Reviewerly/exHarmony huggingface dataset:
Ship detection of optical remote sensing image in multiple scenes

In view of characteristics of the ship in the optical remote-sensing image, such as multiple dimensions, majority of small objects, crowded arrangement and complex background, and so on, the paper presents a ship detection framework combining the network-fusing multi-level features crossing levels, the rotation region proposal network and the bidirectional recurrent neural network fusing self-attention mechanism. Firstly, we set up a network fusing multi-level features crossing levels because of the multiple scales and diverse characteristics of the remote-sensing ships to increase the precision of feature extraction of the ship, thus improving the performance in the multiple scales, small objects, and complex background problems. Secondly, we separately design the ROI Pooling Layer and the bidirectional recurrent neural network fusing self-attention mechanism, which infuses the prior information of ship dimension and position to realize a good performance and precise ship positioning in crowded scenes. Finally, we verify the effectiveness of the proposed method through experiments, the experimental dataset includes the private dataset designed by us based on Google Earth, the ship dataset in DOTA-Ship and HRSC2016 public ship dataset. The results verify the contributions of each proposed module, and the comparison results show that our proposed method has a state-of-the-art performance.
FPGA-Accelerated Quantum Computing Emulation and Quantum Key Distillation

In the past decades, field-programmable gate arrays (FPGAs) have demonstrated an interesting physical platform to facilitate quantum information processing, particularly in the emergence of domain-specific hardware accelerators for quantum computing emulation and quantum key distillation. While conventional general-purpose hardware platforms have been used for quantum information processing, FPGAs promise deep pipeline parallelism, adaptable interface, and trivial support for custom-precision operation. Therefore, the time is ripe for describing recent development of quantum computing emulators and quantum key distillation accelerators on FPGAs. In this article, we provide a comprehensive review of the state-of-the-art in this active field, with a balance between theoretical, implementational, and technological results. Challenges and promising research opportunities are also discussed.
Fined: Fast Inference Network for Edge Detection

In this paper, we address the design of lightweight deep learning-based edge detection. The deep learning technology offers a significant improvement on the edge detection accuracy. However, typical neural network designs have very high model complexity, which prevents it from practical usage. In contrast, we propose a Fast Inference Network for Edge Detection (FINED), which is a lightweight neural net dedicated to edge detection. By carefully choosing proper components for edge detection purpose, we can achieve the state-of-the-art accuracy in edge detection while significantly reducing its complexity. Another key contribution in increasing the inferencing speed is introducing the training helper concept. The extra subnetworks (training helper) are employed in training but not used in inferencing. It can further reduce the model complexity and yet maintain the same level of accuracy. Our experiments show that our systems outperform all the current edge detectors at about the same model (parameter) size.
Solar Energy Prediction using Decision Tree Regressor

The energy demand is increasing every day. This increase in demand has led us to find alternate sources of energy. The non-renewable sources have limited supply, and constant use gives rise to various types of pollution. Gradually, people started relying on renewable sources of energy due to their abundance in nature. Alternately, Solar Energy (SE) is one of the best sources of energy in India. This paper aims to predict the output production of Solar Power Plants in KWh. Also, this research paper attempts to find the relation of weather attributes with the output power generated. The Decision Tree Regressor model will be used to forecast the power generation of a Solar Power Plant. This model's predictions will help us determine how a solar power plant can be efficiently used to generate a high amount of power. The motivation behind the study was to develop a system that can effectively predict the output of these photovoltaic cells so that orders for supply and demand gaps are placed timely, ensuring the quality of service and cost savings.
New insights into ‘technologies of touch’: Information processing in product evaluation and purchase intention

• Haptic rendering stimulation enhances consumer purchase intention. • Haptic rendering augments perceived sensory similarity. • Consumers process the texture of the screen as information for purchasing. • Consumers perceive the technology as a source of information for decision-making. • Instrumental NFT moderates the effect of haptic rendering on perceived similarity. Technology forecasting is a preliminary step in understanding social change. The response to COVID-19 will affect retailers and customers for years to come, forcing changes to interactions between individuals and technology. Innovative technologies that interrelate social and technological factors merit a re-examination, to explain the impact on consumer behavior where ‘physical’ and ‘digital’ are brought together. This paper explores the use of haptic rendering stimulation for pre-purchase decision-making. The objective is to identify how touching an interface can influence product evaluation and purchase intention. Drawing from an exploratory experimental design, the findings show the importance of interface touch for inferring product information or pleasure to interact with the product, confirming the relationship between knowledge and mental representation, body sensory-motor actions and online shopping contexts.
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs

