Reviewerly/exHarmony huggingface dataset:
Improving the Performance of Sentiment Analysis of Tweets Containing Fuzzy Sentiment Using the Feature Ensemble Model

The increase in the volume of user-generated content on Twitter has resulted in tweet sentiment analysis becoming an essential tool for the extraction of information about Twitter users' emotional state.Consequently, there has been a rapid growth of tweet sentiment analysis in the area of natural language processing.Tweet sentiment analysis is increasingly applied in many areas, such as decision support systems and recommendation systems.Therefore, improving the accuracy of tweet sentiment analysis has become practical and an area of interest for many researchers.Many approaches have tried to improve the performance of tweet sentiment analysis methods by using the feature ensemble method.However, most of the previous methods attempted to model the syntactic information of words without considering the sentiment context of these words.Besides, the positioning of words and the impact of phrases containing fuzzy sentiment have not been mentioned in many studies.This study proposed a new approach based on a feature ensemble model related to tweets containing fuzzy sentiment by taking into account elements such as lexical, word-type, semantic, position, and sentiment polarity of words.The proposed method has been experimented on with real data, and the result proves effective in improving the performance of tweet sentiment analysis in terms of the F 1 score.
Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images

Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy.
Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism

As one of the major frauds in financial services, cash-out fraud is that users pursue cash gains with illegal or insincere means. Conventional solutions for the cash-out user detection are to perform subtle feature engineering for each user and then apply a classifier, such as GDBT and Neural Network. However, users in financial services have rich interaction relations, which are seldom fully exploited by conventional solutions. In this paper, with the real datasets in Ant Credit Pay of Ant Financial Services Group, we first study the cashout user detection problem and propose a novel hierarchical attention mechanism based cash-out user detection model, called HACUD. Specifically, we model different types of objects and their rich attributes and interaction relations in the scenario of credit payment service with an Attributed Heterogeneous Information Network (AHIN). The HACUD model enhances feature representation of objects through meta-path based neighbors exploiting different aspects of structure information in AHIN. Furthermore, a hierarchical attention mechanism is elaborately designed to model user’s preferences towards attributes and meta-paths. Experimental results on two real datasets show that the HACUD outperforms the state-of-the-art methods.
Discriminative Feature Learning With Consistent Attention Regularization for Person Re-Identification

Person re-identification (Re-ID) has undergone a rapid development with the blooming of deep neural network. Most methods are very easily affected by target misalignment and background clutter in the training process. In this paper, we propose a simple yet effective feedforward attention network to address the two mentioned problems, in which a novel consistent attention regularizer and an improved triplet loss are designed to learn foreground attentive features for person Re-ID. Specifically, the consistent attention regularizer aims to keep the deduced foreground masks similar from the low-level, mid-level and high-level feature maps. As a result, the network will focus on the foreground regions at the lower layers, which is benefit to learn discriminative features from the foreground regions at the higher layers. Last but not least, the improved triplet loss is introduced to enhance the feature learning capability, which can jointly minimize the intra-class distance and maximize the inter-class distance in each triplet unit. Experimental results on the Market1501, DukeMTMC-reID and CUHK03 datasets have shown that our method outperforms most of the state-of-the-art approaches.
The development of energy blockchain and its implications for China's energy sector

With technology progressing and the decreasing cost of renewable energy, consumers require energy supply to be smarter, cleaner, and more sustainable than before. By providing a decentralized trading mechanism, blockchain technology can facilitate sustainable energy consumption and achieve a circular economy. This study analyzes how China can employ blockchain technology to reform its energy sector. We survey the progress of blockchain technology in the energy sector and explore typical cases of energy blockchains in the world. We discuss the advantages and disadvantages of applying blockchain to China's energy sector. China's monopoly market structure in energy supply impedes the application of blockchain technology, but the expansion of clean energy provides a huge opportunity for it. Although China's technological level is lagging, the biggest obstacle is not technology but rather policy. We conclude that China should loosen its regulatory environment, amend the relevant laws, and balance the conflict between management and innovation.
Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators

The highlight of this paper is to propose an innovative approach to compute an optimal collision free trajectory path for each robot in a known and complex environment. The problem under consideration has been solved by employing an improved version of particle swarm optimization (IPSO) with evolutionary operators (EOPs). In the present context, PSO is improved with the concept of governance in human society and two evolutionary operators such as multi-crossover inherited from the genetic algorithm, and bee colony operator to enhance the intensification capability of the IPSO algorithm. The algorithm proposed to compute the deadlock free subsequent coordinate of an individual robot from their present coordinate, in addition, to minimize the path length for each robot by maintaining a good balance between intensification and diversification. Results obtained from the proposed IPSO-EOPs have been compared with competitors such as DE and IPSO in a similar environment to substantiate the robustness and usefulness of the algorithm. It perceives from the result obtained from simulation and experimentation that IPSO-EOPs is succeeding IPSO, and DE in terms of arrival time, generating a safe optimal path, and energy utilization during the travel.
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception

