Reviewerly/exHarmony huggingface dataset:
DAG-Based Efficient Parallel Scheduler for Blockchains: Hyperledger Sawtooth as a Case Study

Blockchain technology is a distributed, decentralized, and immutable ledger system. It is the platform of choice for managing smart contract transactions (SCTs). Smart contracts are pieces of code that capture business agreements between interested parties and are commonly implemented using blockchains. A block in a blockchain contains a set of transactions representing changes to the system and a hash of the previous block. The SCTs are executed multiple times during the block production and validation phases across the network. In most of the existing blockchains, transactions are executed sequentially. In this work, we propose a parallel direct acyclic graph (DAG) based scheduler module for concurrent execution of SCTs. This module can be seamlessly integrated into the blockchain framework, and the SCTs in a block can be executed efficiently, resulting in higher throughput. The dependencies among the SCTs of a block are represented as a DAG data structure which enables parallel execution of the SCTs. Furthermore, the DAG data structure is shared with block validators, allowing resource conservation for DAG creation across the network. To ensure secure parallel execution, we design a secure validator capable of validating and identifying incorrect DAGs shared by malicious block producers. For evaluation, our framework is implemented in Hyperledger Sawtooth V1.2.6. The performance across multiple smart contract applications is measured for the various schedulers. We observed that our proposed executor exhibits a 1.58 times performance improvement on average over serial execution.
Development of a Reflection Facilitation System with Gamification Adapted to Community Activities

After the COVID-19 pandemic in 2020, many schools promoted ICT-based learning activities. The authors developed a learning-data collected system for children to promote self-directed learning. In this paper, the authors proposed a reflection facilitation system with gamification adapted to the community activities for children. The research question of this study is if reflection facilitation adapted to the orientation of the children has a positive impact on their next self-directed action. In the community activity in 2022, the subject experiment was conducted with the reflection facilitation system. As a result, it was confirmed that the achievement degree increased with the number of activities.
Insights from the Uncanny Valley: Gender(Sex) Differences in Avatar Realism and Uncanniness Perceptions

Two core factors influence the perception of avatars. On one side are the developers who are concerned with building avatars and in some cases pushing the boundaries of the realism. These developers are constrained by the resources available to them that allow them to produce the optimal avatar with the equipment, time, and skills available to them. On the other side are users who engage with avatars who are directly affected by the choices the developers have made. Inside the interaction between these sides is the avatar itself, whose appearance, level of realism and fundamental characteristics like a perceived gender(sex) can influence the user’s perception of that avatar. Despite the large amount of research dedicated to understanding the perception of avatars, many gaps in our understanding remain. In this work, we aim to contribute to further understanding one of these gaps by investigating the potential role of gender(sex) in the perception of avatar realism and uncanniness. We add to this discussion by presenting the results of an experiment where we evaluated realism and uncanniness perceptions by presenting a set of avatars to participants (n = 2065). These avatars are representative of those used in simulation and training contexts, from publicly available sources, and have varying levels of realism. Participants were asked to rank these avatars in terms of their realism and uncanniness perceptions to determine whether the gender(sex) of the participant influences in these perceptions. Our findings show that the gender(sex) of a participant does affect the perception of an avatar’s realism and uncanniness levels.
TrainBF: High-Performance DNN Training Engine Using BFloat16 on AI Accelerators

Training deep neural networks (DNNs) with half-precision floating-point formats is widely supported on recent hardware and frameworks. However, current training approaches using half-precision formats neither obtain the optimal throughput due to the involvement of single-precision format nor achieve state-of-the-art model accuracy due to lower numerical digits. In this work, we present a new DNN training engine, named TrainBF, which leverages a typical half-precision format BFloat16 to maximize training throughput while ensuring sufficient model accuracy. TrainBF deploys BFloat16 across the entire training process for best throughput and improves model accuracy by introducing three proposed normalization techniques. TrainBF is also lightweight by only applying these normalization techniques to the layers that are most critical to model accuracy. Furthermore, TrainBF implements a parallel strategy that parallelizes the execution of operators in DNN training to make use of the spare memory space saved by half-precision for better throughput. Evaluating with six common DNN models and compared with the state-of-the-art mixed-precision approach, TrainBF achieves competitive model accuracy with an average throughput speedup of 1.21 $$\times $$ , 1.74 $$\times $$ , and 1.16 $$\times $$ on NVIDIA A100 GPU, AMD MI100 GPU, and an emerging AI accelerator SambaNova, respectively.
Model-Agnostic Federated Learning

Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but neglected those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only support DNNs reinforces this problem. To address the lack of non-DNN-based FL solutions, we propose MAFL (Model-Agnostic Federated Learning). MAFL merges a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any machine learning model, allowing exploration of FL beyond DNNs. We test MAFL from multiple points of view, assessing its correctness, flexibility, and scaling properties up to 64 nodes of an HPC cluster. We also show how we optimised OpenFL achieving a 5.5 $$\times $$ speedup over a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.
MetaLive: Meta-Reinforcement Learning Based Collective Bitrate Adaptation for Multi-Party Live Streaming

Multi-party interactive live video streaming applications have attracted millions of daily active users and are anticipated a blooming market in the next few years. A fundamental research problem in live streaming is bitrate coordination, which selects the proper upload/download bitrate for multiple participants in the system to maximize the users’ quality of experience (QoE). The existing bitrate adaptation methods fail to achieve optimal performance across a broad set of network conditions with conflict QoE objectives, and lack of the ability of adaptation to dynamic user requirements. In this paper, we proposed a novel meta-reinforcement learning based solution called MetaLive for multi-party live video streaming bitrate adaptation. The proposed framework formulates the bitrate coordination problem as a reinforcement learning task, and introduces a meta-training method to train an agent to learn to carry out various complex tasks from historical experience, and generate bitrate adaptation policies to maximize expected QoEs in diverse environments. We implement MetaLive based on an emulation platform, and use real-world network traces to evaluate its performance. Extensive experiments show that MetaLive achieves the best comprehensive QoE compared with the state-of-the-arts in a variety of network scenarios.
FedGM: Heterogeneous Federated Learning via Generative Learning and Mutual Distillation

Federated learning is a distributed machine learning that enables models to aggregate on the server after local training to protect privacy. However, user heterogeneity presents a challenge in federated learning. To address this issue, some recent work has proposed using knowledge distillation. But the application of knowledge distillation in federated learning is dependent on the proxy dataset, which can be difficult to obtain in practice. Additionally, the simple average aggregation method of model parameters may fail to achieve a global model with good generalization performance, and may also lead to potential privacy breaches. To tackle these issues, we propose FedGM, a data-free federated knowledge distillation method that combines generative learning with mutual distillation. FedGM addresses user heterogeneity while also protecting user privacy. We use a conditional generator to extract global knowledge to guide local model training and build a proxy dataset on the server-side to perform mutual distillation. Extensive experiments on benchmark datasets show that FedGM outperforms state-of-the-art approaches in terms of generalization performance and privacy protection.
Basic Principles of Cross-Lingual Models

When we start working across languages, we need to determine ways of measuring similarity of entities (such as a word, a phrase, a sentence or a link between entities) within each language and across languages. Modern approaches usually rely on the Vector Space Model (VSM), which uses a numerical vector $$x$$ of a specified dimensionality $$D$$ to represent any entity. The entities are similar when their vectors are not too far apart.
Induction of Bilingual Dictionaries

The aim of the Bilingual Lexicon Induction (BLI) task is to produce a bilingual lexicon using a pair of comparable corpora and either a small set of seed translations (a supervised setting) or no seeds at all (an unsupervised setting). A traditional bilingual dictionary usually offers a structure of senses and conditions for their translations, as well as POS tags for disambiguation. In contrast to this task, building bilingual lexicons as the aim for the BLI task involves a number of simplifications.
Conclusions and Future Research

In the beginning of the 2000s the use of comparable corpora was on the margins of NLP research. Existing MT systems were nearly always based on fully parallel corpora, while NLP applications were mostly built separately in each language without the advantages of cross-lingual transfer.
Legal Regulation of Stablecoins

As a type of digital currency, stablecoin is expected to overcome the disadvantage of large fluctuation in the value of digital currencies through a series of mechanisms.Thus the stability of currency value could be realized to achieve its applications including payment.There are two major control capabilities in stablecoins: control over data and control over huge assets.These two capabilities enable the stablecoins to gain huge market advantages, which will actually become a new type of infrastructure and have an impact on social public life.Nevertheless, there are also huge risks behind the advantages.With the platform attribute, stablecoins will be developed to be "too large to fail".Once it's out of control, there will be risks in reserve asset management, data, operation, and competition.We should consider stable currency as an infrastructure, to establish a stable currency reserve management system and consumer protection system, as well as to improve the stable currency platform data access system, and the power and responsibility allocation of regulatory authorities.
The growth of image-related three dimensional reconstruction techniques in deep learning-driven era：a critical summary

