Reviewerly/exHarmony huggingface dataset:
Experimental Investigation of Technical and Human Factors Related to Phishing Susceptibility

This article reports on a simulated phishing experiment targeting 6,938 faculty and staff at George Mason University. The three-week phishing campaign employed three types of phishing exploits and examined demographic, linked workstation/network monitoring audit data, and a variety of behavioral and psychological factors measured via pre- and post-campaign surveys. While earlier research studies have reported disparate effects of gender and age, the present results suggest that these effects are not significant or are of limited strength and that other underlying factors may be more important. Specifically, significant differences in phishing susceptibility were obtained for different email contexts and based on whether individuals have been successfully phished before (these people were more likely to succumb to subsequent phishing emails in our study). Further, participants who responded to phishing exploits scored higher on impulsivity than the non-clickers. Also, participants whose survey responses indicated that they had more appropriate online “security hygiene habits,” such as checking the legitimacy of links, were less likely to be successfully phished in our campaign. Participants whose post-campaign survey responses indicated that they were suspicious of a phishing email message in our campaign were far less likely to click on the phishing link than those who were not suspicious. Similar results were obtained for judgments of pertinence of the email. Participants who indicated that they thought about the negative consequences of clicking the link were less likely to do so than participants who did not think about the negative consequences. Implications for effective training and awareness are discussed.
Fine-grained Question-Answer sentiment classification with hierarchical graph attention network

User-oriented Question-Answer (QA) text pair plays an increasingly important role in online e-commerce platforms, and expresses sentiment information with complicated semantic relations, causing great challenges for accurate sentiment analysis. To address this problem, we propose a novel hierarchical graph attention network (HGAT) to explore abundant relations. Firstly, we utilize the dependency parser to model relations of sentiment words with consideration of syntactic structures within sub-sentences. Then, to better extract hidden features of these sentiment words, we feed the dependency graph into an improved word-level graph attention network (GAT) that incorporates the learned attention weight with the prior graph edge weight. Besides, the sigmoid self-attention mechanism is applied to aggregate salient word representations. Finally, we establish a graph of all sub-sentences with a strong connection and capture inter-relations and intra-relations through the sentence-level GAT. Extensive experiments show that HGAT can achieve significant improvements in QA-style sentiment classification compared with several baselines.
Dynamic Automated Market Makers for Decentralized Cryptocurrency Exchange

Decentralized cryptocurrency exchange protocols such as Uniswap, Curve and other types of Automated Market Makers (AMMs) maintain a liquidity pool (LP) of two or more assets constrained to maintain at all times a mathematical relation to each other, defined by a given function or curve. We propose a dynamic AMM approach where input from a market price oracle is used to modify the mathematical relationship between the assets so that the pool price continuously and automatically adjusts to be identical to the market price. This approach eliminates arbitrage opportunities.
Type-2 fuzzy broad learning controller for wastewater treatment process

Affected by multiple operation conditions, wastewater treatment process (WWTP) is a complex industrial process with strong nonlinearity and disturbance. How to enhance the rapid tracking response-ability and robustness of the controller is still a challenge when the operation conditions change. To solve this problem, a type-2 fuzzy broad learning controller (T2FBLC) is proposed in this paper. First, a type-2 fuzzy broad learning system (T2FBLS) is constructed in T2FBLC by replacing nodes in feature window with a group of interval type-2 fuzzy submodules. Then, the proposed T2FBLC can take tracking error as inputs while its outputs acting on WWTP to directly obtain a control law, and the controller makes a quick tracking response in different operation conditions. Second, the weight parameters of T2FBLC are adjusted by using the gradient descent method to ensure the control performance. In this way, the developed T2FBLC can realize online learning to reduce tracking errors. Third, according to the Lyapunov function theory, the stability of control strategy is proved. Finally, benchmark simulation model 1 (BSM1) is adopted to verify the effectiveness of T2FBLC. The experimental results prove the applicability and superior tracking performance of the proposed method.
Enhanced image prior for unsupervised remoting sensing super-resolution

