Reviewerly/exHarmony huggingface dataset:
Quantifying Blockchain Extractable Value: How dark is the forest?

Permissionless blockchains such as Bitcoin have excelled at financial services. Yet, opportunistic traders extract monetary value from the mesh of decentralized finance (DeFi) smart contracts through so-called blockchain extractable value (BEV). The recent emergence of centralized BEV relayer portrays BEV as a positive additional revenue source. Because BEV was quantitatively shown to deteriorate the blockchain’s consensus security, BEV relayers endanger the ledger security by incentivizing rational miners to fork the chain. For example, a rational miner with a 10% hashrate will fork Ethereum if a BEV opportunity exceeds 4x the block reward.However, related work is currently missing quantitative in-sights on past BEV extraction to assess the practical risks of BEV objectively. In this work, we allow to quantity the BEV danger by deriving the USD extracted from sandwich attacks, liquidations, and decentralized exchange arbitrage. We estimate that over 32 months, BEV yielded 540.54M USD in profit, divided among 11,289 addresses when capturing 49,691 cryptocurrencies and 60, 830 on-chain markets. The highest BEV instance we find amounts to 4.1MUSD, 616.6x the Ethereum block reward.Moreover, while the practitioner’s community has discussed the existence of generalized trading bots, we are, to our knowledge, the first to provide a concrete algorithm. Our algorithm can replace unconfirmed transactions without the need to understand the victim transactions’ underlying logic, which we estimate to have yielded a profit of 57,037.32 ETH (35.37MUSD) over 32 months of past blockchain data.Finally, we formalize and analyze emerging BEV relay systems, where miners accept BEV transactions from a centralized relay server instead of the peer-to-peer (P2P) network. We find that such relay systems aggravate the consensus layer attacks and therefore further endanger blockchain security.
BIoTHR: Electronic Health Record Servicing Scheme in IoT-Blockchain Ecosystem

The pervasiveness of newly introduced Internet-of-Things (IoT) devices has opened up new opportunities in healthcare systems, for example in facilitating remote patient monitoring. There are, however, security and privacy considerations in the transmission of data from these devices to the backend server, and across heterogeneous IoT networks. In this study, we propose a novel privacy-preserving scheme which is based on blockchain and swarm exchange techniques to facilitate seamless and secure transmission of user data (e.g., electronic health record (EHR)-related information) through secured swarm nodes of peer-to-peer communications. BIoTHR refers to the proposed scheme on the private blockchain-assisted EHR management using IoT. Specifically, new blockchain and swarm exchange infrastructures are suggested as a backbone of the proposed scheme to ensure secure and reliable data transmission and timely monitoring of data sent across IoT networks. An autonomous encryption-decryption mechanism is also utilized, along with a dynamic and modular server assistance technology to deploy EHR transmission in a secure manner. Moreover, several swarm-listen, announcement, peer open and peer closing algorithms are incorporated to employ the actual power of pervasive EHR transmission for better e-healthcare service provisioning. The proposed scheme is developed using the open-source tools of GnuPG, IPFS, and Golang. Proposed study simulates a number of heterogeneous IoT-based health sensor nodes, namely, body temperature, pulse rate, and oxygen saturation, i.e., SPO2, galvanic skin response, and blood glucose in blockchain-assisted swarm exchange framework. The results reveal that the proposed scheme, in terms of blockchain-IoT, swarm exchange and EHR transmission, outperforms several peer techniques.
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions, untrustworthy servers, and external attacks. To address these issues, we propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL). In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, aggregates its own model with received ones, and then competes to generate a block before its local training on the next round. We evaluate the learning performance of BLADE-FL, and develop an upper bound on the global loss function. Then we verify that this bound is convex with respect to the number of overall aggregation rounds <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> , and optimize the computing resource allocation for minimizing the upper bound. We also note that there is a critical problem of training deficiency, caused by lazy clients who plagiarize others’ trained models and add artificial noises to disguise their cheating behaviors. Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> , the learning parameters, and the proportion of lazy clients. Based on the MNIST and Fashion-MNIST datasets, we see that the experimental results are consistent with the analytical ones. To be specific, the gap between the developed upper bound and experimental results is lower than <inline-formula><tex-math notation="LaTeX">$5\%$</tex-math></inline-formula> , and the optimized <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> based on the upper bound can effectively minimize the loss function.
On the Value of Virtual Currencies