Abstract： Image-related three dimensional reconstruction techniques refer to the process of reconstructing the three dimensional model derived of a single image or multi-view images.It can illustrate a three dimensional model relevant to any view-derived color texture.Traditional three dimensional reconstruction methods are often required for a large number of images in relevance to such multiple contexts like sparse point cloud reconstruction， camera parameter estimation， dense point cloud reconstruction， surface reconstruction and texture mapping.In recent years， deep learning-driven imagerelated three dimensional reconstruction techniques have been concerned about， and current literatures are focused on introducing the traditional methods of image or special objects-based three dimensional reconstruction.The critical summary of image-based three dimensional reconstructions is called for further in terms of deep learning contexts.We summarize recent situation for deep learning based three dimensional reconstructions in terms of image analysis.First， three 中图法分类号： TP391.
Hierarchical semantics-fused scene text detection

Abstract： Objective Scene-related text detection is essential for computer vision， which aims to localize text instances for targeted image.It is beneficial for such domain of text recognition applications like scene understanding， translation and text visual question answering.The emerging deep learning based convolution neural network (CNN) has been widely developing in relevance to text detection nowadays.Current researches are focused on texts location in terms of the regression of the quadrangular bounding box.However， since regression based methods unfit texts with arbitrary shapes (e. g. ， curved texts)， many approaches focus on segmentation based methods.Fully convolutional networks (FCN) are commonly used to obtain high-resolution feature maps， and the pixel-level mask is predicted to locate the text instances as well.Due to the extreme aspect ratios and the various sizes of text instances， existing models are challenged for one feature map-中图法分类号： TP391.
Multiple unmanned surface vehicles-based path planning method for sea patrol

Abstract： Objective The artificial intelligence (AI) technology based intelligent unmanned equipments like unmanned surface vehicles have been developing for such domains of marine technology-derived military operation and counterterrorism， resource exploration， seabed topography mapping， water quality testing， and marine search and rescue.In recent years， such issues of unmanned surface vehicles-related path planning have been concerned about.However， existing tasks are still focused on the path planning of single unmanned surface vehicle.The methods and simulations are only based on narrowed water areas like lakes and ports， and the problem of energy constraint is required to be melted into as 中图法分类号： U675.
Visual information extraction deep learning method：a critical review

Abstract： A huge amount of big data-driven documents are required to be digitalized， stored and distributed in relation to images contexts.Such of application scenarios are concerned of document images-oriented key information， such as receipt understanding， card recognition， automatic paper scoring and document matching.Such process is called visual information extraction (VIE)， which is focused on information mining， analysis， and extraction from visually rich documents.Documents-related text objects are diverse and varied， multi-language documents can be also commonly-used incorporated with single language scenario.Furthermore， text corpus differs from field to field.For example， a difference in the text content is required to be handled between legal files and medical documents.A complex layout may exist when a variety of visual elements are involved in a document， such as pictures， tables， and statistical curves.Unreadable document images are often derived and distorted from such noises like ink， wrinkles， distortion， and illumination.The completed pipeline of visual information extraction can be segmented into four steps： first， a pre-processing algorithm should be applied to remove the problem of interference and noise in a manner of correction and denoising.Second， document image-derived text strings and their locations contexts may be extracted in terms of text detection and recognition methods.Subsequently，
A Hybrid Compression Technique with Region of Interest for Medical Images in Wireless Sensor Networks

Wireless sensor networks (WSNs) are a technology used within an Internet of Things (IOT) system to gather data and send it through a router to the internet in an IOT system. Data transmission from one node to another always requires to be compressed to utilise the bandwidth of the network. Compression of databases helps to save a lot of space, bandwidth for transmission and also reduces the transmission time. Recently, the need for compressing medical images is increasing globally. In order to tackle this in an effective manner, a Region of Interest (ROI) based hybrid compression technique is presented in this book chapter. In this work, the concepts of Discrete wavelet transform (DWT), Discrete cosine transform (DCT), and Joint Photographic Experts Group (JPEG) are also used in order to develop a robust hybrid algorithm. In order to get optimal results, we propose a hybrid technique based on DWT and DCT compression techniques. The efficiency of the proposed technique is tested over five different medical modalities with other popular compression techniques. The proposed model results on various medical modalities such as X-ray is 37.58 MSE, 32.415 PSNR, on CT scan is 48.22 MSE, 31.332 PSNR, on mammograph is 21.90 MSE, 34.759 PSNR, on MRI is 49.17 MSE, 31.247 PSNR. It is observed from the experimental results that the proposed ROI hybrid technique is effective and efficient in comparison to other compression techniques for transmission of data across WSNs.
Graph-Based Representation for Multi-image Super-Resolution