Numerous approaches based on training low-high resolution image pairs have been proposed to address the super-resolution (SR) task. Despite their success, low-high resolution image pairs are usually difficult to obtain in certain scenarios, and these methods are limited in the actual scene (unknown or non-ideal image acquisition process). In this paper, we proposed a novel unsupervised learning framework, termed Enhanced Image Prior (EIP), which achieves SR tasks without low/high resolution image pairs. We first feed random noise maps into a designed generative adversarial network (GAN) for satellite image SR reconstruction. Then, we convert the reference image to latent space as the enhanced image prior. Finally, we update the input noise in the latent space with a recurrent updating strategy, and further transfer the texture and structured information from the reference image. Results on extensive experiments on the Draper dataset show that EIP achieves significant improvements over state-of-the-art unsupervised SR methods both quantitatively and qualitatively. Our experiments on satellite (SuperView-1) images reveal the potential of the proposed approach in improving the resolution of remote sensing imagery compared with the supervised algorithms. Source code is available at https://github.com/jiaming-wang/EIP.
Understanding consumer behavior in the multimedia context: incorporating gamification in VR-enhanced web system for tourism e-commerce

This study aims to investigate how gamification and virtual reality (VR)-enhanced web services can be integrated to influence consumer behavior in the context of tourism e-commerce. A gamified VR-enhanced tourism web system (VRTWS) was designed and developed for this investigation, while a research framework with 12 hypotheses was proposed and empirically tested by adopting PLS-SEM approach to analyze 208 valid data collected from survey. The results reveal that both Enjoyment and Activation in Gamification significantly and positively affected Media Richness. Additionally, Media Richness significantly and positively affected both Usefulness and Ease of Use in using VR technology with gamification. Also, a user's Perceived Value is not only positively affected by Usefulness and Ease of Use but also Interactivity and Immersion in a gamified VRTWS. Immersion was found to be positively affected by Presence. Through the positive effect on Satisfaction, user's Perceived Value had positive effect on the Intention toward adoption. The proposed gamified VRTWS and the study results with implications are expected to be referenced by the researchers and practitioners for managing incorporation of gamification into designing, developing, and managing their VR-enhanced service in tourism e-commerce.
Potential Anchoring for imbalanced data classification

Data imbalance remains one of the factors negatively affecting the performance of contemporary machine learning algorithms. One of the most common approaches to reducing the negative impact of data imbalance is preprocessing the original dataset with data-level strategies. In this paper we propose a unified framework for imbalanced data over- and undersampling. The proposed approach utilizes radial basis functions to preserve the original shape of the underlying class distributions during the resampling process. This is done by optimizing the positions of generated synthetic observations with respect to the proposed potential resemblance loss. The final Potential Anchoring algorithm combines over- and undersampling within the proposed framework. The results of the experiments conducted on 60 imbalanced datasets show outperformance of Potential Anchoring over state-of-the-art resampling algorithms, including previously proposed methods that utilize radial basis functions to model class potential. Furthermore, the results of the analysis based on the proposed data complexity index show that Potential Anchoring is particularly well suited for handling naturally complex (i.e. not affected by the presence of noise) datasets.
You See What I Want You to See: Exploring Targeted Black-Box Transferability Attack for Hash-based Image Retrieval Systems

With the large multimedia content online, deep hashing has become a popular method for efficient image retrieval and storage. However, by inheriting the algorithmic back-end from softmax classification, these techniques are vulnerable to the well-known adversarial examples as well. The massive collection of online images into the database also opens up new attack vectors. Attackers can embed adversarial images into the database and target specific categories to be retrieved by user queries. In this paper, we start from an adversarial standpoint to explore and enhance the capacity of targeted black-box transferability attack for deep hashing. We motivate this work by a series of empirical studies to see the unique challenges in image retrieval. We study the relations between adversarial subspace and black-box transferability via utilizing random noise as a proxy. Then we develop a new attack that is simultaneously adversarial and robust to noise to enhance transferability. Our experimental results demonstrate about 1.2-3× improvements of black-box transferability compared with the state-of-the-art mechanisms. The code is available at: https://github.com/SugarRuy/CVPR21_Transferred_Hash.
Privacy-preserving Collaborative Learning with Automatic Transformation Search

Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such reconstruction attacks pose severe threats to collaborative learning. Hence, effective mitigation solutions are urgently desired.In this paper, we propose to leverage data augmentation to defeat reconstruction attacks: by preprocessing sensitive images with carefully-selected transformation policies, it becomes infeasible for the adversary to extract any useful information from the corresponding gradients. We design a novel search method to automatically discover qualified policies. We adopt two new metrics to quantify the impacts of transformations on data privacy and model usability, which can significantly accelerate the search speed. Comprehensive evaluations demonstrate that the policies discovered by our method can defeat existing reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance.
zipcodeR: Advancing the analysis of spatial data at the ZIP code level in R

