Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Cooperative Computation Offloading and Secure Handover in Vehicular Edge Computing Networks

Facing the requirements of intelligent vehicles for massive data processing, vehicular edge computing (VEC) utilizes computing resources deployed on the roadside infrastructure to provide proximity computing services for vehicles and forms a novel computing paradigm. Thus, vehicles can reduce the burden of local computing and improve computing efficiency by offloading tasks to roadside computing servers or neighboring resource-idle vehicles for execution via cooperative computation offloading (CO). However, dynamic communication channel states and data handover among multiple VEC servers caused by vehicle mobility pose challenges for CO decision-making and data security. This article applies blockchain to the cooperative CO of VEC and thus proposes a cooperative CO and secure handover framework with a consensus mechanism to guarantee the efficiency of cooperative CO and secure handover. In this framework, models for vehicle mobility and cooperative CO handover are constructed, and a consensus mechanism is proposed. This mechanism ensures the synchronization and immutability of offloaded data in the CO handover. A cooperative CO decision optimization is also formulated considering secure handover with blockchain technology to optimize the latency of vehicular computing tasks. To solve this complex problem, this optimization is transformed into a Markov decision process and a cooperative CO decision algorithm with multiagent deep reinforcement learning is designed, thus achieving the optimal solution. Extensive simulations verified the performance and effectiveness of the proposed method.
Subject-matter and intensional operators I: conditional-agnostic analytic implication

Although logical settings are typically concerned with tracking alethic considerations, frameworks exist in which topic-theoretic considerations-e.g., tracking subject-matter or topic-are given equal importance. Intuitions about extending topic through a propositional language are generally straightforward for extensional cases. For a number of reasons, arriving at a compelling account of the subject-matter of intensional operators-such as intensional conditionals-is a more difficult task. In particular, the framework of topic-sensitive intentional modals (TSIMs) championed by Francesco Berto and his collaborators leave the topics of intensional formulae undefined, which artificially constricts the expressivity of the theory. This paper proposes an approach to fill in this lacuna, emphasizing an analogous problem in Parry-style containment logics. In this setting, the approach receives a proof-of-concept through the introduction of a natural and general family of subsystems of Parry's PAI-with sound and complete axiomatizations-that allow a fine degree of control over the topics of intensional conditionals.
Cryptocurrency Adoption Behaviour of Millennial Investors in India

Young investors, fuelled by the hype and ads, are rushing to cryptocurrency exchanges with the hopes of generating rapid riches, and investors of the millennial generation especially believe cryptocurrency can make them billionaires. Crypto Research and Intelligence Business found that over 15 million Indians had accepted cryptocurrencies and invested close to $10 billion in crypto assets. Because of the ongoing changes over the last few years, changing from paper to virtual currency, a new type of currency, namely cryptocurrency, has emerged. Shorter transaction times and lower transfer fees are two of the advantages that cryptocurrencies offer to their potential users. The concept of cryptocurrency is underpinned by decentralised technology, which has captivated the interest of a variety of stakeholders. This research examines millennial cryptocurrency adoption behaviour in India.
TESR: Two-Stage Approach for Enhancement and Super-Resolution of Remote Sensing Images

Remote Sensing (RS) images are usually captured at resolutions lower than those required. Deep Learning (DL)-based super-resolution (SR) architectures are typically used to increase the resolution artificially. In this study, we designed a new architecture called TESR (Two-stage approach for Enhancement and super-resolution), leveraging the power of Vision Transformers (ViT) and the Diffusion Model (DM) to increase the resolution of RS images artificially. The first stage is the ViT-based model, which serves to increase resolution. The second stage is an iterative DM pre-trained on a larger dataset, which serves to increase image quality. Every stage is trained separately on the given task using a separate dataset. The self-attention mechanism of the ViT helps the first stage generate global and contextual details. The iterative Diffusion Model helps the second stage enhance the image’s quality and generate consistent and harmonic fine details. We found that TESR outperforms state-of-the-art architectures on super-resolution of remote sensing images on the UCMerced benchmark dataset. Considering the PSNR/SSIM metrics, TESR improves SR image quality as compared to state-of-the-art techniques from 34.03/0.9301 to 35.367/0.9449 in the scale ×2. On a scale of ×3, it improves from 29.92/0.8408 to 32.311/0.91143. On a scale of ×4, it improves from 27.77/0.7630 to 31.951/0.90456. We also found that the Charbonnier loss outperformed other loss functions in the training of both stages of TESR. The improvement was by a margin of 21.5%/14.3%, in the PSNR/SSIM, respectively. The source code of TESR is open to the community.
ICT adoption and youth employment in Nigeria's agricultural sector

