Reviewerly/exHarmony huggingface dataset:
Fast Graph Convolution Network Based Multi-label Image Recognition via Cross-modal Fusion

In multi-label image recognition, it has become a popular method to predict those labels that co-occur in an image via modeling the label dependencies. Previous works focus on capturing the correlation between labels, but neglect to effectively fuse the image features and label embeddings, which severely affects the convergence efficiency of the model and inhibits the further precision improvement of multi-label image recognition. To overcome this shortcoming, in this paper, we introduce Multi-modal Factorized Bilinear pooling (MFB) which works as an efficient component to fuse cross-modal embeddings and propose F-GCN, a fast graph convolution network (GCN) based multi-label image recognition model. F-GCN consists of three key modules: (1) an image representation learning module which adopts a convolution neural network (CNN) to learn and generate image representations, (2) a label co-occurrence embedding module which first obtains the label vectors via the word embeddings technique and then adopts GCN to capture label co-occurrence embeddings and (3) an MFB fusion module which efficiently fuses these cross-modal vectors to enable an end-to-end model with a multi-label loss function. We conduct extensive experiments on two multi-label datasets including MS-COCO and VOC2007. Experimental results demonstrate the MFB component efficiently fuses image representations and label co-occurrence embeddings and thus greatly improves the convergence efficiency of the model. In addition, the performance of image recognition has also been promoted compared with the state-of-the-art methods.
How Experts Detect Phishing Scam Emails

Phishing scam emails are emails that pretend to be something they are not in order to get the recipient of the email to undertake some action they normally would not. While technical protections against phishing reduce the number of phishing emails received, they are not perfect and phishing remains one of the largest sources of security risk in technology and communication systems. To better understand the cognitive process that end users can use to identify phishing messages, I interviewed 21 IT experts about instances where they successfully identified emails as phishing in their own inboxes. IT experts naturally follow a three-stage process for identifying phishing emails. In the first stage, the email recipient tries to make sense of the email, and understand how it relates to other things in their life. As they do this, they notice discrepancies: little things that are "off'' about the email. As the recipient notices more discrepancies, they feel a need for an alternative explanation for the email. At some point, some feature of the email --- usually, the presence of a link requesting an action --- triggers them to recognize that phishing is a possible alternative explanation. At this point, they become suspicious (stage two) and investigate the email by looking for technical details that can conclusively identify the email as phishing. Once they find such information, then they move to stage three and deal with the email by deleting it or reporting it. I discuss ways this process can fail, and implications for improving training of end users about phishing.
On the Origin of Scanning

Fast IPv4 scanning has enabled researchers to answer a wealth of security and networking questions. Yet, despite widespread use, there has been little validation of the methodology's accuracy, including whether a single scan provides sufficient coverage. In this paper, we analyze how scan origin affects the results of Internet-wide scans by completing three HTTP, HTTPS, and SSH scans from seven geographically and topologically diverse networks. We find that individual origins miss an average 1.6-8.4% of HTTP, 1.5-4.6% of HTTPS, and 8.3-18.2% of SSH hosts. We analyze why origins see different hosts, and show how permanent and temporary blocking, packet loss, geographic biases, and transient outages affect scan results. We discuss the implications for scanning and provide recommendations for future studies.
Deep learning-based medical image segmentation with limited labels

Deep learning (DL)-based auto-segmentation has the potential for accurate organ delineation in radiotherapy applications but requires large amounts of clean labeled data to train a robust model. However, annotating medical images is extremely time-consuming and requires clinical expertise, especially for segmentation that demands voxel-wise labels. On the other hand, medical images without annotations are abundant and highly accessible. To alleviate the influence of the limited number of clean labels, we propose a weakly supervised DL training approach using deformable image registration (DIR)-based annotations, leveraging the abundance of unlabeled data. We generate pseudo-contours by utilizing DIR to propagate atlas contours onto abundant unlabeled images and train a robust DL-based segmentation model. With 10 labeled TCIA dataset and 50 unlabeled CT scans from our institution, our model achieved Dice similarity coefficient of 87.9%, 73.4%, 73.4%, 63.2% and 61.0% on mandible, left & right parotid glands and left & right submandibular glands of TCIA test set and competitive performance on our institutional clinical dataset and a third party (PDDCA) dataset. Experimental results demonstrated the proposed method outperformed traditional multi-atlas DIR methods and fully supervised limited data training and is promising for DL-based medical image segmentation application with limited annotated data.
Label-Aware Graph Convolutional Networks