Abstract Our economic framework suggests that the exchange rate of virtual currency is determined by three components. First, the current value of transactions in virtual currency which absorb part of the exchange rate risk. Second, the decisions and expectations of forward‐looking investors to buy virtual currency (thereby effectively regulating its supply). Third, the elements that jointly drive future consumer adoption and merchant acceptance of virtual currency. The model predicts that, as virtual currency becomes more established, the exchange rate will become less sensitive to the impact of shocks to speculators' beliefs. This undermines the notion that excessive exchange rate volatility will prohibit widespread use of virtual currency.
Governance and social media in African countries: An empirical investigation

This study assesses linkages between social media and governance dynamics in 49 African countries for the year 2012. The empirical evidence is based on ordinary least squares and quantile regressions. Ten bundled and unbundled governance dynamics are used, notably: (i) political governance (entailing “voice & accountability” and political stability/no violence); (ii) economic governance (involving regulation quality and government effectiveness); (iii) institutional governance (comprising the rule of law and corruption-control) and (iv) general governance (entailing political, economic and institutional governance). Social media is measured with Facebook penetration. The findings show that Facebook penetration is positively associated with governance dynamics and these positive nexuses differ in terms of significance and magnitude of significance throughout the conditional distribution of the governance dynamics.
Machine learning prediction of critical transition and system collapse

To predict a critical transition due to parameter drift without relying on a model is an outstanding problem in nonlinear dynamics and applied fields. A closely related problem is to predict whether the system is already in or if the system will be in a transient state preceding its collapse. We develop a model-free, machine-learning-based solution to both problems by exploiting reservoir computing to incorporate a parameter input channel. We demonstrate that, when the machine is trained in the normal functioning regime with a chaotic attractor (i.e., before the critical transition), the transition point can be predicted accurately. Remarkably, for a parameter drift through the critical point, the machine with the input parameter channel is able to predict not only that the system will be in a transient state, but also the distribution of the transient lifetimes and their average before the final collapse, revealing an important physical property of transient chaos.7 MoreReceived 28 May 2020Accepted 14 January 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.013090Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasBifurcationsChaosPhysical SystemsChaotic systemsTechniquesMachine learningNonlinear Dynamics
Supply Chain Disruption Risk Management with Blockchain: A Dynamic Literature Review

The purpose of this review is to describe the landscape of scientific literature enriched by an author’s keyword analysis to develop and test blockchain’s capabilities for enhancing supply chain resilience in times of increased risk and uncertainty. This review adopts a dynamic quantitative bibliometric method called systematic literature network analysis (SLNA) to extract and analyze the papers. The procedure consists of two methods: a systematic literature review (SLR) and bibliometric network analysis (BNA). This paper provides an important contribution to the literature in applying blockchain as a key component of cyber supply chain risk management (CSRM), manage and predict disruption risks that lead to resilience and robustness of the supply chain. This systematic review also sheds light on different research areas such as the potential of blockchain for privacy and security challenges, security of smart contracts, monitoring counterfeiting, and traceability database systems to ensure food safety and security.
Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models

Abstract Background Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the “gold standard” for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists’ diagnostic and technical skills. It is difficult to get highly skilled microscopists in remote areas of developing countries. To alleviate this problem, in this paper, we propose to investigate state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from microscopic image of thick blood slides. Results YOLOV3 and YOLOV4 models, which are state-of-the-art object detectors in accuracy and speed, are not optimized for detecting small objects such as malaria parasites in microscopic images. We modify these models by increasing feature scale and adding more detection layers to enhance their capability of detecting small objects without notably decreasing detection speed. We propose one modified YOLOV4 model, called YOLOV4-MOD and two modified models of YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. Besides, new anchor box sizes are generated using K-means clustering algorithm to exploit the potential of these models in small object detection. The performance of the modified YOLOV3 and YOLOV4 models were evaluated on a publicly available malaria dataset. These models have achieved state-of-the-art accuracy by exceeding performance of their original versions, Faster R-CNN, and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. YOLOV4-MOD has achieved the best detection accuracy among all the other models with a mAP of 96.32%. YOLOV3-MOD2 and YOLOV3-MOD1 have achieved mAP of 96.14% and 95.46%, respectively. Conclusions The experimental results of this study demonstrate that performance of modified YOLOV3 and YOLOV4 models are highly promising for detecting malaria parasites from images captured by a smartphone camera over the microscope eyepiece. The proposed system is suitable for deployment in low-resource setting areas.
Autonomous UAV Exploration of Dynamic Environments Via Incremental Sampling and Probabilistic Roadmap

