Reviewerly/exHarmony huggingface dataset:
The value of the blockchain platform for shipping logistics considering the perceived utility of consumers

Blockchain has been widely used to trace logistics information from sources to destinations in shipping logistics practices. Motivated by market practices, this study intends to discuss the potential application value of blockchain platforms in shipping logistics, considering the perceived utility of consumers. For this purpose, to investigate the shipping logistics supply chain (SLSC) of a shipping company (SC) and a freight forwarding company (FFC), in which FFC decides whether to introduce a blockchain platform and its introduction channel. Specifically, three channel models are considered: Model T ( i.e. , traditional logistics supply chain channel), Model B ( i.e. , FFCs applied a blockchain platform via a self-built platform channel), and Model J ( i.e. , FFCs applied a blockchain platform via joining a third-party platform channel). Meanwhile, the Nash and Stackelberg game are used to characterize the companies’ equilibrium outcomes under different power structures. By comparing these three modes, some interesting results are found: consumer perception of the level of service and privacy protection and consumer sensitivity to price has critical effects on the value of blockchain platforms. Results also show that the blockchain platform does not always motivate the members of the SLSC to become better. Whether the power structure is symmetrical or asymmetrical, FFC adopts Model B instead of Model J, in which the blockchain platform can help FFC increase profits, and the value of blockchain is significant. Moreover, the FFC can obtain at least approximately 78% more benefit when the power structure is symmetrical than asymmetrical. However, the value of blockchain platforms to SC is not necessarily significant. The value of the blockchain platform for SC is significant only in Model B with the power structure asymmetric. Therefore, the value of the blockchain platform in Model B with the power structure asymmetrical for both FFCs and SCs is significant, achieving a win-win situation. Also, an extensive numerical analysis showing the optimal channel strategy in the three models generated additional management insights.
Distance Learning Technology Acceptance, a Case of Ph.D. Students’ Experiences Post COVID-19

This study aims to examine the challenges faced by higher education students in distance learning using remote communication technologies and how they could affect the quality of education. In addition to examining the challenges, higher education students face when dealing with technology acceptance. The purpose of this analysis is to emphasize the importance of technology acceptance as a necessary fact in gaining knowledge during experiences of distance learning and the use of remote communication technologies. The main objective of this study is to identify, from students’ remote focus group experiences, elements that could highlight the actual situation of technology acceptance among future Ph.D. students and the skills that are thought to be necessary to reinforce the quality of distance learning processes.
An overview of space-variant and active vision mechanisms for resource-constrained human inspired robotic vision

Abstract In order to explore and understand the surrounding environment in an efficient manner, humans have developed a set of space-variant vision mechanisms that allow them to actively attend different locations in the surrounding environment and compensate for memory, neuronal transmission bandwidth and computational limitations in the brain. Similarly, humanoid robots deployed in everyday environments have limited on-board resources, and are faced with increasingly complex tasks that require interaction with objects arranged in many possible spatial configurations. The main goal of this work is to describe and overview biologically inspired, space-variant human visual mechanism benefits, when combined with state-of-the-art algorithms for different visual tasks (e.g. object detection), ranging from low-level hardwired attention vision (i.e. foveal vision) to high-level visual attention mechanisms. We overview the state-of-the-art in biologically plausible space-variant resource-constrained vision architectures, namely for active recognition and localization tasks.
A Self-diagnosis Medical Chatbot for H5N1 Virus

Chatbots are everywhere these days. This paper presents a chatbot, Hans, that is designed for dealing with the public during a H5N1 pandemic. Hans provides a natural way of educating the public on health safety measures with its conversational ability. Users are able to perform self-assessment for H5N1 infection and receive suggestions on preventive actions from the virus spread. Hans is developed based on the Verbot tool with C# programming platform. User Acceptance Test is carried out via an online questionnaire distributed among 30 respondents. The results revealed favorable feedback from the users on the roles and capacity of Hans in providing information and diagnosis on H5N1. Hans is hoped to assist the frontliners in terms of improving service times, reducing workload, and saving costs in manning the medical team.
Enhancing the blockchain interoperability through federated learning with directed acyclic graph