<h2>Abstract</h2> The United States Postal Service (USPS) assigns unique identifiers for postal service areas known as ZIP codes which are commonly used to identify cities and regions throughout the United States in datasets. Despite the widespread use of ZIP codes, there are challenges in using them for geospatial analysis in the social sciences. This paper presents zipcodeR, an R package that facilitates analysis of ZIP code-level data by providing an offline database of ZIP codes and functions for geocoding, normalizing and retrieving data about ZIP codes and relating them to other geographies in R without depending on any external services.
Nonconvex Structural Sparsity Residual Constraint for Image Restoration

This article proposes a novel nonconvex structural sparsity residual constraint (NSSRC) model for image restoration, which integrates structural sparse representation (SSR) with nonconvex sparsity residual constraint (NC-SRC). Although SSR itself is powerful for image restoration by combining the local sparsity and nonlocal self-similarity in natural images, in this work, we explicitly incorporate the novel NC-SRC prior into SSR. Our proposed approach provides more effective sparse modeling for natural images by applying a more flexible sparse representation scheme, leading to high-quality restored images. Moreover, an alternating minimizing framework is developed to solve the proposed NSSRC-based image restoration problems. Extensive experimental results on image denoising and image deblocking validate that the proposed NSSRC achieves better results than many popular or state-of-the-art methods over several publicly available datasets.
Real-time Instance Segmentation with Discriminative Orientation Maps

Although instance segmentation has made considerable advancement over recent years, it’s still a challenge to design high accuracy algorithms with real-time performance. In this paper, we propose a real-time instance segmentation framework termed OrienMask. Upon the one-stage object detector YOLOv3, a mask head is added to predict some discriminative orientation maps, which are explicitly defined as spatial offset vectors for both foreground and background pixels. Thanks to the discrimination ability of orientation maps, masks can be recovered without the need for extra foreground segmentation. All instances that match with the same anchor size share a common orientation map. This special sharing strategy reduces the amortized memory utilization for mask predictions but without loss of mask granularity. Given the surviving box predictions after NMS, instance masks can be concurrently constructed from the corresponding orientation maps with low complexity. Owing to the concise design for mask representation and its effective integration with the anchor-based object detector, our method is qualified under real-time conditions while maintaining competitive accuracy. Experiments on COCO benchmark show that OrienMask achieves 34.8 mask AP at the speed of 42.7 fps evaluated with a single RTX 2080 Ti. The code is available at https://github.com/duwt/OrienMask.
Automatic Graph Partitioning for Very Large-scale Deep Learning

This work proposes RaNNC (Rapid Neural Network Connector) as middleware for automatic hybrid parallelism. In recent deep learning research, as exemplified by T5 and GPT-3, the size of neural network models continues to grow. Since such models do not fit into the memory of accelerator devices, they need to be partitioned by model parallelism techniques. Moreover, to accelerate training for huge training data, we need a combination of model and data parallelisms, i.e., hybrid parallelism. Given a model description for PyTorch without any specification for model parallelism, RaNNC automatically partitions the model into a set of subcomponents so that (1) each subcomponent fits a device memory and (2) a high training throughput for pipeline parallelism is achieved by balancing the computation times of the subcomponents. Since the search space for partitioning models can be extremely large, RaNNC partitions a model through the following three phases. First, it identifies atomic subcomponents using simple heuristic rules. Next it groups them into coarser-grained blocks while balancing their computation times. Finally, it uses a novel dynamic programming-based algorithm to efficiently search for combinations of blocks to determine the final partitions. In our experiments, we compared RaNNC with two popular frameworks, Megatron-LM (hybrid parallelism) and GPipe (originally proposed for model parallelism, but a version allowing hybrid parallelism also exists), for training models with increasingly greater numbers of parameters. In the pre-training of enlarged BERT models, RaNNC successfully trained models five times larger than those Megatron-LM could, and RaNNC's training throughputs were comparable to Megatron-LM's when pre-training the same models. RaNNC also achieved better training throughputs than GPipe on both the enlarged BERT model pre-training (GPipe with hybrid parallelism) and the enlarged ResNet models (GPipe with model parallelism) in all of the settings we tried. These results are remarkable, since RaNNC automatically partitions models without any modification to their descriptions; Megatron-LM and GPipe require users to manually rewrite the models' descriptions.
Zipf Matrix Factorization: Matrix Factorization with Matthew Effect Reduction

Recommender system recommends interesting items to users based on users' past information history. Researchers have been paying attention to improvement of algorithmic performance such as MAE and precision@K. Major techniques such as matrix factorization and learning to rank are optimized based on such evaluation metrics. However, the intrinsic Matthew Effect problem poses great threat to the fairness of the recommender system, and the unfairness problem cannot be resolved by optimization of traditional metrics. In this paper, we propose a novel algorithm that incorporates Matthew Effect reduction with the matrix factorization framework. We demonstrate that our approach can boost the fairness of the algorithm and enhances performance evaluated by traditional metrics.
TransICD: Transformer Based Code-Wise Attention Model for Explainable ICD Coding

