingly popular in a wide range of contexts, including politics, social issues, and current events. For a number of corporate applications, the analysis of social media user reviews is critical. Because social media makes it so simple to obtain public sentiment from around the world, this method is better suited for sentiment mining. With the hybrid technique of two deep constructions, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTTM), this article examines airline industry service and movie reviews (LSTM). The local feature selection power of CNN has been well documented, whereas the sequential text analysis prowess of LSTM has been well documented as well. In sentiment analysis, the suggested CNN-LSTM model primarily aims to achieve two goals. First, it can easily be scaled up to handle large amounts of social media data, and second, unlike traditional machine learning algorithm’s, it is not domain-specific. Two review datasets from different fields have been used to train a perfect that can handle all kinds of dependences in a post. This ensemble model outperforms other deep learning algorithms in accuracy and other limits, as shown by the results in the experiments.
Contrastive hashing with vision transformer for image retrieval

Hashing techniques have attracted considerable attention owing to their advantages of efficient computation and economical storage. However, it is still a challenging problem to generate more compact binary codes for promising performance. In this paper, we propose a novel contrastive vision transformer hashing method, which seamlessly integrates contrastive learning and vision transformers (ViTs) with hash technology into a well-designed model to learn informative features and compact binary codes simultaneously. First, we modify the basic contrastive learning framework by designing several hash layers to meet the specific requirement of hash learning. In our hash network, ViTs are applied as backbones for feature learning, which is rarely performed in existing hash learning methods. Then, we design a multiobjective loss function, in which contrastive loss explores discriminative features by maximizing agreement between different augmented views from the same image, similarity preservation loss performs pairwise semantic preservation to enhance the representative capabilities of hash codes, and quantization loss controls the quantitative error. Hence, we can facilitate end-to-end joint training to improve the retrieval performance. The encouraging experimental results on three widely used benchmark databases demonstrate the superiority of our algorithm compared with several state-of-the-art hashing algorithms.
An Improved Directed Crossover Genetic Algorithm Based on Multilayer Mutation

In order to solve the shortcomings of traditional genetic algorithms in image matching in terms of computational speed and matching accuracy, this paper proposes a directed crossover genetic matching algorithm (DCGA) based on multilayer variation. The algorithm differs from the traditional genetic algorithm (GA) in which the crossover strategy is improved and a multilayer adaptive variation operator is introduced. The crossover operation selects a certain proportion of spherical individuals from each generation as the evolutionary target, and the rest of the individuals evolve towards it in each dimension; the variation operation stratifies the population and adopts different adaptive variation methods for different layers. Avoiding the shortcomings of traditional genetic algorithms that tend to fall into local extremes, thus alleviating premature convergence, improves the search performance of the algorithm. The algorithm proposed in this paper is compared with the commonly used genetic algorithm by testing the effect of the function and tested practically in template matching. The experimental results show that the improved genetic algorithm has better convergence speed and search accuracy.
Homoglyph Attack Detection Model Using Machine Learning and Hash Function

Phishing is still a major security threat in cyberspace. In phishing, attackers steal critical information from victims by presenting a spoofing/fake site that appears to be a visual clone of a legitimate site. Several Unicode characters are visually identical to ASCII characters. This similarity in characters is generally known as homoglyphs. Malicious adversaries utilize homoglyphs in URLs and DNS domains to target organizations. To reduce the risks caused by phishing attacks, effective ways of detecting phishing websites are urgently required. This paper proposes a homoglyph attack detection model that combines a hash function and machine learning. There are two phases to the model approach. The machine was being trained during the development phase. The deployment phase involved deploying the model with a Java interface and testing the outcomes through actual user interaction. The results are more accurate when the URL is hashed, as any little changes to the URL can be recognized. The homoglyph detector can be developed as a stand-alone software that is used as the initial step in requesting a webpage as it enhances browser security and protects websites from phishing attempts. To verify the effectiveness, we compared the proposed model on several criteria to existing phishing detection methods. By using the hash function, the proposed security features increase the overall security of the homoglyph attack detection in terms of accuracy, integrity, and availability. The experiment results showed that the model can detect phishing sites with an accuracy of 99.8% using Random Forest, and the hash function improves the accuracy of homoglyph attack detection.
Generalization of Linear and Area Features Incorporating a Shape Measure