We present Graformer, a novel Transformerbased encoder-decoder architecture for graphto-text generation.With our novel graph selfattention, the encoding of a node relies on all nodes in the input graph -not only direct neighbors -facilitating the detection of global patterns.We represent the relation between two nodes as the length of the shortest path between them.Graformer learns to weight these nodenode relations differently for different attention heads, thus virtually learning differently connected views of the input graph.We evaluate Graformer on two popular graph-to-text generation benchmarks, AGENDA and WebNLG, where it achieves strong performance while using many fewer parameters than other approaches. 1
Budget optimized dynamic virtual machine provisioning in hybrid cloud using fuzzy analytic hierarchy process

Among various deployment models, Hybrid cloud is the preferred model allowing customers to maximize cost savings and performance by leveraging advantage of quick provisioning capabilities of public cloud. However, due to vast diversity of cloud services, today's struggle for the customers is to evaluate and discover the best fit Cloud Service Provider (CSP) for an efficient Virtual Machine (VM) provisioning in various hybrid cloud contracts of multi cloud environment. Currently there is no framework that allows customers to evaluate CSPs based on application workload, application environment and employ an budget optimized VM provisioning with dynamically varying factors while adhering to Service Level Agreement (SLA). Hence, the proposed VM Provisioning framework is a Multi Criteria Decision-Making (MCDM) model with an objective to provision the VMs from the well suited CSP in hybrid cloud. The framework initially derives weights for each decision-making input criteria considered as Quality of Service (QoS) Service Measurement Index (SMI) attributes using extent analysis method of Fuzzy Analytical processing (FHP). It ranks the CSP by aggregating weights of decision makers among the alternatives. The framework then proposes a budget optimized algorithm called BOPVM to provision VMs in the hybrid cloud based on the order of assigned rank to CSPs. The framework is evaluated and implemented on real time workload among the CSPs – Amazon, Azure and Openstack. The evaluation results shows significant provisioning cost savings of 50% with reduced provisioning time by 65% when compared with existing algorithms.
Bhattacharyya distance based concept drift detection method for evolving data stream

The majority of online learners assume that the data distribution to be learned is established in advance. There are many real-world problems where the distribution of the data changes as a function of time. Variations in data streams data distributions can sufficiently reduce the skill of the learning algorithm on new data, if the learning algorithm is not equipped to track such changes. Therefore, the algorithm should initiate required actions to make sure that the new information is learned properly. In this article, we propose Bhattacharyya Distance-based Concept Drift Detection Method (BDDM) which uses Bhattacharyya distance to identify gradual or abrupt variations in the distribution. Experiments executed in the MOA framework using three artificial data generators and ten real-world datasets suggest that BDDM improves the detections and accuracies in many scenarios.
Research trends in combinatorial optimization

Abstract Real‐world problems are becoming highly complex and therefore have to be solved with combinatorial optimization (CO) techniques. Motivated by the strong increase in publications on CO, 8393 articles from this research field are subjected to a bibliometric analysis. The corpus of literature is examined using mathematical methods and a novel algorithm for keyword analysis. In addition to the most relevant countries, organizations, and authors as well as their collaborations, the most pertinent CO problems, solution methods, and application areas are presented. Publications on CO focus mainly on the development or enhancement of metaheuristics like genetic algorithms. The increasingly problem‐oriented studies deal particularly with real‐world applications within the energy sector, production sector, or data management, which are of increasing relevance due to various global developments. The demonstration of global research trends in CO can support researchers in identifying the relevant issues regarding this expanding and transforming research area.
Distributed transactions on serverless stateful functions

Serverless computing is currently the fastest-growing cloud services segment. The most prominent serverless offering is Function-as-a-Service (FaaS), where users write functions and the cloud automates deployment, maintenance, and scalability. Although FaaS is a good fit for executing stateless functions, it does not adequately support stateful constructs like microservices and scalable, low-latency cloud applications, mainly because it lacks proper state management support and the ability to perform function-to-function calls. Most importantly, executing transactions across stateful functions remains an open problem.
Exploring the voluntary response to cyber-fraud: From vigilantism to responsibilisation

