Reviewerly/exHarmony huggingface dataset:
Localized Quantum Chemistry on Quantum Computers

Quantum chemistry calculations of large, strongly correlated systems are typically limited by the computation cost that scales exponentially with the size of the system. Quantum algorithms, designed specifically for quantum computers, can alleviate this, but the resources required are still too large for today's quantum devices. Here, we present a quantum algorithm that combines a localization of multireference wave functions of chemical systems with quantum phase estimation (QPE) and variational unitary coupled cluster singles and doubles (UCCSD) to compute their ground-state energy. Our algorithm, termed "local active space unitary coupled cluster" (LAS-UCC), scales linearly with the system size for certain geometries, providing a polynomial reduction in the total number of gates compared with QPE, while providing accuracy above that of the variational quantum eigensolver using the UCCSD ansatz and also above that of the classical local active space self-consistent field. The accuracy of LAS-UCC is demonstrated by dissociating (H2)2 into two H2 molecules and by breaking the two double bonds in trans-butadiene, and resource estimates are provided for linear chains of up to 20 H2 molecules.
Breast Cancer Tumor Classification Using a Bag of Deep Multi-Resolution Convolutional Features

Breast cancer accounts for 30% of all female cancers. Accurately distinguishing dangerous malignant tumors from benign harmless ones is key to ensuring patients receive lifesaving treatments on time. However, as doctors currently do not identify 10% to 30% of breast cancers during regular assessment, automated methods to detect malignant tumors are desirable. Although several computerized methods for breast cancer classification have been proposed, convolutional neural networks (CNNs) have demonstrably outperformed other approaches. In this paper, we propose an automated method for the binary classification of breast cancer tumors as either malignant or benign that utilizes a bag of deep multi-resolution convolutional features (BoDMCF) extracted from histopathological images at four resolutions (40×, 100×, 200× and 400×) by three pre-trained state-of-the-art deep CNN models: ResNet-50, EfficientNetb0, and Inception-v3. The BoDMCF extracted by the pre-trained CNNs were pooled using global average pooling and classified using the support vector machine (SVM) classifier. While some prior work has utilized CNNs for breast cancer classification, they did not explore using CNNs to extract and pool a bag of deep multi-resolution features. Other prior work utilized CNNs for deep multi-resolution feature extraction from chest X-ray radiographs to detect other conditions such as pneumoconiosis but not for breast cancer detection from histopathological images. In rigorous evaluation experiments, our deep BoDMCF feature approach with global pooling achieved an average accuracy of 99.92%, sensitivity of 0.9987, specificity (or recall) of 0.9797, positive prediction value (PPV) or precision of 0.99870, F1-Score of 0.9987, MCC of 0.9980, Kappa of 0.8368, and AUC of 0.9990 on the publicly available BreaKHis breast cancer image dataset. The proposed approach outperforms the prior state of the art for histopathological breast cancer classification as well as a comprehensive set of CNN baselines, including ResNet18, InceptionV3, DenseNet201, EfficientNetb0, SqueezeNet, and ShuffleNet, when classifying images at any individual resolutions (40×, 100×, 200× or 400×) or when SVM is used to classify a BoDMCF extracted using any single pre-trained CNN model. We also demonstrate through a carefully constructed set of experiments that each component of our approach contributes non-trivially to its superior performance including transfer learning (pre-training and fine-tuning), deep feature extraction at multiple resolutions, global pooling of deep multiresolution features into a powerful BoDMCF representation, and classification using SVM.
Techno-economic analysis of photovoltaic rooftop system on car parking area in Rayong, Thailand

The intensity of solar radiation in Thailand is sufficient to produce electricity, thus the government has unconditionally promoted electricity production from solar energy, known as photovoltaic power system. Although it can reduce energy costs, it should also be environmentally friendly. This research aims to design and evaluate the photovoltaic rooftop system by concerning economic feasibility, cost of energy, and the compensation for carbon dioxide emissions. The research methodology also calculates the installation area and the solar power generation potential. Carbon dioxide emissions compensation and cost-effectiveness tools are used to evaluate the overall system. The results revealed that the photovoltaic rooftop system had the production capacity of 31.49 kilowatt-hours with an installation area of 184 m2. Solar panels orient to north direction with 11 degrees tilt from horizontal plane. The PV rooftop system generated the highest electricity around 137.103 kilowatt-hours per year and electricity production per installed system size is 4.354 kilowatt-hours per kilowatt-power. Within the period of 25 years and interest rate of 6.3145 percent, this project would be worthwhileness for the investment because the payback period is 6.83 years, the net present value is 1,523,571.70 baht, internal rate of return is 17.85 percent, benefit–cost​ ratio is 1.77, cost of energy is 2.31 baht per kilowatt-hour, in addition to the project can reduce carbon dioxide emissions by up to 643.77 tons of carbon dioxide equivalent. The promotion of electricity production from solar energy will increase the sustainability of energy and environment. In addition, the project sensitivity study indicated that initial cost, interest rate, and electricity price will be the important factors in making a decision to initiate a project.
Comparative Analysis of Credit Card Fraud Detection using Logistic regression with Random Forest towards an Increase in Accuracy of Prediction

