Reviewerly/exHarmony huggingface dataset:
Cloud Computing Interoperability : An overview

At present, cloud computing has attracted a serious deal of research interest and attention in multiple domains. One of the core challenges in this environment is to achieve interoperability among heterogeneous cloud service providers (heterogeneous resources, APIs (Application Programming Interface), SLA(Service-level agreement) policy, etc.) to keep up with the increasing demand of cloud services and the growing requirements of user’s applications. For that, we provide in this paper an overview of the existing approaches and proposed solutions. In this setting, we aim to clarify: Who has posed the Cloud Computing Interoperability (CCI) problem? What does CCI mean? When and Why CCI is needed? Where does CCI problem arise? And the key question that is: How to resolve CCI problem? For this latter, we propose a taxonomy where we distinguish between the considered factors before resolving the CCI problem, and the obtained characteristics of the proposed solutions after resolving the CCI problem. Then we study existing works of CCI according to this proposed taxonomy, where we have generated three graphs allowing us to discuss CCI solution approach VS consumer-centric, CCI solution architecture VS consumer-centric, and CCI solution approach VS CCI solution type. We have concluded that: 1) the application service model is more highlighted in the literature then the management and platform levels, 2) the provider-centric solutions use generally model based approaches and are deployed as middleware or brokers, 3) the user-centric solutions are based on the adapting methodologies and deployed as brokers, 4) the hybrid solutions are based on the adapting methodologies and offer standard or broker architectures, 5) the type of CCI solution in model based approaches is mainly corresponding to framework products, 6) the final product of adapting methodologies can be a service or a library type.
A study on the E-commerce Trends using Data Analysis

Over the years, numerous businesses transitioned to creating online access, which helped them towards gaining access to a global presence. With the rise of ecommerce businesses accessed through websites or social media platforms, many seemed to have failed to generate sufficient revenue for themselves. Machine learning, artificial intelligence, and data science have aided retailers in managing their supplies, creating a brand for themselves with the review system, and providing the customer with recommendations. Moreover, with the help of these recommendations, customers have made the right choices when purchasing a product. Thus, this is what all established retailers and their customers demonstrate. Due to a massive increase in e-commerce businesses, it is significant to have details of all the ongoing strategies in the current business to ensure a reasonable growth rate. This study will interpret the different trends, platforms, and strategies involved in an ecommerce business.
DSANE:A Dual Structure-Aware Network Embedding Approach for User Identity Linkage

Increasing popularity and diversity of social media networks has encouraged people to participate on multiple online social networks. Thus, linking account across different networks belonging to the same person becomes an emerging task and is important to many downstream tasks. The mainstream approach is to regard users as nodes in networks and embed them in a low-dimensional latent space, making it convenient to input into subsequent predictive models. Existing methods are still faced up with challenges of incompleteness of networks and insufficiency performance. We propose a novel method called DSANE-Dual Structure-Aware Network Embedding for user identity linkage based on deep neural network, which firstly optimizes networks by a complete-filter method, and then embeds both local and global information into nodes’ vectors, thus greatly enriches nodes’ representation. Experiments on real-world datasets show that our method greatly outperforms baselines.
Consensus-Based Distributed Cooperative Perception for Connected and Automated Vehicles

Cooperative perception techniques incorporating Vehicle-to-Everything (V2X) information offer new possibilities for enhancing the perception capability of automated vehicles (AVs), but also present a new challenge of how to maximize the benefits of connected information with limited communication burden. In this context, this paper proposes a novel cooperative perception solution based on consensus theory to improve the accuracy and consistency for the detection and tracking of non-connected targets by combining V2X information. Given the common multi-sensor configurations of AVs, we design a consensus-based distributed cooperative perception (DCP) algorithm in the framework of multi-layer information fusion for local sensors and connected nodes, and give a nonlinear form based on cubature rules to include more accurate nonlinear system models and nonlinear sensors. Considering the high maneuverability of vehicle targets, we then extend the DCP algorithm to a multi-model form (DMMCP) to improve the model uncertainty of maneuvering targets via combining the prior knowledge of multiple models, which also gives a calculation method for model probability and its average consensus in the context of multiple local sensors. Besides, a new consensus information weight strategy and the properties from different consensus information weights are discussed. The simulation results demonstrate the superiority of both our algorithms over the traditional algorithms in accuracy and consistency, moreover, the DMMCP algorithm, which takes into account model uncertainty, shows better performance than DCP in complex conditions.
PP8K: A New Dataset for 8K UHD Video Compression and Processing

