Reviewerly/exHarmony huggingface dataset:
How is Gaze Influenced by Image Transformations? Dataset and Model

Data size is the bottleneck for developing deep saliency models, because collecting eye-movement data is very time-consuming and expensive. Most of current studies on human attention and saliency modeling have used high-quality stereotype stimuli. In real world, however, captured images undergo various types of transformations. Can we use these transformations to augment existing saliency datasets? Here, we first create a novel saliency dataset including fixations of 10 observers over 1900 images degraded by 19 types of transformations. Second, by analyzing eye movements, we find that observers look at different locations over transformed versus original images. Third, we utilize the new data over transformed images, called data augmentation transformation (DAT), to train deep saliency models. We find that label-preserving DATs with negligible impact on human gaze boost saliency prediction, whereas some other DATs that severely impact human gaze degrade the performance. These label-preserving valid augmentation transformations provide a solution to enlarge existing saliency datasets. Finally, we introduce a novel saliency model based on generative adversarial networks (dubbed GazeGAN). A modified U-Net is utilized as the generator of the GazeGAN, which combines classic "skip connection" with a novel "center-surround connection" (CSC) module. Our proposed CSC module mitigates trivial artifacts while emphasizing semantic salient regions, and increases model nonlinearity, thus demonstrating better robustness against transformations. Extensive experiments and comparisons indicate that GazeGAN achieves state-of-the-art performance over multiple datasets. We also provide a comprehensive comparison of 22 saliency models on various transformed scenes, which contributes a new robustness benchmark to saliency community. Our code and dataset are available at.
Evolution of Shares in a Proof-of-Stake Cryptocurrency

Do the rich always get richer by investing in a cryptocurrency for which new coins are issued according to a proof-of-stake (PoS) protocol? We answer this question in the negative: Without trading, the investor shares in the cryptocurrency are martingales that converge to a well-defined limiting distribution and, hence, are stable in the long run. This result is robust to allowing trading when investors are risk neutral. Then, investors have no incentive to accumulate coins and gamble on the PoS protocol but weakly prefer not to trade. This paper was accepted by Kay Giesecke, finance.
Spatiotemporal consistency-enhanced network for video anomaly detection

Video anomaly detection aims to detect abnormal segments in a video sequence, which is a key problem in video surveillance. Based on deep prediction methods, we propose a spatiotemporal consistency-enhanced network to generate spatiotemporal consistency predictions. A 3D CNN-based encoder and 2D CNN-based decoder constitute the main part of our model. A resampling strategy is applied to the latent space vector when the model is trained by the normal data, yet this can cause the model to perform poorly if the data include abnormal data. Moreover, we combine an input clip with a generated frame into a reformed video clip, which is then fed into a discriminator that is constructed by the 3D CNN to evaluate the consistency of the input clip. Owing to the adversarial training between the generator and discriminator, the spatiotemporal consistency of the generated results is enhanced. During the testing stage, the abnormal data generates a different appearance and motion changes, which affect the ability of our model to predict spatiotemporal consistency in future images. Then, the prediction quality gap between normal and anomalous contents is used to infer whether anomalies occur. Extensive experiments confirm that the proposed method achieves state-of-the-art performance on three benchmark datasets, including ShanghaiTech, CUHK Avenue, and UCSD Ped2.
Multilevel Humanlike Motion Planning for Mobile Robots in Complex Indoor Environments

In this paper, a multilevel humanlike motion planning approach is proposed for indoor mobile robots. Compared with existing approaches, the novelty of this paper is twofold: 1) the proposed path planning framework is multilevel and humanlike to ensure both foreseeability and flexibility, wherein functions of human brain, eyes, and legs are corresponding to global path planning, sensor-level path planning, and action-level path planning, respectively, and 2) along the planned path, a new velocity-adjustable trajectory planning algorithm is put forward which is provably complete and time optimal considering multiple constraints from both the robot and the environment. Experimental results show that the proposed approach has a better performance in terms of efficiency, smoothness, foreseeability, and flexibility, and autonomous navigation is realized in large-scale, dynamic, partially unknown, and unstructured indoor environments.
PhishFarm: A Scalable Framework for Measuring the Effectiveness of Evasion Techniques against Browser Phishing Blacklists