In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-time applications.We present Deep SESR, a residualin-residual network-based generative model that can learn to restore perceptual image qualities at 2×, 3×, or 4× higher spatial resolution.We supervise its training by formulating a multi-modal objective function that addresses the chrominancespecific underwater color degradation, lack of image sharpness, and loss in high-level feature representation.It is also supervised to learn salient foreground regions in the image, which in turn guides the network to learn global contrast enhancement.We design an end-to-end training pipeline to jointly learn the saliency prediction and SESR on a shared hierarchical feature space for fast inference.Moreover, we present UFO-120, the first dataset to facilitate large-scale SESR learning; it contains over 1500 training samples and a benchmark test set of 120 samples.By thorough experimental evaluation on UFO-120 and several other standard datasets, we demonstrate that Deep SESR outperforms the existing solutions for underwater image enhancement and super-resolution.We also validate its generalization performance on several test cases that include underwater images with diverse spectral and spatial degradation levels, and also terrestrial images with unseen natural objects.Lastly, we analyze its computational feasibility for single-board deployments and demonstrate its operational benefits for visually-guided underwater robots.
A blockchain-based scheme for privacy-preserving and secure sharing of medical data

How to alleviate the contradiction between the patient's privacy and the research or commercial demands of health data has become the challenging problem of intelligent medical system with the exponential increase of medical data. In this paper, a blockchain-based privacy-preserving scheme is proposed, which realizes secure sharing of medical data between several entities involved patients, research institutions and semi-trusted cloud servers. And meanwhile, it achieves the data availability and consistency between patients and research institutions, where zero-knowledge proof is employed to verify whether the patient's medical data meets the specific requirements proposed by research institutions without revealing patients’ privacy, and then the proxy re-encryption technology is adopted to ensure that research institutions can decrypt the intermediary ciphertext. In addition, this proposal can execute distributed consensus based on PBFT algorithm for transactions between patients and research institutions according to the prearranged terms. Theoretical analysis shows the proposed scheme can satisfy security and privacy requirements such as confidentiality, integrity and availability, as well as performance evaluation demonstrates it is feasible and efficient in contrast with other typical schemes.
TokenScope

Motivated by the success of Bitcoin, lots of cryptocurrencies have been created, the majority of which were implemented as smart contracts running on Ethereum and called tokens. To regulate the interaction between these tokens and users as well as third-party tools (e.g., wallets, exchange markets, etc.), several standards have been proposed for the implementation of token contracts. Although existing tokens involve lots of money, little is known whether or not their behaviors are consistent with the standards. Inconsistent behaviors can lead to user confusion and financial loss, because users/third-party tools interact with token contracts by invoking standard interfaces and listening to standard events. In this work, we take the first step to investigate such inconsistent token behaviors with regard to ERC-20, the most popular token standard. We propose a novel approach to automatically detect such inconsistency by contrasting the behaviors derived from three different sources, including the manipulations of core data structures recording the token holders and their shares, the actions indicated by standard interfaces, and the behaviors suggested by standard events. We implement our approach in a new tool named TokenScope and use it to inspect all transactions sent to the deployed tokens. We detected 3,259,001 transactions that trigger inconsistent behaviors, and these behaviors resulted from 7,472 tokens. By manually examining all (2,353) open-source tokens having inconsistent behaviors, we found that the precision of TokenScope is above 99.9%. Moreover, we revealed 11 major reasons behind the inconsistency, e.g., flawed tokens, standard methods missing, lack of standard events, etc. In particular, we discovered 50 unreported flawed tokens.
A national survey of attitudes to COVID-19 digital contact tracing in the Republic of Ireland

Contact tracing remains a critical part of controlling COVID-19 spread. Many countries have developed novel software applications (Apps) in an effort to augment traditional contact tracing methods. Conduct a national survey of the Irish population to examine barriers and levers to the use of a contact tracing App. Adult participants were invited to respond via an online survey weblink sent via e-mail and messaging Apps and posted on our university website and on popular social media platforms, prior to launch of the national App solution. A total of 8088 responses were received, with all 26 counties of the Republic of Ireland represented. Fifty-four percent of respondents said they would definitely download a contact-tracing App, while 30% said they would probably download a contact tracing App. Ninety-five percent of respondents identified at least one reason for them to download such an App, with the most common reasons being the potential for the App to help family members and friends and a sense of responsibility to the wider community. Fifty-nine percent identified at least one reason not to download the App, with the most common reasons being fear that technology companies or the government might use the App technology for greater surveillance after the pandemic. The Irish citizens surveyed expressed high levels of willingness to download a public health–backed App to augment contact tracing. Concerns raised regarding privacy and data security will be critical if the App is to achieve the large-scale adoption and ongoing use required for its effective operation.
Efficient Transformer for Remote Sensing Image Segmentation

