Reviewerly/exHarmony huggingface dataset:
The Answer is in the Text: Multi-Stage Methods for Phishing Detection Based on Feature Engineering

A phishing attack is a threat based on fraudulent communication, usually by e-mail, where the cybercriminals, impersonating a trusted person or organization, try to lure and coax a target. Phishing detection approaches that obtain highly representational features from the text of these e-mails are a suitable strategy to counter these threats since these features can be used to train machine learning algorithms, thus generating models able to classify mail samples as phishing or legitimate messages. This paper proposes a multi-stage approach to detect phishing e-mail attacks using natural language processing and machine learning. The proposed multi-stage approach consists of feature engineering within natural language processing, lemmatization, feature selection, feature extraction, improved learning techniques for resampling and cross-validation, and the configuration of hyperparameters. We present two methods of the proposed approach, the first one exploiting the Chi-Square statistics and the Mutual Information to improve the dimensionality reduction, while the second method associates Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA). Both methods handle the problems of the “curse of dimensionality”, the sparsity, and the amount of information that must be obtained from the context in the Vector Space Model (VSM) representation. These methods yield reduced feature sets that, combined with the XGBoost and Random Forest machine learning algorithms, lead to an F1-measure of 100% success rate, for validation tests with the SpamAssassin Public Corpus and the Nazario Phishing Corpus datasets. Even considering just the text in e-mail bodies, the proposed multi-stage phishing detection approach outperforms state-of-the-art schemes for an accredited data set, requiring a much smaller number of features and presenting lower computational cost.
Zero–trade-off multiparameter quantum estimation via simultaneously saturating multiple Heisenberg uncertainty relations

Experiments demonstrate very high precisions achieved simultaneously for multiple parameters with noncommuting generators.
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification

Relation Classification (RC) plays an important role in natural language processing (NLP).Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in an open environment.To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations.Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning.However, it still suffers from the incompatible feature embedding problem when the novel relations come in.Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting.Extensive experiments are conducted to evaluate the effectiveness of our proposed model.
GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things

With the development of the Internet of Multimedia Things (IoMT), an increasing amount of image data is collected by various multimedia devices, such as smartphones, cameras, and drones. This massive number of images are widely used in each field of IoMT, which presents substantial challenges for privacy preservation. In this paper, we propose a new image privacy protection framework in an effort to protect the sensitive personal information contained in images collected by IoMT devices. We aim to use deep neural network techniques to identify the privacy-sensitive content in images, and then protect it with the synthetic content generated by generative adversarial networks (GANs) with differential privacy (DP). Our experiment results show that the proposed framework can effectively protect users’ privacy while maintaining image utility.
Time-dependent unbounded Hamiltonian simulation with vector norm scaling

The accuracy of quantum dynamics simulation is usually measured by the error of the unitary evolution operator in the operator norm, which in turn depends on certain norm of the Hamiltonian. For unbounded operators, after suitable discretization, the norm of the Hamiltonian can be very large, which significantly increases the simulation cost. However, the operator norm measures the worst-case error of the quantum simulation, while practical simulation concerns the error with respect to a given initial vector at hand. We demonstrate that under suitable assumptions of the Hamiltonian and the initial vector, if the error is measured in terms of the vector norm, the computational cost may not increase at all as the norm of the Hamiltonian increases using Trotter type methods. In this sense, our result outperforms all previous error bounds in the quantum simulation literature. Our result extends that of [Jahnke, Lubich, BIT Numer. Math. 2000] to the time-dependent setting. We also clarify the existence and the importance of commutator scalings of Trotter and generalized Trotter methods for time-dependent Hamiltonian simulations.
Batch Coherence-Driven Network for Part-Aware Person Re-Identification