Autonomous exploration requires robots to generate informative trajectories iteratively. Although sampling-based methods are highly efficient in unmanned aerial vehicle exploration, many of these methods do not effectively utilize the sampled information from the previous planning iterations, leading to redundant computation and longer exploration time. Also, few have explicitly shown their exploration ability in dynamic environments even though they can run real-time. To overcome these limitations, we propose a novel dynamic exploration planner (DEP) for exploring unknown environments using incremental sampling and Probabilistic Roadmap (PRM). In our sampling strategy, nodes are added incrementally and distributed evenly in the explored region, yielding the best viewpoints. To further shortening exploration time and ensuring safety, our planner optimizes paths locally and refine them based on the Euclidean Signed Distance Function (ESDF) map. Meanwhile, as the multi-query planner, PRM allows the proposed planner to quickly search alternative paths to avoid dynamic obstacles for safe exploration. Simulation experiments show that our method safely explores dynamic environments and outperforms the benchmark planners in terms of exploration time, path length, and computational time.
Multi-modal advanced deep learning architectures for breast cancer survival prediction

Breast cancer is the most frequently occurring cancer and has compelling contributions to increasing mortality rates among women. The manual prognosis and diagnosis of this disease take long hours, even for a medical professional. A model with better predictive power can benefit cancer patients from going through the toxic side effects and extra medical expenses related to unnecessary treatment. Medical professionals can be benefited from early-stage detection and selection of the appropriate cancer treatment plan. The availability of multi-modal cancer data, i.e., genomic details, histopathology images, and clinical details, supports the researchers in proceeding with the development of multi-modal based advanced deep-learning models. This research proposes gated attentive deep learning models stacked with random forest classifiers, which use multi-modal data and produce informative features to enhance the breast cancer prognosis prediction. It is designed as a bi-phase model; phase one uses a sigmoid gated attention convolutional neural network to generate the stacked features, while phase two passes the stacked features to the random forest classifier. The comparative study of the proposed and other existing methods over METABRIC (1980 patients) and TCGA-BRCA (1080 patients) datasets illustrate significant enhancements, 5.1% in sensitivity values, in the survival estimation of breast cancer patients.
The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation

Next basket recommendation aims to infer a set of items that a user will purchase at the next visit by considering a sequence of baskets he/she has purchased previously. This task has drawn increasing attention from both the academic and industrial communities. The existing solutions mainly focus on sequential modeling over their historical interactions. However, due to the diversity and randomness of users' behaviors, not all these baskets are relevant to help identify the user's next move. It is necessary to denoise the baskets and extract credibly relevant items to enhance recommendation performance. Unfortunately, this dimension is usually overlooked in the current literature.
Counterfactual Data-Augmented Sequential Recommendation

Sequential recommendation aims at predicting users' preferences based on their historical behaviors. However, this recommendation strategy may not perform well in practice due to the sparsity of the real-world data. In this paper, we propose a novel counterfactual data augmentation framework to mitigate the impact of the imperfect training data and empower sequential recommendation models. Our framework is composed of a sampler model and an anchor model. The sampler model aims to generate new user behavior sequences based on the observed ones, while the anchor model is leveraged to provide the final recommendation list, which is trained based on both observed and generated sequences. We design the sampler model to answer the key counterfactual question: "what would a user like to buy if her previously purchased items had been different?". Beyond heuristic intervention methods, we leverage two learning-based methods to implement the sampler model, and thus, improve the quality of the generated sequences when training the anchor model. Additionally, we analyze the influence of the generated sequences on the anchor model in theory and achieve a trade-off between the information and the noise introduced by the generated sequences. Experiments on nine real-world datasets demonstrate our framework's effectiveness and generality.
Dynamic Modality Interaction Modeling for Image-Text Retrieval