Semantic segmentation for remote sensing images (RSIs) is widely applied in geological surveys, urban resources management, and disaster monitoring. Recent solutions on remote sensing segmentation tasks are generally addressed by CNN-based models and transformer-based models. In particular, transformer-based architecture generally struggles with two main problems: a high computation load and inaccurate edge classification. Therefore, to overcome these problems, we propose a novel transformer model to realize lightweight edge classification. First, based on a Swin transformer backbone, a pure Efficient transformer with mlphead is proposed to accelerate the inference speed. Moreover, explicit and implicit edge enhancement methods are proposed to cope with object edge problems. The experimental results evaluated on the Potsdam and Vaihingen datasets present that the proposed approach significantly improved the final accuracy, achieving a trade-off between computational complexity (Flops) and accuracy (Efficient-L obtaining 3.23% mIoU improvement on Vaihingen and 2.46% mIoU improvement on Potsdam compared with HRCNet_W48). As a result, it is believed that the proposed Efficient transformer will have an advantage in dealing with remote sensing image segmentation problems.
A bibliometric review of finance bibliometric papers

As bibliometric approaches are increasingly a focus of literature reviews across different business disciplines, we apply a bibliometric approach to a review of bibliometric studies in finance, assessing 121 bibliometric studies. We identify four literature clusters: studies focusing on assessments of literature in trending topics; analysis of papers that employ emerging econometric techniques; studies grouped around particular fundamental topics such as market dynamics, behavioral finance, and corporate governance; and studies focused on retrospective celebration of single well-known finance journals. We identify which countries, institutions, and scholars have been being particularly influential. Results suggest that the field of economics has been more comfortable publishing bibliometric studies than finance. Results also identify the mainstream finance journals that are particularly leading in publishing bibliometric studies, with results showing that these journals have particularly high-growing scholarly impact. We conclude that bibliometric studies are an important vehicle for highlighting and motivating emerging scholarship.
Amplification by shuffling: from local to central differential privacy via anonymity

Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. For example, trends in users' private preferences or software usage may be monitored via such reports. We study the collection of such statistics in the local differential privacy (LDP) model, and describe an algorithm whose privacy cost is polylogarithmic in the number of changes to a user's value.More fundamentally---by building on anonymity of the users' reports---we also demonstrate how the privacy cost of our LDP algorithm can actually be much lower when viewed in the central model of differential privacy. We show, via a new and general privacy amplification technique, that any permutation-invariant algorithm satisfying e-local differential privacy will satisfy [MATH HERE]-central differential privacy. By this, we explain how the high noise and [MATH HERE] overhead of LDP protocols is a consequence of them being significantly more private in the central model. As a practical corollary, our results imply that several LDP-based industrial deployments may have much lower privacy cost than their advertised e would indicate---at least if reports are anonymized.
Elderly Fall Detection Using Wearable Sensors: A Low Cost Highly Accurate Algorithm

Every year, more than 37 million falls that require medical attention occur. The elderly suffers the greatest number of fatal falls. Therefore, automatic fall detection for the elderly is one of the most important health-care applications as it enables timely medical intervention. The fall detection problem has extensively been studied over the last decade. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable algorithms with feasible computational cost is still an open research challenge. In this paper, a low-cost highly accurate machine learning-based fall detection algorithm is proposed. Particularly, a novel online feature extraction method that efficiently employs the time characteristics of falls is proposed. In addition, a novel design of a machine learning-based system is proposed to achieve the best accuracy/numerical complexity tradeoff. The low computational cost of the proposed algorithm not only enables to embed it in a wearable sensor but also makes the power requirements quite low and hence enhances the autonomy of the wearable device, where the need for battery recharge/replace is minimized. Experimental results on a large open dataset show that the accuracy of the proposed algorithm exceeds 99.9% with a computational cost of less than 500 floating point operations per second.
Learning to Spot and Refactor Inconsistent Method Names

To ensure code readability and facilitate software maintenance, program methods must be named properly. In particular, method names must be consistent with the corresponding method implementations. Debugging method names remains an important topic in the literature, where various approaches analyze commonalities among method names in a large dataset to detect inconsistent method names and suggest better ones. We note that the state-of-the-art does not analyze the implemented code itself to assess consistency. We thus propose a novel automated approach to debugging method names based on the analysis of consistency between method names and method code. The approach leverages deep feature representation techniques adapted to the nature of each artifact. Experimental results on over 2.1 million Java methods show that we can achieve up to 15 percentage points improvement over the state-of-the-art, establishing a record performance of 67.9% F1- measure in identifying inconsistent method names. We further demonstrate that our approach yields up to 25% accuracy in suggesting full names, while the state-of-the-art lags far behind at 1.1% accuracy. Finally, we report on our success in fixing 66 inconsistent method names in a live study on projects in the wild.
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions

Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally enumerate all the cross features under a predefined maximum order, and then identify useful feature interactions through model training, which suffer from two drawbacks. First, they have to make a trade-off between the expressiveness of higher-order cross features and the computational cost, resulting in suboptimal predictions. Second, enumerating all the cross features, including irrelevant ones, may introduce noisy feature combinations that degrade model performance. In this work, we propose the Adaptive Factorization Network (AFN), a new model that learns arbitrary-order cross features adaptively from data. The core of AFN is a logarithmic transformation layer that converts the power of each feature in a feature combination into the coefficient to be learned. The experimental results on four real datasets demonstrate the superior predictive performance of AFN against the state-of-the-arts.