Recent advances in Graph Convolutional Networks (GCNs) have led to state-of-the-art performance on various graph-related tasks. However, most existing GCN models do not explicitly identify whether all the aggregated neighbors are valuable to the learning tasks, which may harm the learning performance. In this paper, we consider the problem of node classification and propose the Label-Aware Graph Convolutional Network (LAGCN) framework which can directly identify valuable neighbors to enhance the performance of existing GCN models. Our contribution is three-fold. First, we propose a label-aware edge classifier that can filter distracting neighbors and add valuable neighbors for each node to refine the original graph into a label-aware (LA) graph. Existing GCN models can directly learn from the LA graph to improve the performance without changing their model architectures. Second, we introduce the concept of positive ratio to evaluate the density of valuable neighbors in the LA graph. Theoretical analysis reveals that using the edge classifier to increase the positive ratio can improve the learning performance of existing GCN models. Third, we conduct extensive node classification experiments on benchmark datasets. The results verify that LAGCN can improve the performance of existing GCN models considerably, in terms of node classification.
Color image encryption via Hénon-zigzag map and chaotic restricted Boltzmann machine over Blockchain

A color image encryption algorithm using the Hénon-zigzag map and chaotic restricted Boltzmann machine (CRBM) is proposed in this paper. The proposed pseudo-random number generator, chaotic restricted Boltzmann machine (CRBM), can simultaneously generate three pseudo-random number sequences. The algorithm includes the permutation phase and the diffusion phase. In the Hénon-zigzag map-based permutation phase, zigzag map is used to modulate two pseudo-random number sequences generated by Hénon map to obtain two new pseudo-random number sequences. The mixing of these two chaotic maps makes the security of the permutation phase significantly improve. Subsequently the two pseudo-random number sequences are used for row permutation and column permutation, respectively. In the diffusion phase, through multiple iterations of CRBM of the 3 × 3 architecture, three pseudo-random number sequences are generated by the state values of three neurons in the visible layer. Then these three pseudo-random number sequences are used for bitxor operation with the R, G and B components of the scrambled image, respectively. A series of numerical experiments and analyses on encrypted images prove that the proposed algorithm is more secure than state-of-the-art algorithms. Furthermore, based on the combined use of blockchain and the proposed algorithm, a novel image encryption/decryption system is proposed. The system has two features: asymmetric encryption/decryption of images and authoritative verification of the integrity of encrypted images. It may provide a better understanding of blockchain in digital image encryption.
Quantum Reverse Hypercontractivity: Its Tensorization and Application to Strong Converses

In this paper we develop the theory of quantum reverse hypercontractivity inequalities and show how they can be derived from log-Sobolev inequalities. Next we prove a generalization of the Stroock–Varopoulos inequality in the non-commutative setting which allows us to derive quantum hypercontractivity and reverse hypercontractivity inequalities solely from 2-log-Sobolev and 1-log-Sobolev inequalities respectively. We then prove some tensorization-type results providing us with tools to prove hypercontractivity and reverse hypercontractivity not only for certain quantum superoperators but also for their tensor powers. Finally as an application of these results, we generalize a recent technique for proving strong converse bounds in information theory via reverse hypercontractivity inequalities to the quantum setting. We prove strong converse bounds for the problems of quantum hypothesis testing and classical-quantum channel coding based on the quantum reverse hypercontractivity inequalities that we derive.
Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model

Social media is a vital means for information-sharing due to its easy access, low cost, and fast dissemination characteristics. However, increases in social media usage have corresponded with a rise in the prevalence of cyberbullying. Most existing cyberbullying detection methods aresupervised and, thus, have two key drawbacks: (1) The data labeling process is often time-consuming and labor-intensive; (2) Current labeling guidelines may not be generalized to future instances because of different language usage and evolving social networks. To address these limitations, this work introduces a principled approach forunsupervised cyberbullying detection. The proposed model consists of two main components: (1) Arepresentation learning network that encodes the social media session by exploiting multi-modal features, e.g., text, network, and time. (2) Amulti-task learning network that simultaneously fits the comment inter-arrival times and estimates the bullying likelihood based on a Gaussian Mixture Model. The proposed model jointly optimizes the parameters of both components to overcome the shortcomings of decoupled training. Our core contribution is an unsupervised cyberbullying detection model that not only experimentally outperforms the state-of-the-art unsupervised models, but also achieves competitive performance compared to supervised models.
Programmable Filament