Existing part-aware person re-identification methods typically employ two separate steps: namely, body part detection and part-level feature extraction. However, part detection introduces an additional computational cost and is inherently challenging for low-quality images. Accordingly, in this work, we propose a simple framework named Batch Coherence-Driven Network (BCD-Net) that bypasses body part detection during both the training and testing phases while still learning semantically aligned part features. Our key observation is that the statistics in a batch of images are stable, and therefore that batch-level constraints are robust. First, we introduce a batch coherence-guided channel attention (BCCA) module that highlights the relevant channels for each respective part from the output of a deep backbone model. We investigate channelpart correspondence using a batch of training images, then impose a novel batch-level supervision signal that helps BCCA to identify part-relevant channels. Second, the mean position of a body part is robust and consequently coherent between batches throughout the training process. Accordingly, we introduce a pair of regularization terms based on the semantic consistency between batches. The first term regularizes the high responses of BCD-Net for each part on one batch in order to constrain it within a predefined area, while the second encourages the aggregate of BCD-Nets responses for all parts covering the entire human body. The above constraints guide BCD-Net to learn diverse, complementary, and semantically aligned part-level features. Extensive experimental results demonstrate that BCDNet consistently achieves state-of-the-art performance on four large-scale ReID benchmarks.
Regularized Densely-Connected Pyramid Network for Salient Instance Segmentation

Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels. To this end, we propose a new pipeline for end-to-end salient instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instance. To better use the rich feature hierarchies in deep networks and enhance the side predictions, we propose the regularized dense connections, which attentively promote informative features and suppress non-informative ones from all feature pyramids. A novel multi-level RoIAlign based decoder is introduced to adaptively aggregate multi-level features for better mask predictions. Such strategies can be well-encapsulated into the Mask R-CNN pipeline. Extensive experiments on popular benchmarks demonstrate that our design significantly outperforms existing state-of-the-art competitors by 6.3% (58.6% vs. 52.3%) in terms of the AP metric. The code is available at https://github.com/yuhuan-wu/RDPNet.
Artificial Intelligence in Pathology: From Prototype to Product

<br>Modern image analysis techniques based on artificial intelligence (AI) have great potential to improve the quality and efficiency of diagnostic procedures in pathology and to detect novel biomarkers. Despite thousands of published research papers on applications of AI in pathology, hardly any research implementations have matured into commercial products for routine use. Bringing an AI solution for pathology to market poses significant technological, business, and regulatory challenges. In this paper, we provide a comprehensive overview and advice on how to meet these challenges. We outline how research prototypes can be turned into a product-ready state and integrated into the IT infrastructure of clinical laboratories. We also discuss business models for profitable AI solutions and reimbursement options for computer assistance in pathology. Moreover, we explain how to obtain regulatory approval so that AI solutions can be launched as <i>in vitro</i> diagnostic medical devices. Thus, this paper offers computer scientists, software companies, and pathologists a road map for transforming prototypes of AI solutions into commercial products.<br>
The orbitofrontal cartographer.

Theories of orbitofrontal cortex (OFC) function have evolved substantially over the last few decades. There is now a general consensus that the OFC is important for predicting aspects of future events and for using these predictions to guide behavior. Yet the precise content of these predictions and the degree to which OFC contributes to agency contingent upon them has become contentious, with several plausible theories advocating different answers to these questions. In this review we will focus on three of these ideas-the economic value, credit assignment, and cognitive map hypotheses-describing both their successes and failures. We will propose that these failures hint at a more nuanced and perhaps unique role for the OFC, particularly the lateral subdivision, in supporting the proposed functions when an underlying model or map of the causal structures in the environment must be constructed or updated. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
CSKG: The CommonSense Knowledge Graph

Sources of commonsense knowledge support applications in natural language understanding, computer vision, and knowledge graphs. Given their complementarity, their integration is desired. Yet, their different foci, modeling approaches, and sparse overlap make integration difficult. In this paper, we consolidate commonsense knowledge by following five principles, which we apply to combine seven key sources into a first integrated CommonSense Knowledge Graph (CSKG). We analyze CSKG and its various text and graph embeddings, showing that CSKG is well-connected and that its embeddings provide a useful entry point to the graph. We demonstrate how CSKG can provide evidence for generalizable downstream reasoning and for pre-training of language models. CSKG and all its embeddings are made publicly available to support further research on commonsense knowledge integration and reasoning.
Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems

To aid the development of future unmanned naval vessels, this manuscript investigates algorithm options for combining physical (noisy) sensors and computational models to provide additional information about system states, inputs, and parameters emphasizing deterministic options rather than stochastic ones. The computational model is formulated using Pontryagin’s treatment of Hamiltonian systems resulting in optimal and near-optimal results dependent upon the algorithm option chosen. Feedback is proposed to re-initialize the initial values of a reformulated two-point boundary value problem rather than using state feedback to form errors that are corrected by tuned estimators. Four algorithm options are proposed with two optional branches, and all of these are compared to three manifestations of classical estimation methods including linear-quadratic optimal. Over ten-thousand simulations were run to evaluate each proposed method’s vulnerability to variations in plant parameters amidst typically noisy state and rate sensors. The proposed methods achieved 69–72% improved state estimation, 29–33% improved rate improvement, while simultaneously achieving mathematically minimal costs of utilization in guidance, navigation, and control decision criteria. The next stage of research is indicated throughout the manuscript: investigation of the proposed methods’ efficacy amidst unknown wave disturbances.
Learning With Multiclass AUC: Theory and Algorithms

The Area under the ROC curve (AUC) is a well-known ranking metric for problems such as imbalanced learning and recommender systems. The vast majority of existing AUC-optimization-based machine learning methods only focus on binary-class cases, while leaving the multiclass cases unconsidered. In this paper, we start an early trial to consider the problem of learning multiclass scoring functions via optimizing multiclass AUC metrics. Our foundation is based on the M metric, which is a well-known multiclass extension of AUC. We first pay a revisit to this metric, showing that it could eliminate the imbalance issue from the minority class pairs. Motivated by this, we propose an empirical surrogate risk minimization framework to approximately optimize the M metric. Theoretically, we show that: (i) optimizing most of the popular differentiable surrogate losses suffices to reach the Bayes optimal scoring function asymptotically; (ii) the training framework enjoys an imbalance-aware generalization error bound, which pays more attention to the bottleneck samples of minority classes compared with the traditional O(√{1/N}) result. Practically, to deal with the low scalability of the computational operations, we propose acceleration methods for three popular surrogate loss functions, including the exponential loss, squared loss, and hinge loss, to speed up loss and gradient evaluations. Finally, experimental results on 11 real-world datasets demonstrate the effectiveness of our proposed framework. The code is now available at https://github.com/joshuaas/Learning-with-Multiclass-AUC-Theory-and-Algorithms.
A federated bidirectional connection broad learning scheme for secure data sharing in Internet of Vehicles

Data sharing in Internet of Vehicles (IoV) makes it possible to provide personalized services for users by service providers in Intelligent Transportation Systems (ITS). As IoV is a multi-user mobile scenario, the reliability and efficiency of data sharing need to be further enhanced. Federated learning allows the server to exchange parameters without obtaining private data from clients so that the privacy is protected. Broad learning system is a novel artificial intelligence technology that can improve training efficiency of data set. Thus, we propose a federated bidirectional connection broad learning scheme (FeBBLS) to solve the data sharing issues. Firstly, we adopt the bidirectional connection broad learning system (BiBLS) model to train data set in vehicular nodes. The server aggregates the collected parameters of BiBLS from vehicular nodes through the federated broad learning system (FedBLS) algorithm. Moreover, we propose a clustering FedBLS algorithm to offload the data sharing into clusters for improving the aggregation capability of the model. Some simulation results show our scheme can improve the efficiency and prediction accuracy of data sharing and protect the privacy of data sharing.
Unsupervised Hashing with Contrastive Information Bottleneck

Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much information of original data as possible. However, this requirement may force the models spending lots of their effort on reconstructing the unuseful background information, while ignoring to preserve the discriminative semantic information that is more important for the hashing task. To tackle this problem, inspired by the recent success of contrastive learning in learning continuous representations, we propose to adapt this framework to learn binary hashing codes. Specifically, we first propose to modify the objective function to meet the specific requirement of hashing and then introduce a probabilistic binary representation layer into the model to facilitate end-to-end training of the entire model. We further prove the strong connection between the proposed contrastive-learning-based hashing method and the mutual information, and show that the proposed model can be considered under the broader framework of the information bottleneck (IB). Under this perspective, a more general hashing model is naturally obtained. Extensive experimental results on three benchmark image datasets demonstrate that the proposed hashing method significantly outperforms existing baselines.
Global Cooling Through Blockchain to Avoid Catastrophic Climate Changes by 2050