In the new era of ultra-high definition (UHD) videos, 8K is becoming more popular in diversified applications to boost the human visual experience and the performances of related vision tasks. However, researchers still suffer from the lack of 8K video sources to develop better processing algorithms for the compression, saliency detection, quality assessment, and vision analysis tasks. To ameliorate this situation, we construct a new comprehensive 8K UHD video dataset, which has two sub-datasets, i.e., the common raw format videos (CRFV) dataset and the video salient object detection (VSOD) dataset. To fully validate the diversity and practicality, the spatial and temporal information characteristics of the CRFV dataset are evaluated by the widely used metrics and the video encoder. Through the extensive experiments and comparative analyses with the other counterpart datasets, the proposed 8K dataset shows apparent advantages in diversity and practicality, which can benefit its applications for the developments of UHD video technologies. This dataset has been released online: https://git.openi.org.cn/OpenDatasets/PP8K.
Task-Covariant Representations for Few-Shot Learning on Remote Sensing Images

In the regression and classification of remotely sensed images through meta-learning, techniques exploit task-invariant information to quickly adapt to new tasks with fewer gradient updates. Despite its usefulness, task-invariant information alone may not effectively capture task-specific knowledge, leading to reduced model performance on new tasks. As a result, the concept of task-covariance has gained significant attention from researchers. We propose task-covariant representations for few-shot Learning on remote sensing images that utilizes capsule networks to effectively represent the covariance relationships among objects. This approach is motivated by the superior ability of capsule networks to capture such relationships. To capture and leverage the covariance relations between tasks, we employ vector capsules and adapt our model parameters based on the newly learned task covariance relations. Our proposed meta-learning algorithm offers a novel approach to effectively address the real task distribution by incorporating both general and specific task information. Based on the experimental results, our proposed meta-learning algorithm shows a significant improvement in both the average accuracy and training efficiency compared to the best model in the experiments. On average, the algorithm increases the accuracy by approximately 4% and improves the training efficiency by approximately 8%.
Responsible &amp; Inclusive Cards

Societal implications of technology are often considered after public deployment. However, broader impacts ought to be considered during the onset and throughout development to reduce potential for harmful uses, biases, and exclusions. There is a need for tools and frameworks that help technologists become more aware of broader contexts of their work and engage in more responsible and inclusive practices. In this paper, we introduce an online card tool containing questions to scaffold critical reflection about projects’ impacts on society, business, and research. We present the iterative design of the Responsible & Inclusive Cards and findings from five workshops (n=21 participants) with teams distributed across a multinational technology corporation, as well as interviews with people with disabilities to assess gameplay and mental models. We found the tool promoted discussions about challenging topics, reduced power gaps through democratized turn-taking, and enabled participants to identify concrete areas to improve their practice.
What medicine can learn from game-playing artificial intelligence: The 1 in 10,000 move

Diagnosis usually proceeds along standard pathways. First check an EKG for chest pain to rule out a heart attack. Get a CT scan before a biopsy for possible cancer. Clinicians learn and faithfully replicate conventional strategies.
Latent knowledge reasoning incorporated for multi-fitting decoupling detection on electric transmission line

Detection by fitting is a basic task in the fault diagnosis of electric transmission lines. To effectively solve the problem of missed and false detection in multi-fitting detection caused by occlusion between fittings and complex settings, we propose a decoupling detection method based on the latent knowledge reasoning model (LKRM) inspired by the fixed combination structure of fittings. This method consists of a scenario knowledge reasoning (SCKR) module, spatial knowledge reasoning (SPKR) and decoupling detection module (DDM). To obtain knowledge about the combination and structure of fittings, the scenario perception method of SCKR is employed to determine the scenario information in each image, and the reasoning network is utilized for further effective fusion of the scenario information. To obtain relevant knowledge about the spatial location of the fittings, spatial-visual features and geometric features are extracted by the spatial perception network in SPKR, and spatial knowledge is inferred through a graph convolution network (GCN). Fitting detection will be trained through the non-coupling method used by the decoupled module, and the final detection decision will then be made. The experimental results show that the LKRM achieves better detection results than the other advanced object detection models. The accuracy rate is improved by 10.8% compared with the original baseline model. The qualitative experiment shows that the LKRM can effectively solve the problem of object occlusion in a complex setting.
Multitone reconstruction visual cryptography based on phase periodicity

