Reviewerly/exHarmony huggingface dataset:
Overview of the track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text

Sentiment analysis of Dravidian languages has received attention in recent years. However, most social media text is code-mixed and there is no research available on sentiment analysis of code-mixed Dravidian languages. The Dravidian-CodeMix-FIRE 2020, a track on Sentiment Analysis for Dravidian Languages in Code-Mixed Text, focused on creating a platform for researchers to come together and investigate the problem. There were two languages for this track: (i) Tamil, and (ii) Malayalam. The participants were given a dataset of YouTube comments and the goal of the shared task submissions was to recognise the sentiment of each comment by classifying them into positive, negative, neutral, mixed-feeling classes or by recognising whether the comment is not in the intended language. The performance of the systems was evaluated by weighted-F1 score.
An Architecture and Performance Evaluation of Blockchain-Based Peer-to-Peer Energy Trading

Several recent studies have suggested Blockchain for Peer-to-Peer energy trading (P2P-ET) to achieve better security, privacy and fast payment settlement. Most of them however rely on either public Blockchains (which have low performance) or permissioned blockchains (which have low decentralization level and do not provide byzantine fault tolerance). Moreover, these solutions have limitations when capturing the business model of existing energy trading systems. This article proposes a Unified permissioned blockchain-based P2P-ET Architecture (UBETA) that integrates three different types of energy markets and provides a unified energy trading and payment settlement model. The UBETA system is based on an enterprise Ethereum Blockchain, known as Hyperledger Besu, and Istanbul Byzantine Fault Tolerance (IBFT) consensus algorithm. We compared the performance of the proposed IBFT-based system with three existing systems (i.e., Ethereum Clique, Ethereum Proof of Work and Hyperledger Fabric's Raft) using specific performance metrics (i.e., read/write transaction latency, read/write transaction throughput and fail rate). The experiments were carried out on a network size of up to 60 nodes and a real energy trading data set from the Western Australian energy market was used. The experiment results indicate that the IBFT-based system has 15x lower latency and nearly 2x throughput compared to existing Proof of Work based P2P-ET solutions. Moreover, the system provides better scalability and success rate than existing Raft based P2P-ET systems: the fail rate of the IBFT-based system only increased by 11% while that of Raft increased by 20% when increasing the number of nodes from 20 to 60. In addition, the proposed unified energy trading model provides lower latency and reduces the number of blockchain transactions compared to the non-unified counterpart.
Relaxed Transformer Decoders for Direct Action Proposal Generation

Temporal action proposal generation is an important and challenging task in video understanding, which aims at detecting all temporal segments containing action in-stances of interest. The existing proposal generation approaches are generally based on pre-defined anchor windows or heuristic bottom-up boundary matching strategies. This paper presents a simple and efficient framework (RTD-Net) for direct action proposal generation, by re-purposing a Transformer-alike architecture. To tackle the essential visual difference between time and space, we make three important improvements over the original transformer detection framework (DETR). First, to deal with slowness prior in videos, we replace the original Transformer en-coder with a boundary attentive module to better capture long-range temporal information. Second, due to the ambiguous temporal boundary and relatively sparse annotations, we present a relaxed matching scheme to relieve the strict criteria of single assignment to each groundtruth. Finally, we devise a three-branch head to further improve the proposal confidence estimation by explicitly predicting its completeness. Extensive experiments on THUMOS14 and ActivityNet-1.3 benchmarks demonstrate the effectiveness of RTD-Net, on both tasks of temporal action proposal generation and temporal action detection. Moreover, due to its simplicity in design, our framework is more efficient than previous proposal generation methods, without non-maximum suppression post-processing. The code and models are made available at https://github.com/MCG-NJU/RTD-Action.
Blockchain applications and architectures for port operations and logistics management

Efficient port logistic operations and management are critical for global trade and transportation services. The current port logistic handling systems are highly centralized and offer limited opportunities for collaboration among diverse stakeholders. Moreover, existing systems fall short of providing traceability, transparency, information security, and immutability of data stored and exchanged during various operational pro- cesses. As a result, the productivity of port terminals is adversely affected. Blockchain is an emerging technology that provides traceability, transparency, auditability through immutable prove- nance data of on-chain trusted transactions, in a decentralized manner without intermediaries or trusted third parties. In this paper, we discuss the potential role of blockchain technology in transforming port logistic operations and services, provide use cases and frameworks that improve port logistics operations. Furthermore, we employ permissioned blockchain architectures to map port logistics services to highlight system components, participants, and deployment alternatives to automate logistics operations. Finally, we outline open research issues and chal- lenges that are barriers to adoption
Underwater target detection based on Faster R-CNN and adversarial occlusion network