Image-text retrieval is a fundamental and crucial branch in information retrieval. Although much progress has been made in bridging vision and language, it remains challenging because of the difficult intra-modal reasoning and cross-modal alignment. Existing modality interaction methods have achieved impressive results on public datasets. However, they heavily rely on expert experience and empirical feedback towards the design of interaction patterns, therefore, lacking flexibility. To address these issues, we develop a novel modality interaction modeling network based upon the routing mechanism, which is the first unified and dynamic multimodal interaction framework towards image-text retrieval. In particular, we first design four types of cells as basic units to explore different levels of modality interactions, and then connect them in a dense strategy to construct a routing space. To endow the model with the capability of path decision, we integrate a dynamic router in each cell for pattern exploration. As the routers are conditioned on inputs, our model can dynamically learn different activated paths for different data. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MS-COCO, verify the superiority of our model compared with several state-of-the-art baselines.
Efficient On-Chip Training of Optical Neural Networks Using Genetic Algorithm

Recent advances in silicon photonic chips have made huge progress in optical computing owing to their flexibility in the reconfiguration of various tasks. Its deployment of neural networks serves as an alternative for mitigating the rapidly increased demand for computing resources in electronic platforms. However, it remains a formidable challenge to train the online programmable optical neural networks efficiently, being restricted by the difficulty in obtaining gradient information on a physical device when executing a gradient descent algorithm. Here, we experimentally demonstrate an efficient, physics-agnostic, and closed-loop protocol for training optical neural networks on chip. A gradient-free algorithm, that is, the genetic algorithm, is adopted. The protocol is on-chip implementable, physical agnostic (no need to rely on characterization and offline modeling), and gradient-free. The protocol works for various types of chip structures and is especially helpful to those that cannot be analytically decomposed and characterized. We confirm its viability using several practical tasks, including the crossbar switch and the Iris classification. Finally, by comparing our physics-agonistic and gradient-free method to the off-chip and gradient-based training methods, we demonstrate the robustness of our system to perturbations such as imperfect phase implementation and photodetection noise. Optical processors with gradient-free genetic algorithms have broad application potentials in pattern recognition, reinforcement learning, quantum computing, and realistic applications (such as facial recognition, natural language processing, and autonomous vehicles).
Teaching and Learning in Times of COVID-19: Uses of Digital Technologies During School Lockdowns

The closure of schools as a result of COVID-19 has been a critical global incident from which to rethink how education works in all our countries. Among the many changes generated by this crisis, all teaching became mediated by digital technologies. This paper intends to analyze the activities carried out during this time through digital technologies and the conceptions of teaching and learning that they reflect. We designed a Likert-type online questionnaire to measure the frequency of teaching activities. It was answered by 1,403 teachers from Spain (734 primary and 669 secondary education teachers). The proposed activities varied depending on the learning promoted (reproductive or constructive), the learning outcomes (verbal, procedural, or attitudinal), the type of assessment to which the activities were directed, and the presence of cooperative activities. The major result of this study was that teachers used reproductive activities more frequently than constructive ones. We also found that most activities were those favoring verbal and attitudinal learning. The cooperative activities were the least frequent. Finally, through a cluster analysis, we identified four teaching profiles depending on the frequency and type of digital technologies use: Passive, Active, Reproductive, and Interpretative. The variable that produced the most consistent differences was previous digital technologies use These results show that Information and Communication Technologies (ICT) uses are reproductive rather than constructive, which impedes effective digital technologies integration into the curriculum so that students gain 21st-century competencies.
Cryptocurrency liquidity and volatility interrelationships during the COVID-19 pandemic

We examine the interactions between cryptocurrency price volatility and liquidity during the outbreak of the COVID-19 pandemic. Evidence suggests that these developing digital products have played a new role as a potential safe-haven during periods of substantial financial market panic. Results suggest that cryptocurrency market liquidity increased significantly after the WHO identification of a worldwide pandemic. Significant and substantial interactions between cryptocurrency price and liquidity effects are identified. These results add further support to the argument that substantial flows of investment entered cryptocurrency markets in search of an investment safe-haven during this exceptional black-swan event.