The current no-computation grayscale image visual cryptography (VC) can only achieve halftone reconstruction but cannot truly achieve multitone. To solve this problem, we propose the concept of phase periodicity of the λ/2 retarder film and calculate the optical axis angle set with phase periodicity. According to the phase periodicity, we propose a λ/2 retarder film phase periodicity visual cryptography (RPP-VC). In RPP-VC, the secret pixels are encoded as the optical axis angles of n λ/2 retarder films and distributed to n shares. The decoding process does not require computation. The reconstructed image has no pixel expansion and can reach up to 23 tones. The quality of the reconstructed images has been greatly improved and the evaluation indicators of perceived quality are nearly doubled compared with other grayscale image VC schemes. The experimental results verify the feasibility of RPP-VC.
Problematizing cultural difference: YouTube narratives about COVID-19 by South Korean and American Vloggers

ABSTRACTIn this study we compare 8 American and 8 South Korean vlogs by YouTubers documenting their experiences of COVID in 2020. We propose a nuanced approach to cross-cultural difference that rejects binaries and involves the study of how communicative resources are used by participants in specific events. We detail similarities and differences between the vloggers, from trends in the distribution of topics to use of visual and linguistic resources. Drawing on a narratives as practices approach and multimodal discourse analysis, we demonstrate that differences in the data are related to various scales that vloggers navigate in their experiences with COVID.본 연구는 2020년에 코로나에 감염된 미국인 유튜버 8명과 한국인 유튜버 8명의 브이로그를 분석, 비교한다. 한국과 미국 유튜버들의 브이로그에서 다뤄지는 주제, 시각적 구성, 언어적 요소 등의 공통점과 차이점을 면밀히 살펴봄으로써, 보다 맥락에 민감하게 문화적 차이를 접근하는 방식을 제시하고자 한다. 내러티브를 실천으로서 바라보는 관점에서 브이로그를 접근하여 한국과 미국 유튜버들의 브이로그는 그들이 마주하는 서로 다른 사회문화적 현실의 여러 단면을 반영한다는 것을 확인하였다.KEYWORDS: StorytellingnarrativeCOVID-19intercultural communicationsocial mediavlogs Disclosure statementNo potential conflict of interest was reported by the authors.Notes1 All case counts are from the COVID-19 database provided by JHU CSSE.2 Subscriber count accessed on May 11, 2022.Additional informationNotes on contributorsJungyoon KohJungyoon Koh is a PhD candidate in Linguistics at Georgetown University. Her research interests include identity construction in social media discourse and human-computer interaction.Anna De FinaAnna De Fina is Professor of Italian Language and Linguistics in the Italian Department and affiliated faculty with the Linguistics Department at Georgetown University. Her interests and publications focus on discourse and narrative, identity, chronotopes, migration and super diversity. She has published extensively on these topics including many articles in internationally recognized journals and 12 volumes between authored and edited books. She is co-editor of the book series Encounters for Multilingual Matters and Discourse, Narrative and Interaction for Routledge. Her latest publications are the Cambridge Handbook of Discourse Studies, coedited with Alexandra Georgakopoulou (2020, Cambridge University Press) and Exploring (Im)mobilities: Language Practices, Discourses and Imaginaries, co-edited with Gerardo Mazzaferro (2021, Multilingual Matters).
Pinakas: A Methodology for Deep Analysis of Tables in Technical Documents

The holistic understanding of the information contained in technical documents depends on the understanding of the document’s individual modalities. These modalities are tables, graphics, diagrams, formulas, etc. and each of them is a standalone research topic that requires a different way of processing and understanding. These modalities, processed and combined with the document text, can introduce new techniques for visual question answering in documents. Thus, in this paper we present Pinakas, a methodology for automatic analysis of the internal tabular information that appears in technical documents and its modeling to stochastic Petri-net graphs. We focus only on tables that strictly abide by the IEEE format rules. The methodology presented here is divided into the following steps: (1) table detection, (2) table recognition, (3) table understanding. Qualitative results of Pinakas are demonstrated as proof of concept for the accurate extraction of information from three different types of tables.
Understanding and Improving Drilled-Down Information Extraction from Online Data Visualizations for Screen-Reader Users

Inaccessible online data visualizations can significantly disenfranchise screen-reader users from accessing critical online information. Current accessibility measures, such as adding alternative text to visualizations, only provide a high-level overview of data, limiting screen-reader users from exploring data visualizations in depth. In this work, we developed taxonomies of information sought by screen-reader users to interact with online data visualizations granularly through role-based and longitudinal studies with screen-reader users. Utilizing these taxonomies, we extended the functionality of VoxLens by supporting drilled-down information extraction. We assessed the performance of our VoxLens enhancements through task-based user studies.
A novel calibration method for the zoom micro-vision system containing electrically tunable lens