Innumerable factors contributing to global warming is nothing of new, but increased human-induced greenhouse gas emissions resulting from the burning of vast amounts of fossil fuels have pushed the Earth’s natural systems (balanced carbon cycle) out of balance causing extreme climate changes in the new millennium. Since 1900, the global mean surface temperature has warmed up approximately +1.0 °C (1.8 °F) above the pre-industrial temperature, nearly half of which has occurred over the past three decades. If no actions are taken immediately in the next decade (by 2030), the global warming may accelerate to the critical +1.5 °C (2.8 °F) by 2050; at that time, extreme weather events may lead to irreversible disruptions to the economy, human health, and the ecosystems. Despite the fact that blockchain became a household name with the launch of Bitcoin in January 2009, its immense unique opportunities are well beyond Bitcoin and altcoins. Blockchain can contribute positively to the efforts of reducing human-induced greenhouse gas emissions therefore global warming by removing trusted third parties (distributed ledger technology), enabling purely peer-to-peer transactions, bringing network participants to transact and interact with each other on the same platform, creating crowd sourced funding, making governance transparent, increasing accessibility to data, making traceability easier, decentralizing the internet, automating various systems remotely located, increasing financial inclusion, finally making identity and private data management highly secure.
Understanding of blockchain-based identity management system adoption in the public sector

Purpose This study aims to understand the benefits and challenges associated with the adoption of a blockchain-based identity management system in public services by conducting an academic literature review, and to explore the design of such a system that can be applied to the Korean government. Design/methodology/approach This study explores the adoption of a blockchain-based identity management system using a literature review and an actual design case intended for use by the government sector. Findings Blockchain-based identity management systems can significantly improve transparency, accountability, and reliability in the user control of one's own data while reducing the time and cost needed to deliver public services, as well as increasing administrative efficiency. However, it is not always easy to implement such systems, and introducing new technologies in the government field requires a complicated, time-consuming process. There is currently an appetite for research extending beyond the typical technology-driven approach to elucidate the government adoption of new technologies and explore its implications. Practical implications The idea behind this system is that by storing and managing personal information on the blockchain and providing mobile apps to customers, users can log in or retrieve previously authenticated personal information without having to go through an authentication process. Since users do not need to go through the verification process every time, it is expected that they will be able to access only the necessary personal information more quickly and conveniently without having to deal with unnecessary details. In addition, the blockchain-based operation of a public service effectively increases the transparency and reliability of that service and reduces the social costs caused by personal information leakage. Originality/value This study introduces the design of a blockchain-based identity management system that can be used in public services, specifically in the Korean government sector for the first time. Along with a literature review, the implications that this study gleans from these real-world use cases can contribute to this field of research.
Quantum and Classical Data Transmission through Completely Depolarizing Channels in a Superposition of Cyclic Orders

Completely depolarizing channels are often regarded as the prototype of physical processes that are useless for communication: any message that passes through them along a well-defined trajectory is completely erased. When two such channels are used in a quantum superposition of two alternative orders, they become able to transmit some amount of classical information, but still no quantum information can pass through them. Here, we show that the ability to place N completely depolarizing channels in a superposition of N alternative causal orders enables a high-fidelity heralded transmission of quantum information with error vanishing as 1/N. This phenomenon highlights a fundamental difference with the N=2 case, where completely depolarizing channels are unable to transmit quantum data, even when placed in a superposition of causal orders. The ability to place quantum channels in a superposition of orders also leads to an increase of the classical communication capacity with N, which we rigorously prove by deriving an exact single-letter expression. Our results highlight the more complex patterns of correlations arising from multiple causal orders, which are similar to the more complex patterns of entanglement arising in multipartite quantum systems.