Multi-image super-resolution is a challenging computer vision problem that aims at recovering a high-resolution image from its multiple low-resolution counterparts. In recent years, deep learning-based approaches have shown promising results, however, they often lack the flexibility of modeling complex relations between pixels, permutability of the input data, or they were designed to process a specific number of input images. In this paper, we propose an improved version of our earlier graph neural network that benefits from permutation-invariant graph-based representation of multiple low-resolution images. Importantly, we demonstrate that our solution allows for performing reconstruction from a set of heterogeneous input images, which is not straightforward for other state-of-the-art techniques. Such flexibility is a crucial feature for practical applications, which is confirmed qualitatively and quantitatively for a set of real-world (rather than simulated) input images.
Graph-Based Deep Learning on the Swiss River Network

Major European rivers have their sources in the Swiss Alps. Data from these rivers and their tributaries have been collected for decades with consistent quality. We use GIS data to extract the structure of each river and link this structure to 81 river water stations (that measure both water temperature and discharge). Since the water temperature of a river is strongly dependent on the air temperature, we also include 44 weather stations (which measure, for instance, air or soil temperature). Based on this large data corpus, we present in this paper a novel graph representing the water network of Switzerland. Our goal is to accelerate the research of the complex relationships at the (Swiss) water bodies. In particular, we present different graph-based pattern recognition tasks that can be solved on the novel water body graph. In a first evaluation, we use graph-based methods to solve two of these tasks, outperforming current state-of-the-art systems by several percentage points.
Graph-Based vs. Vector-Based Classification: A Fair Comparison

Numerous graph classifiers are readily available and frequently used in both research and industry. Ensuring their performance across multiple domains and applications is crucial. In this paper, we conduct a comprehensive assessment of three commonly used graph-based classifiers across 24 graph datasets (we employ classifiers based on graph matchings, graph kernels, and graph neural networks). Our goal is to find out what primarily affects the performance of these classifiers in different tasks. To this end, we compare each of the three classifiers in three different scenarios. In the first scenario, the classifier has access to the original graphs, in the second scenario, the same classifier has access only to the structure of the graph (without labels), and in the third scenario, we replace the graph-based classifiers with a corresponding related statistical classifier, which has access only to an aggregated feature vector of the graph labels. On the basis of this exhaustive evaluation, we are able to suggest whether or not certain graph datasets are suitable for specific benchmark comparisons.
Real World Path Generation for Non-holonomic Systems with Obstacle Avoidance Using RRT* and Google Earth

In this paper, a new version of Rapidly Exploring Random Trees and Rapidly Exploring Random Trees Star (RRT & RRT*) with obstacle avoidance in 3D is shown in MATLAB. This algorithm combined with Google Earth generates a real-world path, avoiding obstacles. The algorithm uses a function called “No Collision” which is developed using the principle of “plane of intersection” which is defined as “if a line extended from the node hits the face of the obstacle that node is excluded from the tree and another random node is selected, this keeps running until no node in the tree hits the obstacles.” This new version considers any real-world obstacles, which are converted into cubes or cuboids. The path created by general RRT and RRT* cannot be used by non-holonomic systems because of the instantaneous turns present in the path so the new version generates a path for non-holonomic systems where the turns are not instantaneous. The simulation and experiment results are given to show the robustness of the improved RRT and RRT* compared to the regular algorithms.
Maximal Independent Sets for Pooling in Graph Neural Networks

Convolutional Neural Networks (CNNs) have enabled major advances in image classification through convolution and pooling. In particular, image pooling transforms a connected discrete lattice into a reduced lattice with the same connectivity and allows reduction functions to consider all pixels in an image. However, there is no pooling that satisfies these properties for graphs. In fact, traditional graph pooling methods suffer from at least one of the following drawbacks: Graph disconnection or overconnection, low decimation ratio, and deletion of large parts of graphs. In this paper, we present three pooling methods based on the notion of maximal independent sets that avoid these pitfalls. Our experimental results confirm the relevance of maximal independent set constraints for graph pooling.