From full-color objects to functional capacitive artifacts, 3D printing multi-materials became essential to broaden the application areas of digital fabrication. We present Programmable Filament, a novel technique that enables multi-material printing using a commodity FDM 3D printer, requiring no hardware upgrades. Our technique builds upon an existing printing technique in which multiple filament segments are printed and spliced into a single threaded filament. We propose an end-to-end pipeline for 3D printing an object in multi-materials, with an introduction of the design systems for end-users. Optimized for low-cost, single-nozzle FDM 3D printers, the system is built upon our computational analysis and experiments to enhance its validity over various printers and materials to design and produce a programmable filament. Finally, we discuss application examples and speculate the future with its potential, such as custom filament manufacturing on-demand.
FedCrowd: A Federated and Privacy-Preserving Crowdsourcing Platform on Blockchain

Crowdsourcing has attracted widespread attention in recent years and developed into various applications. An indispensable service of crowdsourcing systems is task recommendation, which means tasks should be accurately recommended to the workers with aligned interests. However, existing systems rely on their separate servers to conduct recommendation services, resulting in computing resources locked inside each isolated system. Moreover, due to the wide attacking surfaces of traditional centralized servers setting, existing systems are subject to single points of failure or malicious data breaches. Therefore, failure to address these inherent limitations properly will hinder the wide adoption of crowdsourcing. In this article, we propose and implement FedCrowd, the first federated and privacy-preserving crowdsourcing platform by using blockchain technology. Our main idea is to employ the smart contract as a trusted platform for systems to release encrypted tasks, and carefully craft matching protocols to enable efficient task recommendations in the ciphertext domain. Our task-matching protocols are highly customized for the decentralized settings, where users can securely perform keyword and range-based queries over federated task indexes without sharing secret keys. We formally analyze the security strengths and complete the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of the FedCrowd platform.
Enhancing English-Learning Performance through a Simulation Classroom for EFL Students Using Augmented Reality—A Junior High School Case Study

In non-English-speaking countries, students learning EFL (English as a Foreign Language) without a “real” learning environment mostly shows poor English-learning performance. In order to improve the English-learning effectiveness of EFL students, we propose the use of augmented reality (AR) to support situational classroom learning and conduct teaching experiments for situational English learning. The purpose of this study is to examine whether the learning performance of EFL students can be enhanced using augmented reality within a situational context. The learning performance of the experimental student group is validated by means of the attention, relevance, confidence, and satisfaction (ARCS) model. According to statistical analysis, the experimental teaching method is much more effective than that of the control group (i.e., the traditional teaching method). The learning performance of the experimental group students is obviously enhanced and the feedback of using AR by EFL students is positive. The experimental results reveal that (1) students can concentrate more on the practice of speaking English as a foreign language; (2) the real-life AR scenarios enhanced student confidence in learning English; and (3) applying AR teaching materials in situational context classes can provide near real-life scenarios and improve the learning satisfaction of students.
Assessing and Forecasting Cybersecurity Impacts

Cyberattacks constitute a major threat to most organizations. Beyond financial consequences, they may entail multiple impacts that need to be taken into account when making risk management decisions to allocate the required cybersecurity resources. Experts have traditionally focused on a technical perspective of the problem by considering impacts in relation with the confidentiality, integrity, and availability of information. We adopt a more comprehensive approach identifying a broader set of generic cybersecurity objectives, the corresponding set of attributes, and relevant forecasting and assessment models. These are used as basic ingredients for decision support in cybersecurity risk management.
Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation

Deep learning methods show promising results for overlapping cervical cell instance segmentation. However, in order to train a model with good generalization ability, voluminous pixel-level annotations are demanded which is quite expensive and time-consuming for acquisition. In this paper, we propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining (MMT-PSM), which consists of a teacher and a student network during training. Two networks are encouraged to be consistent both in feature and semantic level under small perturbations. The teacher's self-ensemble predictions from K-time augmented samples are used to construct the reliable pseudo-labels for optimizing the student. We design a novel strategy to estimate the sensitivity to perturbations for each proposal and select informative samples from massive cases to facilitate fast and effective semantic distillation. In addition, to eliminate the unavoidable noise from the background region, we propose to use the predicted segmentation mask as guidance to enforce the feature distillation in the foreground region. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only, and outperforms state-of-the-art semi-supervised methods. Code: https://github.com/SIAAAAAA/MMT-PSM.
Classifying global state preparation via deep reinforcement learning

