Reviewerly/exHarmony huggingface dataset:
Smart Transformative Services to Defuse Crises?

Smart transformative services such as digital contact tracing apps are a means to offer transformative and economic value by selfmonitoring contacts and improving the well-being of users, while also reducing concerns when using services during pandemics such as COVID-19. In this study, we identify significant factors as well as communication and promotion strategies to encourage digital contact tracing app nonusers to use such apps in order to benefit from their value-creating potential. This research contributes to transformative service literature by identifying digital contact tracing apps as a means to regain confidence in using services during a pandemic, thereby offering transformative and economic value. By integrating two trust dimensions as meaningful mediators, this research sheds light on the conditions under which social influence and Internet privacy concerns influence nonusers’ usage intention. Moreover, the results not only identify significant factors influencing intended app usage but also reveal strategies for increasing actual app usage.
Text Mining and Visualization of Political Party Programs Using Keyword Extraction Methods: The Case of Portuguese Legislative Elections

Extracting keywords from textual data is a crucial step for text analysis. One such process may involve a considerable amount of time when done manually. In this paper, we show how keyword extraction techniques can be used to untap texts of political nature. To accomplish this objective, we conduct a case-study on top of 16 Portuguese (PT) political party programs made available in the context of the legislative elections that took place in 30th of January 2022. Our contributions are two-fold. At the level of resources, we make available a curated dataset and a python notebook that systematizes the process of transforming text into quantitative data and into visual aspects. At the methodological level, we propose to extend the keyword extraction algorithm used in this study to extract the most relevant keywords, not only from individual political party programs, but also across the entire collection of documents. A further contribution is the case-study itself, which calls attention to the fact that such solutions may be of interest not only to common people, but also to journalists or politicians alike. Broadly, we demonstrate how the discussion and the analysis that stems from the results obtained may foster the political science research by making available large-scale processing of documents with marginal costs.
Software Defect Prediction Survey Introducing Innovations with Multiple Techniques

The software is applied in various areas, so that the quality of the software is very important. The software defect prediction (SDP) is used to solve the issues in the software and enhance the quality. Even if SDP is very helpful in testing, predicting the defective modules is not always easy. Different problems impede smooth performance and use of model defect prediction. The prediction of software defects was an interest of investigation, as early stage prediction of defects improves software quality with reduced cost and effective managing of software. Researchers from different fields help to propose different approaches that help effectively and efficiently. A number of approaches, frameworks, methods, and modeling were proposed using different data sets, metrics, and assessment strategies, in order to remove unnecessary and erroneous details from defect-prone modules. Defects in software systems are common and may cause software users various problems. During the development of different methods, the most probable defect location in large code bases was quickly predicted. Prediction of software faults is an important and beneficial way of improving software quality and reliability. The ability to predict which components in a large software system will contain the most faults in the next release contributes to better management projects, including an early estimation of possible release delays, and a “correcting guide for improving the software’s quality. The identification of bugs/defects at the early stages of the software life cycle reduces the software development effort needed. A lot of research in software fault prediction using machine learning methods has been advanced. There are mainly two problems in the prediction of software defects, dimensional reduction, and imbalances of class.
A Cryptocurrency Price Prediction Study Using Deep Learning and Machine Learning

A cryptocurrency is a network-based computerized exchange that makes imitation and double-spending pretty much impossible. Many cryptocurrencies are built on distributed networks based on blockchain technology, which is a distributed ledger enforced by a network of computers. Thanks to blockchain technology, transactions are secure, transparent, traceable, and immutable. As a result of these traits, cryptocurrency has increased in popularity, especially in the financial industry. This research looks at a few of the most popular and successful deep learning algorithms for predicting bitcoin prices. LSTM and Random Forest outperform our generalized regression neural architecture benchmarking system in terms of prediction. Bitcoin and Ethereum are the only cryptocurrencies supported. The approach can be used to calculate the value of a number of different cryptocurrencies.
Evaluation of information security management in crisis response using KAMI index: The case of company XYZ