This article elaborates on the quality issue in cartographic generalization of linear and area features focusing on the assessment of shape preservation. Assessing shape similarity in generalization is still a topic where further research is required. In the study presented here, shape description and matching techniques are investigated and analyzed, a procedure for choosing generalization parameters suitable for line and area features depiction is described and a quality model is developed for the assessment and verification of the generalization results. Based on the procedure developed, cartographers will be confident that the generalization of linear and area features is appropriate for a specific scale of portrayal fulfilling on the same time a basic requirement in generalization, that of shape preservation. The results of the procedure developed are based on the processing and successful generalization of a large number of different line and area features that is supported by a software environment developed in Python programming language.
HW-Flow-Fusion: Inter-Layer Scheduling for Convolutional Neural Network Accelerators with Dataflow Architectures

Energy and throughput efficient acceleration of convolutional neural networks (CNN) on devices with a strict power budget is achieved by leveraging different scheduling techniques to minimize data movement and maximize data reuse. Several dataflow mapping frameworks have been developed to explore the optimal scheduling of CNN layers on reconfigurable accelerators. However, previous works usually optimize each layer singularly, without leveraging the data reuse between the layers of CNNs. In this work, we present an analytical model to achieve efficient data reuse by searching for efficient scheduling of communication and computation across layers. We call this inter-layer scheduling framework HW-Flow-Fusion, as we explore the fused map-space of multiple layers sharing the available resources of the same accelerator, investigating the constraints and trade-offs of mapping the execution of multiple workloads with data dependencies. We propose a memory-efficient data reuse model, tiling, and resource partitioning strategies to fuse multiple layers without recomputation. Compared to standard single-layer scheduling, inter-layer scheduling can reduce the communication volume by 51% and 53% for selected VGG16-E and ResNet18 layers on a spatial array accelerator, and reduce the latency by 39% and 34% respectively, while also increasing the computation to communication ratio which improves the memory bandwidth efficiency.
Cloze-Style Data Augmentation for Few-Shot Intent Recognition

Intent recognition aims to identify users’ potential intents from their utterances, which is a key component in task-oriented dialog systems. A real challenge, however, is that the number of intent categories has grown faster than human-annotated data, resulting in only a small amount of data being available for many new intent categories. This lack of data leads to the overfitting of traditional deep neural networks on a small amount of training data, which seriously affects practical applications. Hence, some researchers have proposed few-shot learning should address the data-scarcity issue. One of the efficient methods is text augmentation, which always generates noisy or meaningless data. To address these issues, we propose leveraging the knowledge in pre-trained language models and constructed the cloze-style data augmentation (CDA) model. We employ unsupervised learning to force the augmented data to be semantically similar to the initial input sentences and contrastive learning to enhance the uniqueness of each category. Experimental results on CLINC-150 and BANKING-77 datasets show the effectiveness of our proposal by its beating of the competitive baselines. In addition, we conducted an ablation study to verify the function of each module in our models, and the results illustrate that the contrastive learning module plays the most important role in improving the recognition accuracy.
Influencing the influencers: Regulating the morality of online conduct in Indonesia

Abstract Indonesia boasts a lively influencer scene. These influencers promote various products and messages, including political messages, to their followers for commercial gain, and have been particularly active during the coronavirus disease 2019 (COVID‐19) pandemic. Influencers in Indonesia are potentially subject to state regulation, particularly via the controversial Electronic Transactions and Information Law (ITE Law) which criminalizes digital communications that “breach morality,” defame the good name of another, or spread misleading information resulting in consumer losses. In addition, influencers face a risk of community‐led regulation, via an online backlash, should they transgress perceived moral boundaries. In this article, we present and analyze a series of case studies where influencer behavior has attracted regulatory responses. These cases illustrate the interactions among regulatory actors and also where and how the lines for acceptable influencer moral standards of behavior are being drawn.
L2DART: A Trust Management System Integrating Blockchain and Off-Chain Computation