Purpose This paper argues that through information and communication technology (ICT) adoption, the youth will be engaged in all nodes of the agricultural value chains, thereby improving the level of employment and reducing post-harvest losses. The study examines the determinants of ICT adoption among the youth. In addition, it estimates the impact of ICT adoption on youth employment in agriculture towards the actualisation of the Sustainable Development Goals (SDGs), particularly SDG-8, to promote inclusive and sustainable economic growth, productive employment and decent work for all. Design/methodology/approach The study engages data from Wave 4 (2018/2019) of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). The logit regression, the propensity score matching and the inverse probability weighted regression adjustment are used as the estimation techniques. Findings The study underscores that educational level, access to electricity, location, age and income are significant determinants of ICT adoption among the youth. The findings also show that the youth's average weekly engagement in agricultural activities is about 24 h. In addition, the result reveals that ICT adoption can increase youth agricultural employment by approximately 21%. The mean difference indicates that those with access to ICT participate in agricultural activities more than their counterparts without ICT access by 29.46%. Research limitations/implications One of the limitations of the study is that some of the variables such as insecurity, social protection/safety nets, that may have a significant influence on youth agricultural participation where not included in the model due to data constraint. As a recommendation for further studies, given data availability, such variables should be considered when examining youth-agricultural employment nexus. Practical implications Since ICT adoption has a significant impact on agricultural employment, this study proposes improved infrastructure facilities such as reliable power supply, lowering the cost of mobile and data subscriptions and better education facilities should be prioritised at all localities. This will enable the youth to embrace agriculture and help improve their socioeconomic welfare and livelihood. Originality/value Using Wave 4 of the LSMS-ISA, logit regression, propensity score matching and the inverse probability weighted regression adjustment, makes this study one of the very few to examine the impact of ICT adoption on agricultural employment among the youth in Nigeria. It implies that this study has provided empirical evidence and expanded the frontiers of knowledge on the extent to which ICT adoption influences youth agricultural employment in Nigeria.
Screen-shooting resistant image watermarking based on lightweight neural network in frequency domain

Currently, digital mobile devices, especially smartphones, can be used to acquire information conveniently through photograph taking. To protect information security in this case, we propose an efficient screen-shooting resistant watermarking scheme via deep neural network (DNN) in the frequency domain to achieve additional information embedding and source tracing. Specifically, we enhance the imperceptibility of watermarked images and the robustness against various attacks in real scene by computing the residual watermark message and encoding it with the original image using a lightweight neural network in the DCT domain. In addition, a noise layer is designed to simulate the photometric and radiometric effects of screen-shooting transfer. During the training process, the enhancing network is used to highlight the coding features of distorted images and improve the accuracy of extracted watermark message. Experimental results demonstrate that our scheme not only effectively ensures the balance between the imperceptibility of watermark embedding and the robustness of watermark extraction, but also significantly improves computational efficiency compared with some state-of-the-art schemes.
The world-class carbonate-hosted Fankou Zn-Pb deposit in China. Part I. Structural analysis: Evolutionary three-order thrusting structures control on the localization of Zn-Pb orebodies

The Fankou giant deposit (with exceeding 10 Mt Zn + Pb metals, grading at 15 wt%) is a carbonate-hosted, stratabound Zn-Pb deposit in the northern Quren Basin, South China. Lenticular, stratiform and wedge-shaped orebodies are strictly hosted in specific layers of Upper Devonian to Lower Carboniferous carbonate sequences along a series of NE and NNE trending faults. Previous ore geological and geochemical studies proposed that the Fankou can be analogous to a Mississippi Valley-type (MVT) deposit, but how the evolution of complex fault system controls ore-forming fluids migration, metals accumulation and orebodies localization remains unclear. To address this question, we conducted a deposit-scale structural analysis based on the geological mapping, underground works, and diamond drills logging. We identified three-order structures gradually growing in the Fankou deposit, including: 1) The first-order fault represented by F203 is a NW-striking thrust fault penetrating basement and acts as a major conduit for ultimate hydrothermal fluids migrations from depth; 2) The second-order faults include a spectrum of NNW-striking faults F2, F3, F4, F4, F5, F6, F7, F8 and F9. They are intersected with F203 in depth, and soon before and synchronous activities with Zn-Pb mineralization; 3) The third-order faults contain a series of NNE-striking F100, F101 and F102 with limited scale, which is subordinate to second-order faults and control the terminal localization of Zn-Pb orebodies. Collectively, we conclude that the evolutionary three groups of deposit-scale faults, derived from the regional NE-striking Wuchuan-Sihui Fault, jointly control the ore-forming fluids migrations from deep to shallow and from SE to NW in space. It is noteworthy that the second- to third-order faults act as the direct metal precipitation space when the upwards metalliferous fluids replaced with the favorable lithological layers. Therefore, we highlight that the southeast part of the mining area, where the metalliferous fluids came from, is a promising exploration target area in future.
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing

Training deep neural networks (DNNs) is time-consuming. While most existing solutions try to overlap/schedule computation and communication for efficient training, this paper goes one step further by skipping computing and communication through DNN layer freezing. Our key insight is that the training progress of internal DNN layers differs significantly, and front layers often become well-trained much earlier than deep layers. To explore this, we first introduce the notion of training plasticity to quantify the training progress of internal DNN layers. Then we design Egeria, a knowledge-guided DNN training system that employs semantic knowledge from a reference model to accurately evaluate individual layers' training plasticity and safely freeze the converged ones, saving their corresponding backward computation and communication. Our reference model is generated on the fly using quantization techniques and runs forward operations asynchronously on available CPUs to minimize the overhead. In addition, Egeria caches the intermediate outputs of the frozen layers with prefetching to further skip the forward computation. Our implementation and testbed experiments with popular vision and language models show that Egeria achieves 19%-43% training speedup w.r.t. the state-of-the-art without sacrificing accuracy.
Adversarial Contrastive Distillation with Adaptive Denoising

Adversarial Robustness Distillation (ARD) is a novel method to boost the robustness of small models. Unlike general adversarial training, its robust knowledge transfer can be less easily restricted by the model capacity. However, the teacher model that provides the robustness of knowledge does not always make correct predictions, interfering with the student’s robust performance. Besides, in the previous ARD methods, the robustness comes entirely from one-to-one imitation, ignoring the relationship between examples. To this end, we propose a novel structured ARD method called Contrastive Relationship DeNoise Distillation (CRDND). We design an adaptive compensation module to model the instability of the teacher. Moreover, we utilize the contrastive relationship to explore implicit robustness knowledge among multiple examples. Experimental results on multiple attack benchmarks show CRDND can transfer robust knowledge efficiently and achieves state-of-the-art performance.
Diablo: A Benchmark Suite for Blockchains

With the recent advent of blockchains, we have witnessed a plethora of blockchain proposals. These proposals range from using work to using time, storage or stake in order to select blocks to be appended to the chain. As a drawback it makes it difficult for the application developer to choose the right blockchain to support their applications. In particular, the scalability and performance one can obtain from a specific blockchain is typically unknown. The claimed results are often obtained in isolation by the developers of the blockchain themselves. The experimental conditions corresponding to these results are generally missing and the lack of details make these results irreproducible.
Improving PV power plant forecast accuracy: A hybrid deep learning approach compared across short, medium, and long-term horizons

This research paper investigates the use of Long Short-Term Memory (LSTM) and Grid Search Algorithm (GSA)-LSTM methods to forecast PV power output in different horizons. The study proposes precise hyperparameters for the LSTM network for improved performance prediction. Initially, LSTM was evaluated solely, and three different scenarios were investigated to obtain the best LSTM network hyperparameter settings for various horizons using a sensitivity study. Afterwards, LSTM was coupled with GSA to optimize LSTM network hyperparameters, which enhanced prediction accuracy and minimized error. The proposed methodology identified the most effective values for each hyperparameter based on different forecasting horizons. Additionally, Spearman Correlation Coefficient (SCC) and Pearson Correlation Coefficient (PCC) were deployed to determine the relationship between input data and target as well as identifying optimum lag value which has substantial effect on LSTM network. Optimal lag values of 150 and 280 were determined following the strong correlation between the data and target. LSTM and GSA-LSTM methods were compared with each other at various horizons and it was demonstrated that GSA-LSTM is superior by improving MSE at peak points by 10%, 30%, and 34% for 12 hours, 3 days, and 2 weeks horizons respectively. Comparing the proposed methodology with other studies in the literature revealed that the current study is capable of predicting PV power output for the 1 hour ahead horizon with significantly higher accuracy and with maximum improvement of about 28%.
Untargeted white-box adversarial attack to break into deep learning based COVID-19 monitoring face mask detection system

The face mask detection system has been a valuable tool to combat COVID-19 by preventing its rapid transmission. This article demonstrated that the present deep learning-based face mask detection systems are vulnerable to adversarial attacks. We proposed a framework for a robust face mask detection system that is resistant to adversarial attacks. We first developed a face mask detection system by fine-tuning the MobileNetv2 model and training it on the custom-built dataset. The model performed exceptionally well, achieving 95.83% of accuracy on test data. Then, the model's performance is assessed using adversarial images calculated by the fast gradient sign method (FGSM). The FGSM attack reduced the model's classification accuracy from 95.83% to 14.53%, indicating that the adversarial attack on the proposed model severely damaged its performance. Finally, we illustrated that the proposed robust framework enhanced the model's resistance to adversarial attacks. Although there was a notable drop in the accuracy of the robust model on unseen clean data from 95.83% to 92.79%, the model performed exceptionally well, improving the accuracy from 14.53% to 92% on adversarial data. We expect our research to heighten awareness of adversarial attacks on COVID-19 monitoring systems and inspire others to protect healthcare systems from similar attacks.
Security Concerns in Smart Agriculture and Blockchain-based Solution