Company XYZ plays a substantially strategic role in providing technical availability for public security in many digital business activities. Their vision is to enable a secure, trusted, and digitalized environment for citizens and consumers to perform daily activities, such as digital payment, accessing public facilities provided by the government, and many others. However, this makes information travel rapidly to the organization, and the company possesses highly critical and vital assets. These circumstances become an issue in terms of information security management. The pandemic has also brought various technical challenges in reassessing the company's methods and tools in the cyber security area. Some of such changes may likely be permanent or continue in the post-pandemic period. Therefore, the company needs to re-evaluate the capabilities of its security management in the current situation and assess the implications on information security. This study is conducted to measure the company's readiness to apply high standard information security assessment and analyze the pandemic impact on Company XYZ's vulnerability and information security using the latest version of the KAMI Index. The results show that company XYZ has achieved maturity level V of information security management with an overall score of 592. The results also offer further recommendations to improve their information security frameworks in preparing further security assessment, opportunities, and enhancement to address any possible issues during and post-pandemic.
Multi-UAV Cooperative Path Planning Based on Aquila Optimizer

This paper proposes a cooperative path planning algorithm for multiple UAVs based on aquila optimizer to meet the autonomous capability requirements of Close Air Support mission. The algorithm considers both the performance of individual trajectories in UAV path planning and the performance of space-time cooperation among multiple UAVs. We transform the cooperative path planning problem into an optimization problem and solve the multi-UAV trajectory with the features of multiple iterative methods of Aquila optimizer. According to the simulation results, the UAV trajectory generated by the algorithm can avoid enemy threats and terrain threats, maintain communication distance between team members, with no risk of crash, and can meet the requirements of reliability, safety, and environmental adaptability of UAVs in CAS missions with good performance.
Path Planning of Improved RRT* Based on DBSCAN Algorithm

Aiming at the path planning problem of the unmanned aerial vehicle (UAV) under complex and changeable flight environment and flexible missions, the shortcomings of the existing Rapidly-exploring Random Tree* (RRT*) algorithm are improved. The improved method avoids the excessive ineffective search of the RRT* algorithm in space, reduces the computation, improves the planning speed, the search results are more efficient and fit the physical constraints of the vehicle, and the planned path result is better. At the same time, the DBSCAN clustering algorithm is combined with the specific application scenarios for improvement, thus the critical points in the planning results are effectively extracted. The new planning results are smoother and more reliable, and the randomness of the improved RRT* algorithm is avoided. The simulation results show that the new algorithm is universally applicable, has high security, and the planning results obtained are closer to the optimal state that can be directly applied to the path planning scenarios without prior knowledge.
Application and Exploration of the Innovation of Physical Education Model Empowered by the Metaverse Technology

The concept of "metaverse" is a significant change in the development of information technology in today's world.It not only forms a highly integrated world of virtual space and physical reality but also comes as the key factor of interaction between the virtual environment and real life.This paper adopts methods of literature review, field investigation, expert interview, and logical reasoning to explore the innovation and application of the physical education model empowered by metaverse technology.Through analyzing the teaching environment, teaching model, experience, and other factors of the metaverse physical education model, the significance of the application of metaverse technology in physical education is explained.This paper also tries to explore an integrated three-dimensional education model of the virtual world and reality with reasonable structure, real experience, and the best effect, so that to provide a fundamental theoretical reference for physical education to quickly integrate into the era of science and technology and achieve sustainable and high-quality development.
PatchFlow: A Two-Stage Patch-Based Approach for Lightweight Optical Flow Estimation

The deep learning-based optical flow methods have shown noticeable advancements in flow estimation. The dense optical flow map offers high flexibility and quality for aligning neighbouring video frames. However, they are computationally expensive, and the memory requirements for processing high-resolution images such as 2K, 4K and 8K on resources-limited devices such as mobile phones can be prohibitive. We propose a patch-based approach for optical flow estimation. We redistribute the regular CNN-based optical flow regression into a two-stage pipeline, where the first stage estimates an optical flow for a low-resolution image version. The pre-flow is input to the second stage, where the high-resolution image is partitioned into small patches for optical flow refinement. With such a strategy, it becomes possible to process high-resolution images when the memory requirements are not sufficient. On the other hand, this solution also offers the ability to parallelize the optical flow estimation when possible. Furthermore, we show that such a pipeline can additionally allow for utilizing a lighter and shallower model in the two stages. It can perform on par with FastFlowNet (FFN) while being 1.7x faster computationally and with almost a half of the parameters. Against the state-of-the-art optical flow methods, the proposed solution can show a reasonable accuracy trade-off for running time and memory requirements. Code is available at: https://github.com/ahmad-hammad/PatchFlow .
Style Image Harmonization via Global-Local Style Mutual Guided