Underwater target detection is an important part of ocean exploration, which has important applications in military and civil fields. Since the underwater environment is complex and changeable and the sample images that can be obtained are limited, this paper proposes a method to add the adversarial occlusion network (AON) to the standard Faster R-CNN detection algorithm which called Faster R-CNN-AON network. The AON network has a competitive relationship with the Faster R-CNN detection network, which learns how to block a given target and make it difficult for the detecting network to classify the blocked target correctly. Faster R-CNN detection network and the AON network compete and learn together, and ultimately enable the detection network to obtain better robustness for underwater seafood. The joint training of Faster R-CNN and the adversarial network can effectively prevent the detection network from overfitting the generated fixed features. The experimental results in this paper show that compared with the standard Faster R-CNN network, the increase of mAP on VOC07 data set is 2.6%, and the increase of mAP on the underwater data set is 4.2%.
Self-Supervised Learning of Graph Neural Networks: A Unified Review

Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new paradigm for making use of large amounts of unlabeled samples. SSL has achieved promising performance on natural language and image learning tasks. Recently, there is a trend to extend such success to graph data using graph neural networks (GNNs). In this survey, we provide a unified review of different ways of training GNNs using SSL. Specifically, we categorize SSL methods into contrastive and predictive models. In either category, we provide a unified framework for methods as well as how these methods differ in each component under the framework. Our unified treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms. We also summarize different SSL settings and the corresponding datasets used in each setting. To facilitate methodological development and empirical comparison, we develop a standardized testbed for SSL in GNNs, including implementations of common baseline methods, datasets, and evaluation metrics.
Combining improved genetic algorithm and matrix semi-tensor product (STP) in color image encryption

Image encryption is one of the important methods for preservation of confidentiality and integrity of digital images. In this paper, a color image cryptosystem based on improved genetic algorithm and matrix semi-tensor product (STP) is introduced. The encryption process is composed of five stages, preprocessing, DNA encoding, crossover, mutation and DNA decoding. Firstly, the color plain image is splitted into its red, green and blue components, and they are preprocessed by an adaptive block-based image preprocessing driven by semi-tensor product (ABPSTP) to get new components. Next, the obtained components are converted to DNA sequences according to DNA encoding rules. Subsequently, the resulting sequences are shuffled by a double crossover operation of inter-intra components (DCOIC), and diffused by a DNA complementary cycle mutation strategy (DCCMS). Finally, the cipher image is obtained by decoding the diffused DNA components and recombining them. The selection of crossover components and determination of intersection points are controlled by plain image information and chaotic sequences, the mutation position and mutation rule of the element are dependable on them, thus our algorithm may withstand known-plaintext and chosen-plaintext attacks effectively. Simulation results demonstrate that our cipher has large key space, high key sensitivity, and it may resist various attacks.
Mobility Aware Blockchain Enabled Offloading and Scheduling in Vehicular Fog Cloud Computing

The development of vehicular Internet of Things (IoT) applications, such as E-Transport, Augmented Reality, and Virtual Reality are growing progressively. The mobility aware services and network-based security are fundamental requirements of these applications. However, multi-side offloading enabling blockchain and cost-efficient scheduling in heterogeneous vehicular fog cloud nodes network become a challenging task. The study formulates this problem as a convex optimization problem, where all constraints are the convex set. The goal of the study is to minimize communication cost and computation cost of applications under mobility, security, deadline, and resource constraints. Initially, we propose a novel vehicular fog cloud network (VFCN) which consists of different components and heterogeneous computing nodes. The ensure mobility privacy, the study devises Mobility Aware Blockchain-Enabled offloading scheme (MABOS). It extends blockchain enable multi-side offloading (e.g., offline offloading and online offloading) with proof of work (PoW), proof of creditability (PoC) and fault-tolerant techniques. The purpose is to offload all tasks under the secure network without any violation. Furthermore, to ensure Quality of Service (QoS) of applications, this work suggests linear search based task scheduling (LSBTS) method, which maps all tasks onto appropriate computing nodes. The experimental results show that devise schemes outperform all existing baseline approaches to the considered problem.
Hyperspectral inversion of soil heavy metals in Three-River Source Region based on random forest model