The study aims to identify the frauds committed using a payment card such as credit cards, debit cards, and also an experiment is performed to find the best suitable algorithm among Random forest and Logistic Regression. Materials and Methods: To stop the fraud detections using Random forest (N=10) and Logistic regression (N=10) with supervised learning that gives insights from the previous data. Results: The precision of the random forest is 76.29% compared with Logistic regression with accuracy of 74.65% with statistical significance value p=0.03 (p<0.05) using Independent sample t test. Conclusion: This results proved that Random forest was significantly better for Fraud detection than Logistic regression within the study’s limits.
A multi-view-based noise correction algorithm for crowdsourcing learning

Crowdsourcing services provide a way to obtain large amounts of labeled data, which is inexpensive and effective. In crowdsourcing scenarios, the integrated labels of instances can be deduced by implementing ground truth inference algorithms. However, those labels often contain substantial noise and, to mitigate the effects of noise, label noise handling techniques are needed. This paper proposes a novel multi-view-based noise correction algorithm (MVNC). MVNC introduces the idea of multi-view learning to make better use of the information from crowdsourced data. It adds a new view composed of multiple noise labels and then trains classifiers on two views respectively to correct noise instances. In this process, different information between the views is fully utilized to generate the disagreement between two classifiers, so that the classifiers can complement each other and make more reliable predictions for noise instances. Experimental results on 38 benchmark data sets and 6 real-world data sets show that the new view significantly enhances the effect of noise correction.
Bitcoin Price Forecasting: An Integrated Approach Using Hybrid LSTM-ELM Models

In recent years, digital currencies have flourished on a considerable scale, and the markets of digital currencies have generated a nonnegligible impact on the whole financial system. Under this background, the accurate prediction of cryptocurrency prices could be a prerequisite for managing the risk of both cryptocurrency markets and financial systems. Considering the multiscale attributes of cryptocurrency price, we match the different machine learning algorithms to corresponding multiscale components and construct the ensemble prediction models based on machine learning and multiscale analysis. The Bitcoin price series, respectively, from 2017/11/24 to 2020/4/21 and 2020/4/22 to 2020/11/27, is selected as the training and prediction datasets. The empirical results show that the ensemble models can achieve a prediction accuracy of 95.12%, with better performance than the benchmark models, and the proposed models are robust in upward and downward market conditions. Meanwhile, the different algorithms are applicable for components with varying time scales.
EIFFeL

Federated learning (FL) enables clients to collaborate with a server to train a machine learning model. To ensure privacy, the server performs secure aggregation of updates from the clients. Unfortunately, this prevents verification of the well-formedness (integrity) of the updates as the updates are masked. Consequently, malformed updates designed to poison the model can be injected without detection. In this paper, we formalize the problem of ensuring both update privacy and integrity in FL and present a new system, EIFFeL, that enables secure aggregation of verified updates. EIFFeL is a general framework that can enforce arbitrary integrity checks and remove malformed updates from the aggregate, without violating privacy. Our empirical evaluation demonstrates the practicality of EIFFeL. For instance, with 100 clients and 10% poisoning, EIFFeL can train an MNIST classification model to the same accuracy as that of a non-poisoned federated learner in just 2.4s per iteration.
Group Property Inference Attacks Against Graph Neural Networks