The process of style image harmonization is attaching an area of the source image to the target style image to form a harmonious new image. Existing methods generally have problems such as distorted foreground, missing content, and semantic inconsistencies caused by the excessive transfer of local style. In this paper, we present a framework for style image harmonization via global and local styles mutual guided to ameliorate these problems. Specifically, we learn to extract global and local information from the Vision Transformer and Convolutional Neural Networks, and adaptively fuse the two kinds of information under a multi-scale fusion structure to ameliorate disharmony between foreground and background styles. Then we train the blending network GradGAN to smooth the image gradient. Finally, we take both style and gradient into consideration to solve the sudden change in the blended boundary gradient. In addition, supervision is unnecessary in our training process. Our experimental results show that our algorithm can balance global and local styles in the foreground stylization, retaining the original information of the object while keeping the boundary gradient smooth, which is more advanced than other methods.
MSFF: Multi-Scale feature fusion for fine-grained image classification

Fine-grained image classification is a sub-category classification problem with a common superior category.Aiming at the characteristics of large intra-class differences and small inter-class differences in fine-grained images, this paper proposes a fine-grained image classification method based on multi-scale feature fusion.The method constructs a three-branch network model.The attention module and local extraction module are used to obtain the image of the target object and the image of the parts with strong distinguishing detail features.The depth metric learning is used to shorten the distance from the same data by using misclassification information to improve the classification accuracy; secondly, without using the image bounding box/partial annotation information, the image information of different scales is fused through a parallel network structure; finally, the entire network is optimized by combining the loss functions of the three-branch networks.This method performs end-to-end training collaboratively in a multi-branch network to enhance the ability to express information, thereby improving the accuracy of image classification.To evaluate the effectiveness of our method, fine-grained classification experiments were conducted on three datasets.The experimental results show that the algorithm has higher classification accuracy than other fine-grained classification algorithms.
A Comparison between TG Grammar and Structural Grammar

Two major schools of grammar emerged in the 20th century, namely American Structural Grammar represented by L. Bloomfield and Transformational-Generative Grammar (TG Grammar) represented by Chomsky.This paper compares the differences between the two schools of grammar from aspects of background, the stage of development, the philosophical and psychological basis, the view of language, goals of study, objects and methods of study, language acquisition theory and other aspects.This paper also compares the differences between the two schools of grammar in English teaching.It is helpful for us to better grasp the nature of these two schools of grammar by comparing them, so as to better apply them to English teaching.
A Blockchain-Based Smart Contractual Framework for the Electric Vehicle Charging Ecosystem

The impending adoption of electric vehicles is rapidly increasing the complexity of the charging process management for the various stakeholders of the EV charging ecosystem. These stakeholders need to cooperate in exchanging metering, billing, and other sensitive and private data required for the provision of EV charging services. Thus, secure, transparent, and reliable data communication networks must be established to enable efficient interaction among the different stakeholders regarding identity and charging management, along with the session settlement and billing processes. Blockchain is a distributed, digital transaction technology that allows securely exchanging and storing data, as well as executing smart contracts in peer-to-peer networks amongst different entities without established trusted relationships. These smart contracts may holistically describe the relationships between the different entities, as well as the rules of their interaction. Additionally, blockchain technology has the potential to ensure the integrity, reliability, and efficiency of the established ecosystem, strengthen cybersecurity, protect privacy, and minimize transaction costs by applying sophisticated cryptographic techniques. This paper provides an overview of how blockchain technology could transform and improve an operational EV charging management network and proposes a blockchain-based framework that is suitable for the electric vehicle charging ecosystem.
Path planning of mobile robot in complex environment based on improved Q-learning algorithm