Phishing attacks have reached record volumes in recent years. Simultaneously, modern phishing websites are growing in sophistication by employing diverse cloaking techniques to avoid detection by security infrastructure. In this paper, we present PhishFarm: a scalable framework for methodically testing the resilience of anti-phishing entities and browser blacklists to attackers' evasion efforts. We use PhishFarm to deploy 2,380 live phishing sites (on new, unique, and previously-unseen .com domains) each using one of six different HTTP request filters based on real phishing kits. We reported subsets of these sites to 10 distinct anti-phishing entities and measured both the occurrence and timeliness of native blacklisting in major web browsers to gauge the effectiveness of protection ultimately extended to victim users and organizations. Our experiments revealed shortcomings in current infrastructure, which allows some phishing sites to go unnoticed by the security community while remaining accessible to victims. We found that simple cloaking techniques representative of real-world attacks- including those based on geolocation, device type, or JavaScript- were effective in reducing the likelihood of blacklisting by over 55% on average. We also discovered that blacklisting did not function as intended in popular mobile browsers (Chrome, Safari, and Firefox), which left users of these browsers particularly vulnerable to phishing attacks. Following disclosure of our findings, anti-phishing entities are now better able to detect and mitigate several cloaking techniques (including those that target mobile users), and blacklisting has also become more consistent between desktop and mobile platforms- but work remains to be done by anti-phishing entities to ensure users are adequately protected. Our PhishFarm framework is designed for continuous monitoring of the ecosystem and can be extended to test future state-of-the-art evasion techniques used by malicious websites.
M3S-NIR: Multi-modal Multi-scale Noise-Insensitive Ranking for RGB-T Saliency Detection

RGB-Thermal saliency detection is to use thermal infrared information to assist salient object detection with visible light information. Multi-Modal Multi-Scale Noise-Insensitive Ranking (M3S-NIR), is proposed for RGB-Thermal (RGB-T) saliency detection. Given spatially aligned RGB and thermal images, M3S-NIR first segments them together into a set of multi-scale superpixels. Second, it takes these superpixels as graph nodes and performs multi-modal multi-scale manifold ranking to achieve saliency calculation, in which the cross-modal and cross-scale collaborations are performed to integrate different kinds of information. Third, to handle noises and corruptions of ranking seeds (i.e., boundary superpixels) introduced by salient objects and RGB-T alignment, M3S-NIR introduces an intermediate variable to infer the optimal ranking seeds, and formulates it as a sparse learning problem. Finally, M3S-NIR uses a unified ADMM (Alternating Direction Method of Multipliers)-based optimization framework to solve the ranking model efficiently. Extensive experiments on the benchmark dataset demonstrate the effectiveness of the proposed approach over other state-of-the-art RGB-T saliency detection methods.
Hierarchical Multi-label Classification of Text with Capsule Networks

Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference.In this paper we apply and compare simple shallow capsule networks for hierarchical multi-label text classification and show that they can perform superior to other neural networks, such as CNNs and LSTMs, and non-neural network architectures such as SVMs.For our experiments, we use the established Web of Science (WOS) dataset and introduce a new real-world scenario dataset, the BlurbGenreCollection (BGC).Our results confirm the hypothesis that capsule networks are especially advantageous for rare events and structurally diverse categories, which we attribute to their ability to combine latent encoded information.
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

Quantization can improve the execution latency and energy efficiency of neural networks on both commodity GPUs and specialized accelerators. The majority of existing literature focuses on training quantized DNNs, while this work examines the less-studied topic of quantizing a floating-point model without (re)training. DNN weights and activations follow a bell-shaped distribution post-training, while practical hardware uses a linear quantization grid. This leads to challenges in dealing with outliers in the distribution. Prior work has addressed this by clipping the outliers or using specialized hardware. In this work, we propose outlier channel splitting (OCS), which duplicates channels containing outliers, then halves the channel values. The network remains functionally identical, but affected outliers are moved toward the center of the distribution. OCS requires no additional training and works on commodity hardware. Experimental evaluation on ImageNet classification and language modeling shows that OCS can outperform state-of-the-art clipping techniques with only minor overhead.
Lay Down the Common Metrics: Evaluating Proof-of-Work Consensus Protocols' Security

Following Bitcoin's Nakamoto Consensus protocol (NC), hundreds of cryptocurrencies utilize proofs of work (PoW) to maintain their ledgers. However, research shows that NC fails to achieve perfect chain quality, allowing malicious miners to alter the public ledger in order to launch several attacks, i.e., selfish mining, double-spending and feather-forking. Some later designs, represented by Ethereum, Bitcoin-NG, DECOR+, Byzcoin and Publish or Perish, aim to solve the problem by raising the chain quality; other designs, represented by Fruitchains, DECOR+ and Subchains, claim to successfully defend against the attacks in the absence of perfect chain quality. As their effectiveness remains self-claimed, the community is divided on whether a secure PoW protocol is possible. In order to resolve this ambiguity and to lay down the foundation of a common body of knowledge, this paper introduces a multi-metric evaluation framework to quantitatively analyze PoW protocols' chain quality and attack resistance. Subsequently we use this framework to evaluate the security of these improved designs through Markov decision processes. We conclude that to date, no PoW protocol achieves ideal chain quality or is resistant against all three attacks. We attribute existing PoW protocols' imperfect chain quality to their unrealistic security assumptions, and their unsatisfactory attack resistance to a dilemma between "rewarding the bad" and "punishing the good". Moreover, our analysis reveals various new protocol-specific attack strategies. Based on our analysis, we propose future directions toward more secure PoW protocols and indicate several common pitfalls in PoW security analyses.
Stripe-based and attribute-aware network: a two-branch deep model for vehicle re-identification