Hyperspectral remote sensing technology has considerable research value in monitoring and evaluating soil heavy metal pollution. In this study, the Three-River Source Region was taken as the study area. The occurrence relationship of six heavy metals in soil, such as Mn, Cu, Zn, Pb, Cr, Ni, with soil organic matter, clay minerals, and iron-manganese oxides, was studied through the determination and analysis of soil samples and the collection of soil reflectance spectrum. Spectral transformation was carried out by first derivative, second derivative, inverse-log, continuum removal and multiple scattering correction of the spectrum. The correlation between soil heavy metal content and soil spectrum was analyzed to select the characteristic band, and partial least squares (PLS) method, support vector machine (SVM) method and random forest (RF) model were used to build inversion model based on characteristic band. Then the best combination of spectral transformation and inversion model were explored. The results showed that Pb contents were the twice of the background in Qinghai province. The combination spectrum processing method can improve the correlation between spectrum and heavy metals. The location and quantity of characteristic bands of six heavy metals are different. The accuracy of RF was significantly better than that of SVM and PLS for all six heavy metal (i.e. pb: R2RF = 0.83, R2SVM = 0.62, R2PLS = 0.18), and the model effective of soil properties in non-polluted sites were reliable (i.e. clay: R2RF = 0.93, R2SVM = 0.87, R2PLS = 0.74). This study can provide technical support for the larger-scale monitoring of soil heavy metal content and heavy metal pollution assessment.
Amalgamation of blockchain and IoT for smart cities underlying 6G communication: A comprehensive review

Nowadays, increasing urbanization has necessitated the social, environmental, and economic development of cities to enhance the Quality of Life (QoL) significantly and introduces the “Smart City” concept. It integrates Information and Communication Tools (ICT), Internet of Things (IoT), and other technologies to resolve urban challenges. The key goal is to make the most acceptable use of available resources and technologies to develop smart cities. An IoT-enabled application plays a crucial role here, but it has various security, privacy, latency, and reliability issues with a single-point-of-failure problem. The evolving technology blockchain can handle the aforementioned security and privacy issues and provide high-quality services due to several features like transparency, trust-free, decentralization, immutability, and others. The 6G communication network takes care of latency and reliability issues in the smart city with their unique characteristics such as latency (10–100 μs) and reliability (99.99999%). Motivated by these facts, in this paper, we present a comprehensive review for blockchain technology and IoT together functional to smart cities. First, state-of-art-the works and contextual information are introduced. Then, we proposed a blockchain-based decentralized architecture for IoT-integrated smart cities covering different application perspectives, such as smart grid, Intelligent Transportation System (ITS), and healthcare 5.0 underlying 6G communication networks. Next, we describe the challenges of the proposed architecture respective to each application, as mentioned above. Finally, we collated the open research issues and future direction to efficiently integrate blockchain into IoT-envisioned smart cities.
On the Robustness of Vision Transformers to Adversarial Examples

Recent advances in attention-based networks have shown that Vision Transformers can achieve state-of-the-art or near state-of-the-art results on many image classification tasks. This puts transformers in the unique position of being a promising alternative to traditional convolutional neural networks (CNNs). While CNNs have been carefully studied with respect to adversarial attacks, the same cannot be said of Vision Transformers. In this paper, we study the robustness of Vision Transformers to adversarial examples. Our analyses of transformer security is divided into three parts. First, we test the transformer under standard white-box and black-box attacks. Second, we study the transfer-ability of adversarial examples between CNNs and trans-formers. We show that adversarial examples do not readily transfer between CNNs and transformers. Based on this finding, we analyze the security of a simple ensemble defense of CNNs and transformers. By creating a new attack, the self-attention blended gradient attack, we show that such an ensemble is not secure under a white-box adversary. However, under a black-box adversary, we show that an ensemble can achieve unprecedented robustness without sacrificing clean accuracy. Our analysis for this work is done using six types of white-box attacks and two types of black-box attacks. Our study encompasses multiple Vision Transformers, Big Transfer Models and CNN architectures trained on CIFAR-10, CIFAR-100 and ImageNet.
A Survey on the Integration of Blockchain With IoT to Enhance Performance and Eliminate Challenges