Abstract The use of federated learning to achieve blockchain interoperability has become a hot topic in research, because it enables data exchange without revealing any private information. However, the previous work, such as ScaleSFL (Asia‐CCS, 2022), that has implemented federated learning for blockchain interoperability, the throughput of the framework still cannot support the practical applications. Therefore, a federated learning framework based on Directed Acyclic Graph (DAG) is proposed which utilizes sharding mechanism to enhance the blockchain interoperability. By constructing a weighted context graph based on data correlation, reasonable sharding of the dataset is achieved, thereby improving the efficiency of blockchain interoperability. The experimental results show that the federated framework reduces global computation in federated learning by 30% compared with other schemes, while increasing blockchain throughput by nearly 40%.
Automating the first and last mile? Reframing the ‘challenges’ of everyday mobilities

In this article, we interrogate the utility of conceptualising the 'first and last mile' (FLM) as a 'challenge' to be addressed through automated and integrated mobility services. We critically engage with the concept through a design anthropological approach which takes two steps so as: to complicate literatures that construct the FLM as a place where automated, service-based and micro-mobility innovations will engender sustainable modal choices above individual automobility; and to demonstrate how people's situated mobility competencies and values, shape social and material realities and future imaginaries of everyday mobilities. To do so, we draw on ethnographic research into everyday mobility practices, meanings and imaginaries in a suburban neighbourhood in Sweden. We show how locally situated mobilities both challenge the spatial and temporal underpinnings of the first and last mile concept, and resist universalist technology-driven automation narratives. We argue that instead of attempting to bridge gaps in seemingly linear journeys through automated systems, there is a need to account for the practices, tensions and desires embedded in everyday mobilities.
Improved Social Network User Recommendation System—The Machine Learning Approach

In this paper, we propose a recommendation system for social media users which makes recommendations on the basis of the user profile information and the contents posted by users-the bio-aware algorithm. Text mining techniques are used to pre-process the words before feeding it to an LDA model which handles the topic and feature extraction. In this way, we determine the similarity between users based on their interests. We further trained a machine learning model which is able to identify and score the top interests of a particular social media user. Other users who share similar scores are shown as recommendations to each other.
Improvement of Beginning Reading Ability in Class V Dyslexic Students through the Silaba Method at Jombang 05 Public Elementary School

The objective of this research is to look into the inclusion of children with neurological diseases in Indian education. According to the research, a substantial proportion of children in India have neurological disorders, and they frequently face barriers to education. The study examines the current situation of education for children with neurological problems in India, as well as the policies and laws that regulate their education. The review also looks at the difficulties that these children, their families, and educators have had in implementing inclusive education. Lastly, the report explores interventions and techniques that can help children with neurological disorders be included in Indian education settings with a concerted effort from all stakeholders involved in the education process.
Arrival-time detection with histogram distance for acoustic emission signals

Arrival-time picking is a fundamental step in time-of-arrival (TOA)-based localisation in current and future acoustic emission (AE), microseismic and seismic localisation systems. The accurate detection of TOA is of great importance for high localisation accuracy. This work presents a histogram distance-based method for TOA detection of elastic wave signals. The authors treat an original elastic waveform that includes the arrival as two different locally stationary segments, namely the intervals following and preceding arrival. To determine the optimal separation of these two stationary segments, ie the arrival time, histograms of these intervals are calculated and a measure of the distance between them modified from the Bhattacharyya coefficient is proposed. The performance of the proposed method is evaluated using TOA picking for AE. The method is shown to provide accurate and robust picks on AE signals under various signal-to-noise ratios. To evaluate the adaptability of the method to other TOA picking scenarios, it is applied to detect the seismic P-phase. The detection accuracy is adequate and errors are constrained to within a few seconds. Factors that influence the detection accuracy are discussed. The results suggest that the proposed method has the potential to detect TOA in various fields.
DL-PCN: Differential learning and parallel convolutional network for action recognition

Graph Convolution Network (GCN) algorithms have greatly improved the accuracy of skeleton-based human action recognition. GCN can utilize the spatial information between skeletal joints in subsequent frames better than other deep learning algorithms, which is beneficial for achieving high accuracy. However, the traditional GCN algorithms consume lots of computation for the stack of multiple primary GCN layers. Aiming at solving the problem, we introduce a lightweight network, a Differential Learning and Parallel Convolutional Networks (DL-PCN), whose key modules are Differential Learning (DLM) and the Parallel Convolutional Network (PCN). DLM features a feedforward connection, which carries the error information of GCN modules with the same structure, where GCN and CNN modules directly extract the original information from the input data, making the spatiotemporal information extracted by these modules more complete than that of GCN and CNN tandem structure. PCN comprises GCN and Convolution Neural Network (CNN) in parallel. Our network achieves comparable performance on the NTU RGB+D 60 dataset, the NTU RGB+D 120 dataset and the Northwestern-UCLA dataset while considering both accuracy and calculation parameters.
The distribution semantics in probabilistic logic programming and probabilistic description logics: a survey