Abstract Quantum information processing often requires the preparation of arbitrary quantum states, such as all the states on the Bloch sphere for two-level systems. While numerical optimization can prepare individual target states, they lack the ability to find general control protocols that can generate many different target states. Here, we demonstrate global quantum control by preparing a continuous set of states with deep reinforcement learning. The protocols are represented using neural networks, which automatically groups the protocols into similar types, which could be useful for finding classes of protocols and extracting physical insights. As application, we generate arbitrary superposition states for the electron spin in complex multi-level nitrogen-vacancy centers, revealing classes of protocols characterized by specific preparation timescales. Our method could help improve control of near-term quantum computers, quantum sensing devices and quantum simulations.
Joint adaptive manifold and embedding learning for unsupervised feature selection

As data always lie on a lower-dimensional space, feature selection has become an important step in computer vision, machine learning and data mining. Due to the lack of class information, the performance of unsupervised feature selection depends on how to characterize and preserve the manifold structure among data. In this paper, we propose a novel unsupervised feature selection framework, named as joint adaptive manifold and embedding learning for unsupervised feature selection (JAMEL). It iteratively and adaptively learns lower-dimensional embeddings for data to preserve the manifold structure among data, regresses data to embeddings to measure the importance of features, and learns the manifold structure among data according to the data density in the intrinsic space, where the redundant and noisy features are eliminated. In addition, we present an efficient algorithm to solve the proposed problem, together with the convergence analysis. Finally, the evaluation results with the tasks of k-means, spectral clustering and nearest neighbor classification using the selected features on 12 datasets show the effectiveness and efficiency of our approach.
TOPSIS inspired Budget and Deadline Aware Multi-Workflow Scheduling for Cloud computing

Scheduling is a decision-making mechanism that allows resource sharing among several activities by determining their order of execution on the available resources. In the heterogeneous distributed systems, it is a great challenge to schedule concurrent workflows submitted by different users at different times. Scheduling with deadline and budget constraints are becoming an even more challenging issue for cloud systems due to the cloud dynamics such as on-demand provisioning, elasticity, abundant resource types, and various pricing schemes. A well-managed budget and deadline constraint scheduling is required to optimize the system performance and end-user satisfaction. Hence, improving system performance and optimizing multiple scheduling criteria at the same time is a big challenge. To address these issues, a novel multi-workflow scheduling algorithm based on the Multi-Criteria Decision Making (MCDM) approach, TOPSIS (Technique of Order Preference by Similarity to Ideal Solution) is presented. A weighted sum of run time, cost and data transfer time are used to determine the optimal resource among the available resources in accordance with the task requirements. The performance of the proposed algorithm is compared with the state-of-the-art algorithms such as Budget-Heterogeneous Earliest Finish Time (BHEFT), Budget and Deadline Constraint Heterogeneous Earliest Finish Time (BDHEFT) and Cloud-based Workflow Scheduling Algorithm (CWSA) algorithms based on budget constraint,deadline constraint, and resource utilization. The experimental results demonstrate that the proposed T-BDMWS outperforms current state-of-the-art heuristics with the criteria of achieving the user-specified budget or deadline constraints and resource efficiency.
Learning Joint Visual Semantic Matching Embeddings for Language-Guided Retrieval

Interactive image retrieval is an emerging research topic with the objective of integrating inputs from multiple modalities as query for retrieval, e.g., textual feedback from users to guide, modify or refine image retrieval. In this work, we study the problem of composing images and textual modifications for language-guided retrieval in the context of fashion applications. We propose a unified Joint Visual Semantic Matching (JVSM) model that learns image-text compositional embeddings by jointly associating visual and textual modalities in a shared discriminative embedding space via compositional losses. JVSM has been designed with versatility and flexibility in mind, being able to perform multiple image and text tasks in a single model, such as text-image matching and language-guided retrieval. We show the effectiveness of our approach in the fashion domain, where it is difficult to express keyword-based queries given the complex specificity of fashion terms. Our experiments on three datasets (Fashion-200k, UT-Zap50k, and Fashion-iq) show that JVSM achieves state-of-the-art results on language-guided retrieval and additionally we show its capabilities to perform image and text retrieval.