Reviewerly/exHarmony huggingface dataset:
Gradient-based optimization with ranking mechanisms for parameter identification of photovoltaic systems

Deriving optimal photovoltaic (PV) models’ optimal parameters have tremendous significance in simulating, evaluating, and controlling the photovoltaic systems. Determining unknown parameters of these PV models is a multimodal, nonlinear, and complex optimization problem. Hence, developing a robust optimization model to achieve optimal parameters of the PV models effectively is essential. This paper proposes an enhanced metaphor-free gradient-based optimizer (EGBO) for extracting PV parameters quickly, precisely, and reliably. In the EGBO, a rank-based mechanism is employed to update its parameters efficiently. Also, the logistic map (LC) is implemented to better use the local escaping operator (LEO) in the original GBO algorithm. The proposed EGBO optimally identifies various parameters in the PV model, such as single diodes, double diodes, and PV modules. The relevant results indicate that compared with most advanced optimization methods, the EGBO algorithm is competitive in reliability, accuracy, and convergence speed. Moreover, the relevant results from the experimental data drawn from the manufacturer’s datasheet demonstrate that the developed approach can offer highly accurate solutions at various irradiances and temperatures. Consequently, the achieved results confirm that the novel approach can be presented as a utility tool for deriving optimal PV models’ optimal parameters, and it can be helpful in modeling PV systems.
Removing Raindrops and Rain Streaks in One Go

Existing rain-removal algorithms often tackle either rain streak removal or raindrop removal, and thus may fail to handle real-world rainy scenes. Besides, the lack of real-world deraining datasets comprising different types of rain and their corresponding rain-free ground-truth also impedes deraining algorithm development. In this paper, we aim to address real-world deraining problems from two aspects. First, we propose a complementary cascaded network architecture, namely CCN, to remove rain streaks and raindrops in a unified framework. Specifically, our CCN removes raindrops and rain streaks in a complementary fashion, i.e., raindrop removal followed by rain streak removal and vice versa, and then fuses the results via an attention based fusion module. Considering significant shape and structure differences between rain streaks and raindrops, it is difficult to manually design a sophisticated network to remove them effectively. Thus, we employ neural architecture search to adaptively find optimal architectures within our specified deraining search space. Second, we present a new real-world rain dataset, namely RainDS, to prosper the development of deraining algorithms in practical scenarios. RainDS consists of rain images in different types and their corresponding rain-free ground-truth, including rain streak only, raindrop only, and both of them. Extensive experimental results on both existing benchmarks and RainDS demonstrate that our method outperforms the state-of-the-art.
Spatial-Temporal Transformer for Dynamic Scene Graph Generation

Dynamic scene graph generation aims at generating a scene graph of the given video. Compared to the task of scene graph generation from images, it is more challenging because of the dynamic relationships between objects and the temporal dependencies between frames allowing for a richer semantic interpretation. In this paper, we propose Spatial-temporal Transformer (STTran), a neural network that consists of two core modules: (1) a spatial encoder that takes an input frame to extract spatial context and reason about the visual relationships within a frame, and (2) a temporal decoder which takes the output of the spatial encoder as input in order to capture the temporal dependencies between frames and infer the dynamic relationships. Furthermore, STTran is flexible to take varying lengths of videos as input without clipping, which is especially important for long videos. Our method is validated on the benchmark dataset Action Genome (AG). The experimental results demonstrate the superior performance of our method in terms of dynamic scene graphs. Moreover, a set of ablative studies is conducted and the effect of each proposed module is justified. Code available at: https://github.com/yrcong/STTran.
MOST: A Multi-Oriented Scene Text Detector with Localization Refinement

Over the past few years, the field of scene text detection has progressed rapidly that modern text detectors are able to hunt text in various challenging scenarios. However, they might still fall short when handling text instances of extreme aspect ratios and varying scales. To tackle such difficulties, we propose in this paper a new algorithm for scene text detection, which puts forward a set of strategies to significantly improve the quality of text localization. Specifically, a Text Feature Alignment Module (TFAM) is proposed to dynamically adjust the receptive fields of features based on initial raw detections; a Position-Aware Non-Maximum Suppression (PA-NMS) module is devised to selectively concentrate on reliable raw detections and exclude unreliable ones; besides, we propose an Instance-wise IoU loss for balanced training to deal with text instances of different scales. An extensive ablation study demonstrates the effectiveness and superiority of the proposed strategies. The resulting text detection system, which integrates the proposed strategies with a leading scene text detector EAST, achieves state-of-the-art or competitive performance on various standard benchmarks for text detection while keeping a fast running speed.
Locate then Segment: A Strong Pipeline for Referring Image Segmentation