Smart farming is generally defined as an IT-based intelligent farming system that finds, analyses, and manages field variability by carrying out crop production activities at the right location, time, and method for maximum profitability, sustainability, and land resource conservation. Precision farming can result in high profitability and production but heavily depends on the accuracy of provided data and decision-making. While IoT can be very handy in collecting and processing crop and environment data in a precise and timely manner, at the same time, Blockchain technology can be utilized to preserve the sense data and provide a platform for the immutability of records to accurately and precisely store the crop products data and environment. Integrating Blockchain and IoT technologies in precision farming can manage the requirements of precision farming in developing an intelligent application that will minimize resource consumption and help in high crop yield while maintaining quality standards. Although there are several benefits of adopting smart agriculture and IoT-based technologies, there are several security concerns related to these technologies. This article reviews the basics of smart agriculture, components, and security issues. Further, it proposes a blockchain-based model for the secure integration of smart agriculture components to provide security and reliability.
Negative Can Be Positive: Signed Graph Neural Networks for Recommendation

Most of the existing GNN-based recommender system models focus on learning users’ personalized preferences from these (explicit/implicit) positive feedback to achieve personalized recommendations. However, in the real-world recommender system, the users’ feedback behavior also includes negative feedback behavior (e.g., click dislike button), which also reflects users’ personalized preferences. How to utilize negative feedback is a challenging research problem. In this paper, we first qualitatively and quantitatively analyze the three kinds of negative feedback that widely existed in real-world recommender systems and investigate the role of negative feedback in recommender systems. We found that it is different from what we expected — not all negative items are ranked low, and some negative items are even ranked high in the overall items. Then, we propose a novel Signed Graph Neural Network Recommendation model (SiGRec) to encode the users’ negative feedback behavior. Our SiGRec can learn positive and negative embeddings of users and items via positive and negative graph neural network encoders, respectively. Besides, we also define a new Sign Cosine (SiC) loss function to adaptively mine the information of negative feedback for different types of negative feedback. Extensive experiments on four datasets demonstrate the proposed model outperforms several existing models. Specifically, on the Zhihu dataset, SiGRec outperforms the unsigned GNN model (i.e., LightGCN), 27.58% 29.81%, and 31.21% in P@20, R@20, and nDCG@20, respectively. We hope our work can open the door to further exploring the negative feedback in recommendations.
A novel pipelined end-to-end relation extraction framework with entity mentions and contextual semantic representation

The mainstream method of end-to-end relation extraction is to jointly extract entities and relations by sharing span representation, which, however, may cause feature conflict. The advent of advanced pre-trained models enhances the ability to learn span semantic representation and allows the breaking of the dominance of joint models. We argue the benefits of using separate encoders for entity recognition and relation classification and propose a novel pipelined end-to-end relation extraction framework. By adopting attention mechanisms, the framework has the ability to fuse contextual semantic representation, which is missed in other pipelined models. By introducing explicit entity mentions, the framework is able to capture entities' location information and type information, which are difficult to utilize in joint models. Several elaborate tricks are integrated into the training process of the framework to further improve its performance. Our experiments show that our method increases the state-of-the-art relation F1-score on CoNLL04, ADE and SciERC datasets to 75.6% (+1.2%), 85.0% (+1.2%), 43.9% (+2.3%), respectively, indicating that our pipelined approach is promising in end-to-end relation extraction.
One evolutionary algorithm deceives humans and ten convolutional neural networks trained on ImageNet at image recognition

Convolutional neural networks (CNNs) are widely used in computer vision, but can be deceived by carefully crafted adversarial images. In this paper, we propose an evolutionary algorithm (EA) based adversarial attack against CNNs trained on ImageNet. Our EA-based attack aims to generate adversarial images that not only achieve a high confidence probability of being classified into the target category (at least 75%), but also appear indistinguishable to the human eye in a black-box setting. These constraints are implemented to simulate a realistic adversarial attack scenario. Our attack has been thoroughly evaluated on 10 CNNs in various attack scenarios, including high-confidence targeted, good-enough targeted, and untargeted. Furthermore, we have compared our attack favorably against other well-known white-box and black-box attacks. The experimental results revealed that the proposed EA-based attack is superior or on par with its competitors in terms of the success rate and the visual quality of the adversarial images produced.