Representing uncertain information is crucial for modeling real world domains. This has been fully recognized both in the field of Logic Programming and of Description Logics (DLs), with the introduction of probabilistic logic languages and various probabilistic extensions of DLs respectively. Several works have considered the distribution semantics as the underlying semantics of Probabilistic Logic Programming (PLP) languages and probabilistic DLs (PDLs), and have then targeted the problem of reasoning and learning in them. This paper is a survey of inference, parameter and structure learning algorithms for PLP languages and PDLs based on the distribution semantics. A few of these algorithms are also available as web applications.
Effective handling of exceptional situations in robust software agents

Software agents are normally expected to operate in open and dynamic environments, and therefore they are often supposed to face situations that significantly deviate from the nominal course of events. The effective management of exceptional situations is of paramount importance to provide agents with the needed means to operate in their environments, mostly because these situations should be considered as the norm in open and dynamic environments. This paper presents some recent additions to the Jadescript agent-oriented programming language that were specifically designed to provide agents with the needed capabilities to effectively detect and manage exceptional situations. The first part of this paper motivates the need of sophisticated exception handling capabilities, also by relating the proposed language features with the state of the art documented in the literature. Then, the second part of this paper discusses the proposed language features, also considering the conceptual similarities and differences with the related features normally available in mainstream programming languages. In particular, the proposed language features are presented in terms of three language improvements: the general-purpose support to handle exceptions, the specific support to handle behaviour failures, and the specific support to handle stale messages. Finally, before concluding with some indications on future research activities, the third part of this paper describes a concrete example intended to practically present the actual use of the new language features.
Research and design of student archives management system based on consortium blockchain

Student archives are records of students’ learning activities during school and important proof of students’ learning experiences, so the construction of a credible digital archives system is of great significance. Blockchain is a data structure and information storage technology that has become well-known with the development of Bitcoin and is currently widely used to build a trusted digital environment. The use of blockchain technology can solve the problems of low confidentiality and easy tampering of traditional archives management. Therefore, this paper proposes a blockchain-based student archives management scheme, designs the key data structures and algorithms of the system model, and tests the functions and performance of the system model on the ESXi platform. Experimental results show that the scheme proposed in this paper can effectively improve the confidentiality and difficulty of archive tampering, and at the same time have good resource sharing and retrieval efficiency, making archives management safer, more credible and more intelligent.
Application of edge computing and APP software services in graphic product art design

The design of a green city needs to improve energy efficiency and urban greening rate and plan public green spaces in places with good natural conditions and reasonable structures. With the rapid development of embedded technology and embedded APP software services, the application range of software maintenance systems has become more and more extensive, and it has also stimulated the increasing demand for embedded software. Improve the efficiency of embedded software development, reduce complexity, cost and development cycle, and apply component-based software development technology. These are very suitable for embedded system development. Component technology is the main form of current software development, which has the characteristics of reusability, packaging and assembly. It also expands Lw CCM, component model and component container. This article analyzes the product graphic design process, and designers choose appropriate design software to improve work efficiency when performing product graphic design. The application of design software in the product design process is mainly according to the specific characteristics of forward design and reverse design. The difference is that a product with a relatively simple structure uses 3D software to design the product in the direct design process, while a product with a relatively complex and cumbersome structure uses plane software to design the product in the reverse design process. Therefore, this article uses mobile edge technology to learn the application of APP software and the art design of graphic products.
Deep Learning for Semantic Segmentation of Football Match Image

As one of the most popular sports, football has been a subject to growth and advancements in technology. The combination of football and artificial intelligence is expected to be used for intelligent football analysis. Image semantic segmentation is an important basis for image analysis and understanding. This paper proposes a deep learning-based image segmentation model for pixel-level classification of the video recordings frames of football matches. Every pixel of football video frame is classified into one of the 10 classes, e.g., players, ball, goal bar and several background scenes. In this paper, we first test a variety of CNN architectures and pre-trained models and select the MobileNet-UNet architecture as our baseline. We note the severe unbalanced data distribution in football scene segmentation. To solve this problem, the weighted multi-class cross-entropy loss is adopted in training of MobileNet-UNet to redistribute the weights of classification loss, focusing on smaller foreground object classes and improving segmentation accuracy. We also propose to use image transformations and a random mixture sampling technique for training data augmentation to reduce model overfitting. The model is trained and validated in the well-annotated Football Semantic Segmentation Open Dataset. The proposed best model achieves 0.96 frequency weighted IoU and 0.90 mean IoU segmentation accuracy on validation set.
KeyState: Improving Image-based Reinforcement Learning with Keypoint for Robot Control