Online crimes such as frauds have grown substantially in volume over the last 20 years, with the associated changes in technology such as the internet and use of email. The state response through the police has lagged in many countries with law enforcement often struggling to cope with the volume and new challenges of such crimes. Private policing has traditionally filled the gap through commercial forms where clients are willing to pay through to voluntary actions such as vigilantism. Cybercrime has not escaped such outcomes with a growing voluntary sector responding to such crimes. In doing so there have been a wide range of such responses ranging from vigilantism through to state supported initiatives. This paper seeks to chart this voluntary response and in doing so conceptulizes it through three waves of voluntarism.
Multi-path-based quasi-real-time key provisioning in quantum-key-distribution enabled optical networks (QKD-ON)

With its information-theoretic security, quantum-key-distribution-enabled optical networks (QKD-ON) have become a promising candidate for future optical networks. The concept of quantum key pool (QKP) was introduced to offer an effective strategy for storing quantum keys. However, with the loss on its theoretical security due to storing these keys, balancing the storage of quantum keys and the security requirements of QKD-ONs poses a major challenge in their practical deployments. Hence, in this paper a concept of quasi-real-time key provisioning (QRT-KP) is introduced to address the tradeoff between quantum key storage and the degree of security. To satisfy the practical deployment of QRT-KP and the requirement of high-traffic flow, we propose a multi-path based QRT-KP (MP-QRT-KP) algorithm. Simulation results show that the MP-QRT-KP effectively enhances the performance of QKD-ONs in different scenarios, and it turns out that the algorithm performs better than single-path based QRT-KP (SP-QRT-KP) in terms of the success probability of key-allocation requests and key-resources utilization.
Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification.This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor.The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades.Besides, the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest (BO-RF) classifiers.This study uses four datasets (two collected from Indian hospitals-Ishita Pathology Center (IPC) of 4X, 10X, and 40X and Aster Medcity (AMC) of 10X, 20X, and 40X-two benchmark datasets-gland segmentation (GlaS) of 20X and IMEDIATREAT of 10X) comprising multiple microscope magnifications.Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy (97.25%-IPC, 94.40%-AMC, 97.58%-GlaS, 99.16%-Imediatreat), sensitivity (0.9725-IPC, 0.9440-AMC, 0.9807-GlaS, 0.9923-Imediatreat), specificity (0.9908-IPC, 0.9813-AMC, 0.9907-GlaS, 0.9971-Imediatreat) and F-score (0.9725-IPC, 0.9441-AMC, 0.9780-GlaS, 0.9923-Imediatreat).The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset, highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.
Privacy-Preserving Spatio-Temporal Keyword Search for Outsourced Location-Based Services

With the popularization of location-based services (LBS), encryption techniques have been utilized to protect data security when outsourcing LBS to cloud. However, existing schemes only consider spatial range search or keyword search, while expressive and practical search over encrypted LBS data is still a challenging problem. In this article, we introduce PrivSTL, a privacy-preserving spatio-temporal keyword search framework over the encrypted LBS data based on attribute-based encryption, linear encryption and RSA encryption. It allows mobile users to submit LBS query with spatial range, time interval and Boolean keyword expression, and provides accurate and authorized search by matching these query conditions and also the access policy. Then we introduce an extended scheme PrivSTG, which utilizes Geohash to divide the locations into grids, and outsources an encrypted index tree to cloud servers. PrivSTG improves the service efficiency by searching only over the ciphertexts in the surrounding grids of mobile user. Finally, we analyze the security of PrivSTL against chosen-plaintext, chosen-keyword and outside keyword-guessing attacks in generic bilinear group model, and show that PrivSTL guarantees the spatio-temporal keyword profile privacy, and also protects the query privacy. The experimental results indicate that our scheme is practical and efficient for outsourced LBS.
A Decentralized Music Copyright Operation Management System Based On Blockchain Technology

With the rapid development of the Internet, online music has become a new trend. The low cost of access and fast spread speed greatly enriches people’s spiritual life, but it also poses great challenges to the protection of intellectual property rights. Nowadays, although the intellectual property rights of music works have been better valued and protected, there are still some problems that limit the development of the music industry and affect the users’ experience. For example, the copyrights of different music works may be exclusively represented by different companies, resulting in limited choices for users and vicious competition among music operating companies, which affects the users’ experience and is not conducive to the long-term development of the music industry. In order to solve the above problems, we present a decentralized music copyright operation management system based on Blockchain technology. By using the Blockchain’s shared ledger mechanism and smart contracts as the basic framework of the Ethereum platform, we realize decentralized music copyright management. The system not only protects copyright better, but also coordinates the interests of creators, copyright owners, operators and users, which is beneficial to the further development of the music industry, and creates more value and better experience for users.
Deep HDR estimation with generative detail reconstruction