The blockchain technology has been gaining an increasing popularity for the last years, and smart contracts are being used for a growing number of applications in several scenarios. The execution of smart contracts on public blockchains can be invoked by any user with a transaction, although in many scenarios there would be the need for restricting the right of executing smart contracts only to a restricted set of users. To help deal with this issue, this article proposes a system based on a popular access control framework called RT, Role-based Trust Management, to regulate smart contracts execution rights. The proposed system, called Layer 2 DecentrAlized Role-based Trust management (L2DART), implements the RT framework on a public blockchain, and it is designed as a layer-2 technology that involves both on-chain and off-chain functionalities to reduce the blockchain costs while keeping blockchain auditability, i.e., immutability and transparency. The on-chain costs of L2DART have been evaluated on Ethereum and compared with a previous solution implementing on-chain all the functionalities. The results show that the on-chain costs of L2DART are relatively low, making the system deployable in real-world scenarios.
MBDSNet: Automatic segmentation of kidney ultrasound images using a multi-branch and deep supervision network

Ultrasound imaging has been widely used in the screening of kidney diseases. The segmentation of the kidney in ultrasound images can aid in the diagnosis of clinical diseases. However, segmenting the kidney from ultrasound images is challenging, due to the effects of heterostructure, image quality, and other factors. In this paper, a multi-branch and deep supervision network (MBDSNet) is proposed to segment the kidney from ultrasound images. Specifically, the architecture consists of two parts: multi-branch encoding network and refined decoding network. The multi-branch encoding network consists of a multi-scale feature input pyramid and three branch encoders. The multi-branch encoding network can reduce the sensitivity of the network to the input data through the multi-scale feature inputs pyramid, and utilize the information exchange between the three branch encoders can improve the generalization ability of the network. In the refined decoding network, the introduction of the side-out deep supervision module and the kidney boundary detection module can further guide the network to effectively segment the kidney with complete contour. Objective comparisons with several state-of-the-art segmentation methods on six commonly used metrics are performed. The method proposed in this paper has achieved the best results in terms of evaluation indicators and segmentation results.
Organizational Readiness/Maturity Considerations for Blockchain Adoption

Digital transformation involves new concepts, radical innovation, and radical organizational change across multiple organizational dimensions. Blockchains can be considered a form of digital transformation for organizations. An aspect of the radical nature of blockchains flows from the capabilities it can provide for trustworthy transactions between organizations. This creates opportunities for new business models by disintermediation of some parties to traditional transaction flows in the same industry or supply chain. Multiple parties have to agree to adopt the new style of transactions. Scorecards and metrics for multiparty technology adoption have additional scope that metrics within a single organization do not. Organizational readiness and maturity metrics for effectively utilizing blockchains have to address the broad range of business considerations that management should consider when evaluating opportunities for digital transformation via blockchain.
Effective methods for email classification: Is it a business or personal email?

With the steady increase in the number of Internet users, email remains the most popular and extensively used communication means. Therefore, email management is an important and growing problem for individuals and organizations. In this paper, we deal with the classification of emails into two main categories, Business and Personal. To find the best performing solution for this problem, a comprehensive set of experiments has been conducted with the deep learning algorithms: Bidirectional Long-Short Term Memory (BiLSTM) and Attention-based BiLSTM (BiLSTM+Att), together with traditional Machine Learning (ML) algorithms: Stochastic Gradient Descent (SGD) optimization applied on Support Vector Machine (SVM) and Extremely Randomized Trees (ERT) ensemble method. The variations of individual email and conversational email thread arc representations have been explored to reach the best classification generalization on the selected task. A special contribution of this paper is the extraction of a large number of additional lexical, conversational, expressional, emotional, and moral features, which proved very useful for differentiation between personal and official written conversations. The experiments were performed on the publicly available Enron email benchmark corpora on which we obtained the State-Of-the-Art (SOA) results. As part of the submission, we have made our work publicly available to the scientific community for research purposes.