Recent research has shown that machine learning (ML) models are vulnerable to privacy attacks that leak information about the training data. In this work, we consider Graph Neural Networks (GNNs) as the target model, and focus on a particular type of privacy attack named property inference attack (PIA) which infers the sensitive properties of the training graph through the access to GNNs. While the existing work has investigated PIAs against graph-level properties (e.g., node degree and graph density), we are the first to perform a systematic study of the group property inference attacks (GPIAs) that infer the distribution of particular groups of nodes and links (e.g., there are more links between male nodes than those between female nodes) in the training graph. First, we consider a taxonomy of threat models with various types of adversary knowledge, and design six different attacks for these settings. Second, we demonstrate the effectiveness of these attacks through extensive experiments on three representative GNN models and three real-world graphs. Third, we analyze the underlying factors that contribute to GPIA's success, and show that the GNN model trained on the graphs with or without the target property represents some dissimilarity in model parameters and/or model outputs, which enables the adversary to infer the existence of the property. Further, we design a set of defense mechanisms against the GPIA attacks, and demonstrate empirically that these mechanisms can reduce attack accuracy effectively with small loss on GNN model accuracy.
Thora

Most blockchain-based cryptocurrencies suffer from a heavily limited transaction throughput, which is a barrier to their growing adoption. Payment channel networks (PCNs) are one of the promising solutions to this problem. PCNs reduce the on-chain load of transactions and increase the throughput by processing many payments off-chain. In fact, any two users connected via a path of payment channels (i.e., joint addresses between the two channel end-points) can perform payments, and the underlying blockchain is used only when there is a dispute between users. Unfortunately, payments in PCNs can only be conducted securely along a path, which prevents the design of many interesting applications. Moreover, the most widely used implementation, the Lightning Network in Bitcoin, suffers from a collateral lock time linear in the path length, it is affected by security issues, and it relies on specific scripting features called Hash Timelock Contracts that hinders the applicability of the underlying protocol in other blockchains.
SSLGuard

Self-supervised learning is an emerging machine learning (ML) paradigm. Compared to supervised learning which leverages high-quality labeled datasets, self-supervised learning relies on unlabeled datasets to pre-train powerful encoders which can then be treated as feature extractors for various downstream tasks. The huge amount of data and computational resources consumption makes the encoders themselves become the valuable intellectual property of the model owner. Recent research has shown that the ML model's copyright is threatened by model stealing attacks, which aim to train a surrogate model to mimic the behavior of a given model. We empirically show that pre-trained encoders are highly vulnerable to model stealing attacks. However, most of the current efforts of copyright protection algorithms such as watermarking concentrate on classifiers. Meanwhile, the intrinsic challenges of pre-trained encoder's copyright protection remain largely unstudied. We fill the gap by proposing SSLGuard, the first watermarking scheme for pre-trained encoders. Given a clean pre-trained encoder, SSLGuard injects a watermark into it and outputs a watermarked version. The shadow training technique is also applied to preserve the watermark under potential model stealing attacks. Our extensive evaluation shows that SSLGuard is effective in watermark injection and verification, and it is robust against model stealing and other watermark removal attacks such as input noising, output perturbing, overwriting, model pruning, and fine-tuning.
Harnessing Perceptual Adversarial Patches for Crowd Counting

Crowd counting, which has been widely adopted for estimating the number of people in safety-critical scenes, is shown to be vulnerable to adversarial examples in the physical world (e.g., adversarial patches). Though harmful, adversarial examples are also valuable for evaluating and better understanding model robustness. However, existing adversarial example generation methods for crowd counting lack strong transferability among different black-box models, which limits their practicability for real-world systems. Motivated by the fact that attacking transferability is positively correlated to the model-invariant characteristics, this paper proposes the Perceptual Adversarial Patch (PAP) generation framework to tailor the adversarial perturbations for crowd counting scenes using the model-shared perceptual features. Specifically, we handcraft an adaptive crowd density weighting approach to capture the invariant scale perception features across various models and utilize the density guided attention to capture the model-shared position perception. Both of them are demonstrated to improve the attacking transferability of our adversarial patches. Extensive experiments show that our PAP could achieve state-of-the-art attacking performance in both the digital and physical world, and outperform previous proposals by large margins (at most +685.7 MAE and +699.5 MSE). Besides, we empirically demonstrate that adversarial training with our PAP can benefit the performance of vanilla models in alleviating several practical challenges in crowd counting scenarios, including generalization across datasets (up to -376.0 MAE and -354.9 MSE) and robustness towards complex backgrounds (up to -10.3 MAE and -16.4 MSE).
Evolutionary game-based incentive models for sustainable trust enhancement in a blockchained shared manufacturing network