Abstract We study the problem of High Dynamic Range (HDR) image reconstruction from a Standard Dynamic Range (SDR) input with potential clipping artifacts. Instead of building a direct model that maps from SDR to HDR images as in previous work, we decompose an input SDR image into a base (low frequency) and detail layer (high frequency), and treat reconstructing these two layers as two separate problems. We propose a novel architecture that comprises individual components specially designed to handle both tasks. Specifically, our base layer reconstruction component recovers low frequency content and remaps the color gamut of the input SDR, whereas our detail layer reconstruction component, which builds upon prior work on image inpainting, hallucinates missing texture information. The output HDR prediction is produced by a final refinement stage. We present qualitative and quantitative comparisons with existing techniques where our method achieves state‐of‐the‐art performance.
Tweet Sentiment Analysis of the 2020 U.S. Presidential Election

In this paper, we conducted a tweet sentiment analysis of the 2020 U.S. Presidential Election between Donald Trump and Joe Biden. Specially, we identified the Multi-Layer Perceptron classifier as the methodology with the best performance on the Sanders Twitter benchmark dataset. We collected a sample of over 260,000 tweets related to the 2020 U.S. Presidential Election from the Twitter website via Twitter API, processed feature extraction, and applied Multi-Layer Perceptron to classify these tweets with a positive or negative sentiment. From the results, we concluded that (1) contrary to popular poll results, the candidates had a very close negative to positive sentiment ratio, (2) negative sentiment is more common and prominent than positive sentiment within the social media domain, (3) some key events can be detected by the trends of sentiment on social media, and (4) sentiment analysis can be used as a low-cost and easy alternative to gather political opinion.
Evaluating the Rationales of Amateur Investors

Social media's rise in popularity has demonstrated the usefulness of the wisdom of the crowd. Most previous works take into account the law of large numbers and simply average the results extracted from tasks such as opinion mining and sentiment analysis. Few attempt to identify high-quality opinions from the mined results. In this paper, we propose an approach for capturing expert-like rationales from social media platforms without the requirement of the annotated data. By leveraging stylistic and semantic features, our approach achieves an F1-score of 90.81%. The comparison between the rationales of experts and those of the crowd is done from stylistic and semantic perspectives, revealing that stylistic and semantic information provides complementary cues for professional rationales. We further show the advantage of using these superlative analysis results in the financial market, and find that top-ranked opinions identified by our approach increase potential returns by up to 90.31% and reduce downside risk by up to 71.69%, compared with opinions ranked by feedback from social media users. Moreover, the performance of our method on downside risk control is comparable with that of professional analysts.
Social media-based opinion retrieval for product analysis using multi-task deep neural networks

Social media platforms are considered one of the most effective intermediaries for companies to interact with consumers. Social media-based decision support systems for the marketing domain are highly developed, but product development and innovation-oriented studies remain limited. This study offers a novel approach which utilises opinion retrieval theme along with sentiment analysis to support the decision-making process for product analysis and development. To achieve this aim, we propose an end-to-end social media-based opinion retrieval system and utilise machine learning and natural language processing techniques. Google Glass is chosen as a use-case as this product was unable to achieve its commercial targets despite its superior technological offerings. We design a multi-task deep neural network architecture for the training of sentiment prediction and opinion detection tasks. We first divide the tweets containing certain useful opinions and suggestions into two categories based on their sentiment labels. The negative tweets are analysed to identify product-related concerns, whereas the positive and neutral tweets are used to extract innovative ideas and identify new use cases for product development. We visualise and interpret the clusters of keywords extracted from each sentiment label group. Apart from methodological contributions, this study offers practical contributions for the next generations of smart glasses.
Vision-based UAV Safe Landing exploiting Lightweight Deep Neural Networks

Recent advances in artificial intelligence, control and sensing technologies have facilitated the development of autonomous Unmanned Aerial Vehicles (UAVs, or drones) able to self-navigate in various settings. Although these technologies have already entered a mature stage, ensuring flight safety in crowded areas or performing an emergency landing in case of malfunctions, while adhering to relevant legislation, is generally treated as an afterthought when designing autonomous UAV platforms for unstructured environments. This paper proposes a UAV safe landing navigation pipeline that relies on lightweight computer vision modules, able to be executed on the limited computational resources on-board a typical UAV. Pre-trained Deep Neural Networks (DNNs) are mainly employed as the underlying building blocks, since deep learning has made a major impact on robotic perception by drastically improving the performance of relevant tasks, such as object detection or tracking, semantic image segmentation, etc. Evaluation of the proposed pipeline on a simulated environment indicates highly favorable results.