Vehicle re-identification (Re-ID) has been attracting increasing interest in the field of computer vision due to the growing utilization of surveillance cameras in public security. However, vehicle Re-ID still suffers a similarity challenge despite the efforts made to solve this problem. This challenge involves distinguishing different instances with nearly identical appearances. In this paper, we propose a novel two-branch stripe-based and attribute-aware deep convolutional neural network (SAN) to learn the efficient feature embedding for vehicle Re-ID task. The two-branch neural network, consisting of stripe-based branch and attribute-aware branches, can adaptively extract the discriminative features from the visual appearance of vehicles. A horizontal average pooling and dimension-reduced convolutional layers are inserted into the stripe-based branch to achieve part-level features. Meanwhile, the attribute-aware branch extracts the global feature under the supervision of vehicle attribute labels to separate the similar vehicle identities with different attribute annotations. Finally, the part-level and global features are concatenated together to form the final descriptor of the input image for vehicle Re-ID. The final descriptor not only can separate vehicles with different attributes but also distinguish vehicle identities with the same attributes. The extensive experiments on both VehicleID and VeRi databases show that the proposed SAN method outperforms other state-of-the-art vehicle Re-ID approaches.
Calculated risk? A cybersecurity evaluation tool for SMEs

Small and medium-sized enterprises (SMEs) are among the least mature and most vulnerable in terms of their cybersecurity risk and resilience. In this article, we describe a methodology developed using the National Institute of Standards and Technology’s (NIST) Cybersecurity Framework (CSF) as a starting point. The NIST CSF does not meet all the needs of the SME IT leader, but it offers a solid foundation for a useful evaluation and recommendation methodology. We propose an SME cybersecurity evaluation tool (CET) that consists of a 35-question online survey to be completed by IT leaders to self-rate their maturity within the five NIST framework categories: identify, protect, detect, respond, and recover. We outline this approach to cybersecurity risk management before discussing its effectiveness and implications for practitioners.
An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining

Multi-task learning (MTL) has achieved remarkable success in natural language processing applications. In this work, we study a multi-task learning model with multiple decoders on varieties of biomedical and clinical natural language processing tasks such as text similarity, relation extraction, named entity recognition, and text inference. Our empirical results demonstrate that the MTL fine-tuned models outperform state-of-the-art transformer models (e.g., BERT and its variants) by 2.0% and 1.3% in biomedical and clinical domain adaptation, respectively. Pairwise MTL further demonstrates more details about which tasks can improve or decrease others. This is particularly helpful in the context that researchers are in the hassle of choosing a suitable model for new problems. The code and models are publicly available at https://github.com/ncbi-nlp/bluebert.
SoK: A Classification Framework for Stablecoin Designs

Stablecoins promise to bridge fiat currencies with the world of cryptocurrencies. They provide a way for users to take advantage of the benefits of digital currencies, such as ability to transfer assets over the internet, credibly commit to minting schedules, and enable new asset classes, while also partially mitigating their volatility risks. In this paper, we systematically discuss general design, decompose existing stablecoins into various component design elements, explore their strengths and drawbacks, and identify future directions.
Cognitive vision inspired object segmentation metric and loss function

Object segmentation (OS) technology is a research hotspot in computer vision, and it has a wide range of applications in many fields. Cognitive vision studies have shown that human vision is highly sensitive to both global information and local details in scenes.To this end, we design a novel, efficient, and easy-to-use enhanced-alignment measure ($E_\\xi$) for evaluating the performance of the OS model.$E_\\xi$ combines local pixel values with the image-level mean value, jointly evaluates the image-/pixel-level similarity between a segmentation result and a ground-truth (GT) result.Extensive experiments on the four popular benchmarks via five meta-measures, i.e., application ranking, demoting generic, denying noise, human ranking, andrecognizing GT, show significant relative improvement compared with existing widely-adopted evaluation metrics such as IoU and $F_\\beta$.By using the weighted binary cross-entropy loss, the enhanced-alignment loss, and the weighted IoU loss, we further design a hybrid loss function (Hybrid-$E_{\\rm~loss}$) to guide the network to learn pixel-, object- and image-level features.Qualitative and quantitative results show further improvement in terms of accuracy when using our hybrid loss function in three different OS tasks.
Entanglement Swapping with Semiconductor-Generated Photons Violates Bell’s Inequality