International Classification of Disease (ICD) coding procedure which refers to tagging medical notes with diagnosis codes has been shown to be effective and crucial to the billing system in medical sector. Currently, ICD codes are assigned to a clinical note manually which is likely to cause many errors. Moreover, training skilled coders also requires time and human resources. Therefore, automating the ICD code determination process is an important task. With the advancement of artificial intelligence theory and computational hardware, machine learning approach has emerged as a suitable solution to automate this process. In this project, we apply a transformer-based architecture to capture the interdependence among the tokens of a document and then use a code-wise attention mechanism to learn code-specific representations of the entire document. Finally, they are fed to separate dense layers for corresponding code prediction. Furthermore, to handle the imbalance in the code frequency of clinical datasets, we employ a label distribution aware margin (LDAM) loss function. The experimental results on the MIMIC-III dataset show that our proposed model outperforms other baselines by a significant margin. In particular, our best setting achieves a micro-AUC score of 0.923 compared to 0.868 of bidirectional recurrent neural networks. We also show that by using the code-wise attention mechanism, the model can provide more insights about its prediction, and thus it can support clinicians to make reliable decisions. Our code is available online (https://github.com/biplob1ly/TransICD).
The Secure Lattice-Based Data Aggregation Scheme in Residential Networks for Smart Grid

With the rapid development of computer networks, the network architecture has been more and more complex and diverse. Smart grid is a kind of wireless sensor network consuming energy in smart city. Smart grid communications have developed rapidly in recent years, while the privacy, integrity, and confidentiality of user data have been the main security issues. However, lattice-based homomorphic cryptography can resist quantum attacks and effectively provide the security of the scheme. In this article, we proposed an aggregate signature scheme based on new Batch RSA. After that, a data aggregation scheme is proposed based on this aggregate signature scheme and the qualified homomorphic cryptosystem. Compared to the previous schemes, our scheme is more secure because we implement seven properties, such as post quantum, privacy, and so on. Compared to the representative scheme proposed by Abdallah <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , our scheme has lower computation and communication overhead. In addition, the cryptographic algorithms involved in our scheme are lightweight. The network model in our scheme is suitable for large intelligent communities. So, our scheme is very suitable for the residential area network.
Overview of the Mowjaz Multi-Topic Labelling Task

Multilabel text classification is an important task in Natural Language Processing (NLP). One use case of such a task is in categorizing news articles, where each article may belong to one or more classes. In this work, we present the ICICS2021 Mowjaz Multi-Topic Labelling Task. Given a piece of news, systems participating in this task are expected to select its topic(s). The systems are evaluated based on the F1 score measure. In total, 46 teams registered on the task's CodaLab page. Out of them, 28 teams submitted 309 runs. The results are surprisingly high. Moreover, they are very close to each other with all teams having systems achieving F1 scores ranging between 0.7965 and 0.8567. Most of these systems used deep learning models, such as Recurrent Neural Networks (RNN), coupled with pretrained word embeddings such as BERT-based models. Few of them experimented with traditional machine learning models such as Support Vector Machine (SVM) and Naive Bayes (NB).
Research Towards Yolo-Series Algorithms: Comparison and Analysis of Object Detection Models for Real-Time UAV Applications

Abstract General Unmanned Aerial Vehicles (UAVs) are widely used through the computer vision functions of onboard cameras and embedded systems. However, due to the limited memory and computing power of embedded devices on the UAV platform, it is a very challenging issue to analyze the real-time scene through the object detection method. To deal with these challenges, this paper compares the performance of different Yolo series models on the Pascal VOC dataset, using mAP and FPS as evaluation metrics, and applies the training results to the XTDrone UAV Simulation Platform for testing. We evaluate YOLOv3, YOLOv3-tiny, YOLOv3-SPP3, YOLOv4, and YOLOv4-tiny on the Pascal VOC benchmark dataset; the mAP of YOLOv4 is 87.48%, which is 14.2% higher than that of YOLOv3. FPS reaches 72, and the test time on the test set is 103.86s; the shortest test time on the validation set is Yolov4-tiny, but the mAP only reaches 50.06%, which is not as good as Yolov3-tiny. This paper compares the performance of five models in the Pascal VOC Dataset and simulates them on the XTDrone platform, and finally concludes that Yolov3-Tiny can meet the requirements of real-time, lightweight, and high precision.