For zoom micro-vision system containing electrically tunable lens (ETL), traditional calibration methods are usually ineffective or inefficient due to the limited field of view (FOV) and shallow depth of field (DOF). This article proposes a novel calibration method that can overcome these problems. Firstly, the geometric imaging model of the zoom micro-vision system considering the input focal power is established. Subsequently, a unique image acquisition scheme and the corresponding feature extraction algorithm developed in frequency domain are given, which can greatly reduce the calibration workload. In addition, how to calculate the initial value of the parameters in the imaging model and how to optimize these parameters globally are presented. Finally, calibration experiments including the comparisons with traditional multi-focus and fitting methods are designed and implemented. Experimental results show that the proposed method has good consistency and accuracy when calibrating at different focal intervals, and the average reprojection error is less than 0.25 pixels. Comparative experiments show that the traditional multi-focus and fitting methods not only need more calibration images and the auxiliary high-precision positioning stage, but also have poor applicability for zoom micro-vision systems in practical.
Efficient lossless based secure communication in 6G Internet-of-Things environments

Future sustainable energy-efficient computing solutions in e-healthcare, smart cities, and intelligent robotics applications provide the many benefits and real-time services made possible by the Internet of Things (IoT) and the cloud computing platform. IoT is a relatively new technology expected to emerge due to the expansion of data, communication channels, and embedded systems. Through the IoT, users and physical objects exchange data and interact on virtual networks. Due to the resource-constraint nature of IoT, it is becoming increasingly difficult for organizations and individuals to protect their private information as the value of digital information rises. Reversible Data Hiding (RDH) in Encrypted Images (RDHEI) is gaining popularity in 6G technology for privacy protection because of its ability to embed secret data within an encrypted image and confirm the hidden information authenticity. In this research, we proposed a sustainable, energy-efficient, multi-MSB (most significant bit)-based dynamic quadtree partition with enhanced Huffman coding. Different prediction representations are also utilized to anticipate the existing pixel value from the adjacent pixels based on quadtree partitions to take greater advantage of the spatial association in the cover image. Furthermore, the freed-up large space is used to insert additional data using dynamic quadtree partitions to encrypt the image. Our approach has an optimum embedding capacity compared to the current state-of-the-art schemes.
Community Channel-Net: Efficient channel-wise interactions via community graph topology

The layer-wise structure of deep neural networks (DNNs) isolates the channel interactions in the same layer, which significantly impedes the efficient learning of DNNs. Several existing methods enable channel-wise information exchange via learning channel interdependence in a heuristic and empirical manner. Nevertheless, only informative channels are emphasized while other channels are suppressed in these approaches. This results in a low channel diversity, which impeds the generalization of DNNs. Our work aims to learn channel-wise interdependence and keep the channel diversity concurrently via designing optimal channel interaction patterns. We model the channel interaction pattern from a graph perspective, where the interactions can be regarded as information exchange on the channel graph. Based on this framework, we propose the Community Channel-Net (CC-Net), using a community-based graph topology for channel interaction. Each community contains channels with semantic commonalities, and the inter-community connections are activated among critical channels. With this structured and dynamic topology, the channels from the same community can learn channel interdependence, and those critical channels from distinct communities can gain more diverse features. CC-Net outperforms baselines on image classification tasks over various backbones with fewer computational costs.
A Blockchain-Oriented Framework for Cloud-Assisted System to Countermeasure Phishing for Establishing Secure Smart City

The information that is saved in the cloud about users is protected by a number of different safeguards in order to facilitate the development of smart cities. Phishing and other forms of social engineering are two examples of misleading tactics that may be used by hostile actors to get sensitive information about users. Phishing is still the first step of a multistage assault, despite the significant technological advancements that have been made to it in recent years. Phishing kits have evolved to become attack tools that are much simpler, more user-friendly, and more readily available as time has gone. Indicators of a successful phishing assault include using foreign characters in the URL, typosquatting of prominent domain names, reserved characters in redirections, and multichain phishing. When papers with these types of phishing URLs are uploaded to cloud storage, hackers get a helping push in the right direction. The use of cloud servers in the commission of these assaults is becoming more common. The currently available software to disallow list phishing URLs does not provide sufficient protection against multilevel phishing and instead places the onus of safety on the user to protect themselves. In addition, the immutability of blockchain data and the avalanche effect both demonstrate their effectiveness as crucial safety measures. In view of the recent advances in technology, we suggest an implementation of filtering that is based on blockchain technology to safeguard the cloud-based data of users. The Phish Block that has been presented is able to recognize homographic phishing URLs with an accuracy of 91 percent, thus ensuring the security of cloud storage.