Shared manufacturing (SM) is an advanced manufacturing mode to solve the problems of information asymmetry and resource imbalance in the complex supply and demand relationship under the background of mass personalization. Blockchain can provide technical solutions for trust issues between manufacturing service providers and demanders, but less research has focused on the stable maintenance of a blockchained shared manufacturing network (BSMN). Industrial knowledge graph and cognitive intelligence play an important role in designing incentive mechanisms for BSMN. Based on the evolutionary game theory, this paper designs various incentive models and develops the corresponding smart contracts to encourage different enterprises to participate in the “accounting” operations, to enhance the trust and maintain the operational stability of BSMN. Firstly, the evolution mechanism of BSMN was analyzed. Then, an evolutionary game model between shared manufacturing enterprise nodes was established to deduce the stable state of BSMN. Based on the analysis of the evolutionarily stable state, an incentive model with three strategies to encourage different enterprise nodes to participate in “accounting” operations was determined, and smart contracts of the incentive models were developed. Finally, the effectiveness of the proposed incentive models was verified by simulation experiments.
Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats

Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats-instructions, templates, and tools for consistently formatting data within a discipline-can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.
Recent Deep Learning Based NLP Techniques for Chatbot Development: An Exhaustive Survey

Chatbots are becoming popular as many applications need it. E-commerce, aviation, supply chain management companies etc. are using AI based chatbots to attract customers. Deep learning is another vertical impacting chatbots' behavior by bringing it close to human physiology. Ways of interaction directly enhance the business productivity that is the main reason for the rise of chatbots. Chatbots are evolving with the use of emerging techniques. In this paper we are making a detailed study of recent deep learning techniques for chatbots. It will bring understanding of developers closer towards good chatbot design. We will cover current gaps and solutions to catalyze current research.
Correlates of responding to and becoming victimized by fraud: Examining risk factors by scam type

Abstract Consumer fraud reports in North America have been increasing each year along with median fraud losses. Using survey data from 1375 American and Canadian consumers who previously reported a scam to a North American consumer complaint organization, this study examines the correlates of responding to and losing money to four categories of consumer fraud: opportunity‐based scams, threat‐based scams, consumer purchase scams, and phishing scams. Relative to opportunity‐based scams that offer the promise of rewards, consumers were less likely to respond to and report losing money when solicited by threat‐based scams and phishing scams. The odds of victimization were highest for consumer purchase scams. Risk factors, including gender, race, education, income, loneliness, financial fragility, and financial literacy, differed across scam categories, suggesting that victim profiles differ across fraud types. Some of the risk factors associated with responding to the scam solicitation (vs. ignoring it outright) were different from risk factors associated with victimization. Having advance knowledge of fraud prior to being exposed was protective across nearly all scam types. Results suggest that awareness about specific scams helps protect against financial loss. Additional research is needed on how to effectively deliver fraud awareness messages to those who are most susceptible.
Proposed Model for Secured Data Storage in Decentralized Cloud by Blockchain Ethereum

Since cloud computing is an essential component of any modern company (usually accounting for a considerable share of information technology (IT) infrastructure investment), consumers rely on cloud services. Data privacy and security are worries when data remains in third-party storage. Existing document version control systems are centralized and at risk from data loss, as seen by higher time utilization and incorrect document update procedures that allow modifications to a document without the awareness of other network operators. Underutilized peer resources might be leveraged to construct storage. According to this argument, an elevated level of data security may be obtained by encrypting the data and dispersing it among numerous nodes. In this study, we attempted to review the security of cloud systems when using the blockchain Ethereum, and cloud computing was briefly discussed with its advantages and disadvantages. The idea of a decentralized cloud was briefly demonstrated with blockchain technology. Furthermore, previous papers were reviewed and presented in tabular form. This dictated that there are still research gaps in the field of blockchain-based cloud computing systems. This study proposed a model for secured data storage over a decentralized cloud by blockchain Ethereum.
MUSE: Multi-faceted attention for signed network embedding

Signed network embedding is an approach to learning low-dimensional representations of nodes in signed networks with both positive and negative links, which facilitates downstream tasks such as link prediction with general data mining frameworks. Due to the distinct properties and significant added value of negative links, existing signed network embedding methods usually design dedicated methods based on social theories such as balance theory and status theory. However, existing signed network embedding methods ignore the characteristics of multiple facets of each node and mix them up in one single representation, which limits the ability to capture the fine-grained attentions between node pairs. In this paper, we propose MUSE, a MUlti-faceted attention-based Signed network Embedding framework to tackle this problem. Specifically, a joint intra- and inter-facet attention mechanism is introduced to aggregate fine-grained information from neighbor nodes. Moreover, balance theory is also utilized to guide information aggregation from multi-order balanced and unbalanced neighbors. Experimental results on four real-world signed network datasets demonstrate the effectiveness of our proposed framework.