Referring image segmentation aims to segment the objects referred by a natural language expression. Previous methods usually focus on designing an implicit and recurrent feature interaction mechanism to fuse the visual-linguistic features to directly generate the final segmentation mask without explicitly modeling the localization information of the referent instances. To tackle these problems, we view this task from another perspective by decoupling it into a "Locate-Then-Segment" (LTS) scheme. Given a language expression, people generally first perform attention to the corresponding target image regions, then generate a fine segmentation mask about the object based on its context. The LTS first extracts and fuses both visual and textual features to get a cross-modal representation, then applies a cross-model interaction on the visual-textual features to locate the referred object with position prior, and finally generates the segmentation result with a light-weight segmentation network. Our LTS is simple but surprisingly effective. On three popular benchmark datasets, the LTS outperforms all the previous state-of-the-arts methods by a large margin (e.g., +3.2% on RefCOCO+ and +3.4% on RefCOCOg). In addition, our model is more interpretable with explicitly locating the object, which is also proved by visualization experiments. We believe this framework is promising to serve as a strong baseline for referring image segmentation.
Security, Privacy, and Reliability in Digital Healthcare Systems Using Blockchain

According to the security breach level index, millions of records are stolen worldwide on every single day. Personal health records are the most targeted records on the internet, and they are considered sensitive, and valuable. Security and privacy are the most important parameters of cryptography and encryption. They reduce the availability of data on patients and healthcare to the appropriate personnel and ultimately lead to a barrier in the transfer of healthcare into a digital health system. Using a permission blockchain to share healthcare data can reduce security and privacy issues. According to the literature, most healthcare systems rely on a centralized system, which is more prone to security vulnerabilities. The existing blockchain-based healthcare schemes provide only a data-sharing framework, but they lack security and privacy. To cope with these kinds of security issues, we have designed a novel security algorithm that provides security as well as privacy with much better efficiency and a lower cost. Hence, in this research, we have proposed a patient healthcare framework that provides greater security, reliability, and authentication compared to existing blockchain-based access control.
A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search

Approximate nearest neighbor search (ANNS) constitutes an important operation in a multitude of applications, including recommendation systems, information retrieval, and pattern recognition. In the past decade, graph-based ANNS algorithms have been the leading paradigm in this domain, with dozens of graph-based ANNS algorithms proposed. Such algorithms aim to provide effective, efficient solutions for retrieving the nearest neighbors for a given query. Nevertheless, these efforts focus on developing and optimizing algorithms with different approaches, so there is a real need for a comprehensive survey about the approaches' relative performance, strengths, and pitfalls. Thus here we provide a thorough comparative analysis and experimental evaluation of 13 representative graph-based ANNS algorithms via a new taxonomy and fine-grained pipeline. We compared each algorithm in a uniform test environment on eight real-world datasets and 12 synthetic datasets with varying sizes and characteristics. Our study yields novel discoveries, offerings several useful principles to improve algorithms, thus designing an optimized method that outperforms the state-of-the-art algorithms. This effort also helped us pinpoint algorithms' working portions, along with rule-of-thumb recommendations about promising research directions and suitable algorithms for practitioners in different fields.
Blockchain-Based Security Mechanism for the Medical Data at Fog Computing Architecture of Internet of Things

The recent developments in fog computing architecture and cloud of things (CoT) technology includes data mining management and artificial intelligence operations. However, one of the major challenges of this model is vulnerability to security threats and cyber-attacks against the fog computing layers. In such a scenario, each of the layers are susceptible to different intimidations, including the sensed data (edge layer), computing and processing of data (fog (layer), and storage and management for public users (cloud). The conventional data storage and security mechanisms that are currently in use appear to not be suitable for such a huge amount of generated data in the fog computing architecture. Thus, the major focus of this research is to provide security countermeasures against medical data mining threats, which are generated from the sensing layer (a human wearable device) and storage of data in the cloud database of internet of things (IoT). Therefore, we propose a public-permissioned blockchain security mechanism using elliptic curve crypto (ECC) digital signature that that supports a distributed ledger database (server) to provide an immutable security solution, transaction transparency and prevent the patient records tampering at the IoTs fog layer. The blockchain technology approach also helps to mitigate these issues of latency, centralization, and scalability in the fog model.
Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal

Rain streaks and raindrops are two natural phenomena, which degrade image capture in different ways. Currently, most existing deep deraining networks take them as two distinct problems and individually address one, and thus cannot deal adequately with both simultaneously. To address this, we propose a Dual Attention-in-Attention Model (DAiAM) which includes two DAMs for removing both rain streaks and raindrops. Inside the DAM, there are two attentive maps - each of which attends to the heavy and light rainy regions, respectively, to guide the deraining process differently for applicable regions. In addition, to further refine the result, a Differential-driven Dual Attention-in-Attention Model (D-DAiAM) is proposed with a “heavy-to-light" scheme to remove rain via addressing the unsatisfying deraining regions. Extensive experiments on one public raindrop dataset, one public rain streak and our synthesized joint rain streak and raindrop (JRSRD) dataset have demonstrated that the proposed method not only is capable of removing rain streaks and raindrops simultaneously, but also achieves the state-of-the-art performance on both tasks.
How can we predict the impact of the social media messages on the value of cryptocurrency? Insights from big data analytics

Cryptocurrency and blockchain are one of the most beautiful digital transformations occurring around the world. They have changed the orthodox meaning and working of currency as we know it. It is interesting to note how it excites and worries some. The main reason for the popularity of cryptocurrencies is tremendous returns in very little time. Social media platforms like twitter, provide a safe-place where individuals’ can share their thoughts as well as mindsets, which then can be heard and be reciprocated by others. This paper aims to draw a correlation between the hyped tweets and the prices of cryptocurrencies like Bitcoin - The Crypto King and Dogecoin - The Memecoin during those times. We also aim to predict the future price values of Bitcoin using its past values. By using cryptocurrencies’ financial data, twitter data, RAPIDS and cuml, a fine line can be drawn between the amount of impact tweets have on people as well as on the market. The tweets on cryptocurrency were segregated and price forecasting was done using augmented dickey fuller test and ARIMA models, 10 future values of bitcoin were predicted with 96% accuracy and 0.0395 average error.Besides, from the investigations above of the authentic cost of BTC, it is perfectly clear that there have been way more steep falls in the history of Cryptocurrencies even before Elon started tweeting about it. Thus, it can clearly be stated that no one person can control the utter volatile world of cryptocurrencies! And the decentralized system ledger of cryptocurrency remains unharmed.
Blockchain-Based Secured IPFS-Enable Event Storage Technique With Authentication Protocol in VANET

In recent decades, intelligent transportation systems (ITS) have improved drivers' safety and have shared information (such as traffic congestion and accidents) in a very efficient way. However, the privacy of vehicles and the security of event information is a major concern. The problem of secure sharing of event information without compromising the trusted third party (TTP) and data storage is the main issue in ITS. Blockchain technologies can resolve this problem. A work has been published on blockchain-based protocol for secure sharing of events and authentication of vehicles. This protocol addresses the issue of the safe storing of event information. However, authentication of vehicles solely depends on the cloud server. As a result, their scheme utilizes the notion of partially decentralized architecture. This paper proposes a novel decentralized architecture for the vehicular ad-hoc network (VANET) without the cloud server. This work also presents a protocol for securing event information and vehicle authentication using the blockchain mechanism. In this protocol, the registered user accesses the event information securely from the interplanetary file system (IPFS). We incorporate the IPFS, along with blockchain, to store the information in a fully distributed manner. The proposed protocol is compared with the state-of-the-art. The comparison provides desirable security at a reasonable cost. The evaluation of the proposed smart contract in terms of cost (GAS) is also discussed.
Novel Vote Scheme for Decision-Making Feedback Based on Blockchain in Internet of Vehicles

Obtaining timely and accurate traffic information is one of the most important problems in intelligent transportation system, which will make vehicles run smoothly, avoid road congestion, save road running time and reduce vehicle energy consumption. In the current Internet of Vehicles system, the traffic management center can learn from the feedback information of all vehicles to improve the ability of decision-making and traffic command. However, the existing feedback mechanism does not respond to the spatial-temporal characteristics of data in time, due to the lack of communication capability of the current equipment. So, it cannot meet the requirements of ultra-low delay, high reliability and high security in the Internet of Vehicles. To solve this problem, this paper proposes a blockchain-based proxy vote and revocation scheme for decision feedback in Internet of Vehicles, which allows the intelligent system to ignore the unevenness and heterogeneity in the 6G technology. In addition, blockchain technology notarizes the vote data of vehicles and outsources microservices. Secondly, we use the attributes of decision-related nodes instead of their identities to enable anonymous vote. Smart contracts can automatically expand the scalability of outsourced microservices. Finally, the security proof of the proposed scheme ensures the security and consistency of outsourced microservices. The simulation results also show that our scheme greatly improves the efficiency of voting feedback.
Adaptive reverse graph learning for robust subspace learning