Path planning is one of the key technologies of mobile robots. This paper applies the Q-learning algorithm to path planning of mobile robots. To solve the blindness of the mobile robot in exploring the environment, this paper combines RRT algorithm and improves it to increase the goal-orientated performance of the robot in exploring the environment. To overcome the slow convergence rate of the classical Q-learning, the exploration rate, discount rate, learning rate and other parameters in Q-learning are modified. A modified ant colony optimisation (ACO) algorithm is designed to make the robot consider the information of other robots and improve the precision of action decision. The hierarchical idea in the grey wolves optimisation algorithm is introduced into the modified ACO algorithm to realise the dynamic regulation of pheromone. Finally, the modified Dyna-2 algorithm is designed to enhance the generalisation of the Q-learning algorithm. The potential of the proposed hybrid intelligent algorithm is proved by the path planning experiments in two static complex environments.
A Survey of Vision and Language Related Multi-Modal Task

With the significant breakthrough in the research of single-modal related deep learning tasks, more and more works begin to focus on multi-modal tasks.Multi-modal tasks usually involve more than one different modalities, and a modality represents a type of behavior or state.Common multi-modal information includes vision, hearing, language, touch, and smell.Vision and language are two of the most common modalities in human daily life, and many typical multi-modal tasks focus on these two modalities, such as visual captioning and visual grounding.In this paper, we conduct in-depth research on typical tasks of vision and language from the perspectives of generation, analysis, and reasoning.First, the analysis and summary with the typical tasks and some pretty classical methods are introduced, which will be generalized from the aspects of different algorithmic concerns, and be further discussed frequently used datasets and metrics.Then, some other variant tasks and cutting-edge tasks are briefly summarized to build a more comprehensive vision and language related multi-modal tasks framework.Finally, we further discuss the development of pre-training related research and make an outlook for future research.We hope this survey can help relevant researchers to understand the latest progress, existing problems, and exploration directions of vision and language multi-modal related tasks, and provide guidance for future research.
Exploration of College English In-depth Teaching from the Perspective of Information Fragmentation

With the rapid development of science and information technology and the popularization of mobile devices, college students' learning is becoming more and more superficial and fragmented, which brings new challenges to college English teaching.How to make college English teaching go deeper has become an outstanding problem.This paper adopts the research methods of literature and logical analysis, takes the college English course of Luxun Academy of Fine Arts as the research object, focuses on the difficulties and challenges faced by the current college English teaching, puts forward the model of college English in-depth teaching based on the latest research results of in-depth teaching, and analyzes the connotation, teaching objectives, teaching contents and teaching procedures of in-depth teaching, this paper hopes to effectively expand the depth and breadth of college English classroom teaching and improve teaching quality and effect.
Anomaly Detection in the Open World: Normality Shift Detection, Explanation, and Adaptation

Concept drift is one of the most frustrating challenges for learning-based security applications built on the closeworld assumption of identical distribution between training and deployment.Anomaly detection, one of the most important tasks in security domains, is instead immune to the drift of abnormal behavior due to the training without any abnormal data (known as zero-positive), which however comes at the cost of more severe impacts when normality shifts.However, existing studies mainly focus on concept drift of abnormal behaviour and/or supervised learning, leaving the normality shift for zero-positive anomaly detection largely unexplored.In this work, we are the first to explore the normality shift for deep learning-based anomaly detection in security applications, and propose OWAD, a general framework to detect, explain, and adapt to normality shift in practice.In particular, OWAD outperforms prior work by detecting shift in an unsupervised fashion, reducing the overhead of manual labeling, and providing better adaptation performance through distribution-level tackling.We demonstrate the effectiveness of OWAD through several realistic experiments on three security-related anomaly detection applications with long-term practical data.Results show that OWAD can provide better adaptation performance of normality shift with less labeling overhead.We provide case studies to analyze the normality shift and provide operational recommendations for security applications.We also conduct an initial real-world deployment on a SCADA security system.1 Normality shift intuitively refers to the change of distribution of normal data (detailed definition is in §II-C).In this paper, we interchangeably use terms "drift" and "shift".We tend to use "normality shift" as a whole term.