Internet of things IoT is playing a remarkable role in the advancement of many fields such as healthcare, smart grids, supply chain management, etc. It also eases people’s daily lives and enhances their interaction with each other as well as with their surroundings and the environment in a broader scope. IoT performs this role utilizing devices and sensors of different shapes and sizes ranging from small embedded sensors and wearable devices all the way to automated systems. However, IoT networks are growing in size, complexity, and number of connected devices. As a result, many challenges and problems arise such as security, authenticity, reliability, and scalability. Based on that and taking into account the anticipated evolution of the IoT, it is extremely vital not only to maintain but to increase confidence in and reliance on IoT systems by tackling the aforementioned issues. The emergence of blockchain opened the door to solve some challenges related to IoT networks. Blockchain characteristics such as security, transparency, reliability, and traceability make it the perfect candidate to improve IoT systems, solve their problems, and support their future expansion. This paper demonstrates the major challenges facing IoT systems and blockchain’s proposed role in solving them. It also evaluates the position of current researches in the field of merging blockchain with IoT networks and the latest implementation stages. Additionally, it discusses the issues related to the IoT-blockchain integration itself. Finally, this research proposes an architectural design to integrate IoT with blockchain in two layers using dew and cloudlet computing. Our aim is to benefit from blockchain features and services to guarantee a decentralized data storage and processing and address security and anonymity challenges and achieve transparency and efficient authentication service.
Comparison of students’ use and acceptance of emergency online learning due to COVID-19 in the USA, Mexico, Peru, and Turkey

Due to the COVID-19 pandemic, and pursuant to the governments' order of citizens remaining at home, several countries were required to transition from face-to-face instruction to an online model to provide higher education to their students. While factors affecting the use of online learning are diverse and have been studied by models of use and acceptance of technology, this cross-sectional study explores the factors unique to the current emergency situation that influence students' use and acceptance of emergency online learning. Moreover, it proposes a model to predict a student's cognitive engagement in Mexico, Peru, Turkey, and the USA. This is a quantitative study with an exploratory and descriptive scope and cross-sectional design. Data was collected from 1009 students from the four countries, who completed surveys anonymously. The factors analyzed were attitude, affect, and motivation, perceived behavioral control (ease of use, self-efficacy, and accessibility), and cognitive engagement. The data was analyzed using descriptive, correlation, and regression analysis. The predictive model shows that students' attitude toward online learning impacts their cognitive engagement in Mexico, Peru, and the USA. Furthermore, self-efficacy is a significant moderator for cognitive engagement in all four countries. The model also shows that each country has different determinants for cognitive engagement. Understanding the factors that affect the use of emergency online learning is essential for the success and/or achievement of its maximum benefits in situations like a global pandemic. Limitations of this study have been identified as use of convenience sampling, and an inability to explore factors related to instruction and system attributes. Professors who did not teach online learning lacked knowledge about online educational strategies and used the technological resources that were immediately available to them. Therefore, research that explores the use of instructional strategies and the use of technological systems during emergency online learning is necessary. This study includes suggestions to incorporate open educational resources that use microlearning and emphasizes the importance of student self-efficacy; because it was predictor of cognitive engagement in all four countries. Faculty and higher education institutions can and should develop strategies to increase students' sense of self-efficacy.
Triplet-Graph Reasoning Network for Few-Shot Metal Generic Surface Defect Segmentation

Metal surface defect segmentation can play an important role in dealing with the issue of quality control during the production and manufacturing stages. There are still two major challenges in industrial applications. One is the case that the number of metal surface defect samples is severely insufficient, and the other is that the most existing algorithms can only be used for specific surface defects and it is difficult to generalize to other metal surfaces. In this work, a theory of few-shot metal generic surface defect segmentation is introduced to solve these challenges. Simultaneously, the Triplet-Graph Reasoning Network (TGRNet) and a novel dataset Surface Defects- 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> are proposed to achieve this theory. In our TGRNet, the surface defect triplet (including triplet encoder and trip loss) is proposed and is used to segment background and defect area, respectively. Through triplet, the few-shot metal surface defect segmentation problem is transformed into few-shot semantic segmentation problem of defect area and background area. For few-shot semantic segmentation, we propose a method of multi-graph reasoning to explore the similarity relationship between different images. And to improve segmentation performance in the industrial scene, an adaptive auxiliary prediction module is proposed. For Surface Defects- 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> , it includes multiple categories of metal surface defect images to verify the generalization performance of our TGRNet and adds the nonmetal categories (leather and tile) as extensions. Through extensive comparative experiments and ablation experiments, it is proved that our architecture can achieve state-of-the-art results.