Subspace learning decreases the dimensions for high-dimensional data by projecting the original data into a low-dimensional subspace, as well as preserving the similarity among data points. Besides, this way has been becoming one of the popular techniques of dimensionality reduction. However, many previous methods either conduct the transformation process or preserve the similarity structure on the original space, which usually contains redundancy and noise. As a result, the effectiveness of subspace learning is affected. Therefore, we design two strategies to minimize the impact of both redundancy and noisy data from the original space, i.e., reverse graph embedding and robust estimators. Specifically, we use the reverse graph embedding in the original space to learn the transformation projection, considering local relation among data from the low-dimensional subspace. We also automatically distribute large weights and small values, respectively, to important samples and unimportant samples by robust estimators for decreasing the effect of redundancy and noise. Moreover, we combine these two strategies to construct subspace learning in one architecture. Experiments on 12 practical and 2 location-based social network datasets show that the proposed method is superior to other subspace learning methods of state-of-the-art selection, regarding different evaluation metrics.
Regularization by Denoising via Fixed-Point Projection (RED-PRO)

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 11 May 2020Accepted: 19 May 2021Published online: 23 September 2021Keywordsinverse problems, image denoising, regularization by denoising, RED, demicontractive mappings, fixed-point set, plug-and-play prior, PnPAMS Subject Headings62H35, 68U10, 94A08, 65F10, 65F22, 47A52Publication DataISSN (online): 1936-4954Publisher: Society for Industrial and Applied MathematicsCODEN: sjisbi
Probing Classifiers: Promises, Shortcomings, and Advances

Abstract Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. The basic idea is simple—a classifier is trained to predict some linguistic property from a model’s representations—and has been used to examine a wide variety of models and properties. However, recent studies have demonstrated various methodological limitations of this approach. This squib critically reviews the probing classifiers framework, highlighting their promises, shortcomings, and advances.
Modulated Graph Convolutional Network for 3D Human Pose Estimation

The graph convolutional network (GCN) has recently achieved promising performance of 3D human pose estimation (HPE) by modeling the relationship among body parts. However, most prior GCN approaches suffer from two main drawbacks. First, they share a feature transformation for each node within a graph convolution layer. This prevents them from learning different relations between different body joints. Second, the graph is usually defined according to the human skeleton and is suboptimal because human activities often exhibit motion patterns beyond the natural connections of body joints. To address these limitations, we introduce a novel Modulated GCN for 3D HPE. It consists of two main components: weight modulation and affinity modulation. Weight modulation learns different modulation vectors for different nodes so that the feature transformations of different nodes are disentangled while retaining a small model size. Affinity modulation adjusts the graph structure in a GCN so that it can model additional edges beyond the human skeleton. We investigate several affinity modulation methods as well as the impact of regularizations. Rigorous ablation study indicates both types of modulation improve performance with negligible overhead. Compared with state-of-the-art GCNs for 3D HPE, our approach either significantly reduces the estimation errors, e.g., by around 10%, while retaining a small model size or drastically reduces the model size, e.g., from 4.22M to 0.29M (a 14.5× reduction), while achieving comparable performance. Results on two benchmarks show our Modulated GCN outperforms some recent states of the art. Our code is available at https://github.com/ZhimingZo/Modulated-GCN.
Occlude Them All: Occlusion-Aware Attention Network for Occluded Person Re-ID

Person Re-Identification (ReID) has achieved remarkable performance along with the deep learning era. However, most approaches carry out ReID only based upon holistic pedestrian regions. In contrast, real-world scenarios involve occluded pedestrians, which provide partial visual appearances and destroy the ReID accuracy. A common strategy is to locate visible body parts by auxiliary model, which however suffers from significant domain gaps and data bias issues. To avoid such problematic models in occluded person ReID, we propose the Occlusion-Aware Mask Network (OAMN). In particular, we incorporate an attention-guided mask module, which requires guidance from labeled occlusion data. To this end, we propose a novel occlusion augmentation scheme that produces diverse and precisely labeled occlusion for any holistic dataset. The proposed scheme suits real-world scenarios better than existing schemes, which consider only limited types of occlusions. We also offer a novel occlusion unification scheme to tackle ambiguity information at the test phase. The above three components enable existing attention mechanisms to precisely capture body parts regardless of the occlusion. Comprehensive experiments on a variety of person ReID benchmarks demonstrate the superiority of OAMN over state-of-the-arts.