Transferring entangled states between photon pairs is essential in quantum communication. Semiconductor quantum dots are the leading candidate for generating polarization-entangled photons deterministically. Here we show for the first time swapping of entangled states between two pairs of photons emitted by a single dot. A joint Bell measurement heralds the successful generation of the Bell state ${\mathrm{\ensuremath{\Psi}}}^{+}$, yielding a fidelity of $0.81\ifmmode\pm\else\textpm\fi{}0.04$ and violating the CHSH and Bell inequalities. Our photon source matches atomic quantum memory frequencies, facilitating implementation of hybrid quantum repeaters.
Shifting the Baseline: Single Modality Performance on Visual Navigation &amp;

Jesse Thomason, Daniel Gordon, Yonatan Bisk. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Performance Evaluation of Machine Learning Algorithms for Credit Card Fraud Detection

Credit card transactions have become common place today and so is the frauds associated with it. One of the most common modus operandi to carry out fraud is to obtain the card information illegally and use it to make online purchases. For credit card companies and merchants, it is in-feasible to detect these fraudulent transactions among thousands of normal transactions. If sufficient data is collected and made available, machine learning algorithms can be applied to solve this problem. In this work, popular supervised and unsupervised machine learning algorithms have been applied to detect credit card frauds in a highly imbalanced dataset. It was found that unsupervised machine learning algorithms can handle the skewness and give best classification results.
How an Enterprise Blockchain Application in the U.S. Pharmaceuticals Supply Chain is Saving Lives

The MediLedger Project has built a blockchain ecosystem application that prevents counterfeit pharmaceuticals from entering the U.S. pharmaceuticals supply chain. From the lessons learned, we make four recommendations: 1) use a “benevolent dictator” and base governance on “consensus through collaboration”; 2) store the verification of transactions, not the transactions themselves, on the blockchain; 3) use zero-knowledge proofs to verify product and transaction authenticity while preserving full privacy; 4) use blockchain application capabilities not found in traditional technologies to fix ineffective IS landscapes.
Financial access, governance and the persistence of inequality in Africa: Mechanisms and policy instruments

The aim of this paper is to investigate policy instruments by which the persistence of inequality is affected by financial development channels in 48 African countries for the period of 1996–2014. Financial dynamic channels of depth, efficiency, activity and stability are used. Political, economic and institutional governance policy instruments are also involved. The empirical evidence is based on the generalised method of moments. The results show that financial depth and financial stability are the best channels of reducing inequality. Moreover, the relevance of these financial channels is significantly apparent when policy instruments are exclusively governance variables. The comparative relevance of governance dynamics in the persistence of inequality is discussed. The study responds to two recent policy and scholarly challenges, notably: the persistence of inequality in Africa and the relevance of governance in addressing income inequality by means of financial access.
Digital contact-tracing during the Covid-19 pandemic: An analysis of newspaper coverage in Germany, Austria, and Switzerland

Governments around the globe have started to develop and deploy digital contact tracing apps to gain control over the spread of the novel coronavirus (Covid-19). The appropriateness and usefulness of these technologies as a containment measure have since sparked political and academic discussions globally. The present paper contributes to this debate through an exploration of how the national daily newspapers in Germany, Austria, and Switzerland reported on the development and adoption of digital contact-tracing apps during early and after stages of the lockdown. These countries were among the first in Europe to develop apps and were critical voices in the debate of decentralized vs. centralized data processing. We conducted thematic analysis on news coverage published between January and May 2020 in high-circulation national daily newspapers from Germany, Austria, and Switzerland. A total of 148 articles from nine newspaper companies were included in the final analysis. From our analysis emerged six core themes of the development and adoption of digital contact tracing apps: 1) data governance; 2) role of IT giants; 3) scientific rigor; 4) voluntariness; 5) functional efficacy; 6) role of the app. These results shed light on the different facets of discussion regarding digital contact tracing as portrayed in German-speaking media. This study complements emerging survey data on public perceptions of digital contact tracing apps by providing a better understanding of the ideas circulating in the media ecosystem.
Theoretical Perspectives on Adaptive Automation

This chapter describes the theoretical basis of adaptive automation and surveys research and development efforts aimed at validation of this approach to automation design. The concept of adaptive automation originated with work in artificial intelligence in the 1970s. Adaptive automation is a form of automation that is flexible or dynamic in nature. In adaptive systems, decisions regarding the initiation, cessation, and type of automation are shared between the human operator and machine intelligence. Numerous theories, models, and platforms for delivering adaptive automation have already been proposed. There has been a growing interest in the merits of automation that is dynamic or adaptive in nature. Adaptive automation makes it possible for a system to have autonomous control over changes among modes of automation. Adaptive systems represent the next step in the evolution of automation, and it is a big step. The success of adaptive systems will in large part be determined by the interface.