Learning from high-dimensional images is essential for reinforcement learning (RL) to train autonomous agents that can interact directly with the environment using visual observations. A general strategy is to extract task-relevant information in the images to learn a representation that characterizes the system states. Although existing works can find good representations for static states efficiently, they still take a long time to learn system dynamics, which is critical for applications such as robot control. Since the most important system dynamics in robot control is the motions of the robot itself, image-based RL can be more efficient if the state of the robot is known. In this paper, we propose to extract the keypoints of the robot as auxiliary information to improve the data-efficiency of RL from image pixels. The proposed method, called KeyState, is evaluated on DeepMind Control Suite, a common benchmark for evaluating data-efficiency and performance of RL agents. The experimental results show that the performance of KeyState is, on average, 1.65 times better than the prior pixel-based methods.
Data Synthesizing for Specific Human Action Segmentation

Temporal action segmentation has been one of the most challenging tasks in human activity analysis because it involves segmenting and recognizing actions in long action sequences, which requires a higher-level understanding of human activities. Many deep learning models have emerged and achieved excellent performance in action segmentation tasks. However, these models often require a large amount of labeled data for training. Generally, public datasets can help train models. But collecting and labeling sufficient training data can be costly for some specific actions that do not exist in public datasets. Therefore, we propose a framework for specific human action segmentation using data augmentation. Specifically, given a few labeled action sequences, we first segment them into short-trimmed action segments by label and then use a motion blending method to randomly combine these action segments representing different categories to form many new labeled action sequences. In this way, we can build a synthetic action dataset for the specific action, which provides enough data for training large-scale models. Our experimental results show that SOTA skeleton-based action segmentation models work well with our synthetic action dataset, and data augmentation can also improve the performance of these models.
Smart AR workstation configuration in industrial assembly lines

Augmented reality (AR) is becoming a successful training tool in various industrial sectors that involve design, assembly, and maintenance. One of the crucial applications of AR in industrial assembly lines concerns the workstation configuration. The media contents/assets used in AR applications include text, audio, video, 3D models, and animations. In this paper, we explore different assets of AR suitable for workstation configuration, notably 2D images and 3D CAD models. We specifically discuss the problems associated with the use of 3D CAD models in light rendering AR devices such as HoloLens2 since storing, processing, and rendering 3D CAD data remains a challenging issue even today. We propose a solution that uses simplified CAD models in place of their originals without affecting the user experience to configure an intelligent workstation and test such models inside HoloLens2.
Human Age Prediction Based on Brain MRI Using Density-Based Regression

In this paper, we have applied a density-based regression technique to predict the age of a human brain based on Magnetic Resonance Images. We propose four different multivariate discrete distributions to accommodate different mean-covariance and correlation structures, given the challenging nature of medical datasets. Then, we use a novel Minorization-Maximization framework to address the issue of high dimensional optimization. Finally, to handle the time and memory requirements of processing large amounts of high-resolution images, we have adopted a real-time framework, where the learning behavior is gradually improved as new data become available.
A Blockchain based Framework for Secure and Decentralized Energy Trading in a Community

Blockchain is a decentralized, digital, and distributed ledger which allows transparent and secure information sharing among the peer-to-peer network. It eliminates the need for a centralized trusted party and, though it was introduced as the backbone technology for cryptocurrencies but has proved to be a promising and revolutionary technology for almost all global industries. The application of blockchain technology in the energy sector proposes a paradigm of solutions to problems of different levels of complexity in the traditional energy ecosystem. Extensive research has been proposed to exploit the inherent benefits of blockchain technology for the integration of distributed energy sources and facilitate peer-to-peer energy trading. This paper proposes a blockchain-based architecture to facilitate secure and decentralized energy trading generated from renewable energy sources. The solution utilizes the Ethereum blockchain and Smart Contracts for energy trading among the members of a small community without any trusted third entity and adopts features to achieve data integrity and confidentiality, and user identity privacy.