Reviewerly/exHarmony huggingface dataset:
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack

The goal of a decision-based adversarial attack on a trained model is to generate adversarial examples based solely on observing output labels returned by the targeted model. We develop HopSkipJumpAttack, a family of algorithms based on a novel estimate of the gradient direction using binary information at the decision boundary. The proposed family includes both untargeted and targeted attacks optimized for $\ell_2$ and $\ell_\infty$ similarity metrics respectively. Theoretical analysis is provided for the proposed algorithms and the gradient direction estimate. Experiments show HopSkipJumpAttack requires significantly fewer model queries than Boundary Attack. It also achieves competitive performance in attacking several widely-used defense mechanisms. (HopSkipJumpAttack was named Boundary Attack++ in a previous version of the preprint.)
Practical decoy-state method for twin-field quantum key distribution

Twin-Field (TF) quantum key distribution (QKD) represents a novel QKD approach whose principal merit is to beat the point-to-point private capacity of a lossy quantum channel, thanks to performing single-photon interference in an untrusted node. Indeed, recent security proofs of various TF-QKD type protocols have confirmed that the secret key rate of these schemes scales essentially as the square root of the transmittance of the channel. Here, we focus on the TF-QKD protocol introduced by Curty et al, whose secret key rate is nearly an order of magnitude higher than previous solutions. Its security relies on the estimation of the detection probabilities associated to various photon-number states through the decoy-state method. We derive analytical bounds on these quantities assuming that each party uses either two, three or four decoy intensity settings, and we investigate the protocol's performance in this scenario. Our simulations show that two decoy intensity settings are enough to beat the point-to-point private capacity of the channel, and that the use of four decoys is already basically optimal, in the sense that it almost reproduces the ideal scenario of infinite decoys. We also observe that the protocol seems to be quite robust against intensity fluctuations of the optical pulses prepared by the parties.
Guided Patchwise Nonlocal SAR Despeckling

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing visible filtering artifacts. Overall, the proposed method compares favourably with all state-of-the-art despeckling filters, and also with our own previous optical-guided filter.
BlackWidow: Monitoring the Dark Web for Cyber Security Information

The Dark Web, a conglomerate of services hidden from search engines and regular users, is used by cyber criminals to offer all kinds of illegal services and goods. Multiple Dark Web offerings are highly relevant for the cyber security domain in anticipating and preventing attacks, such as information about zero-day exploits, stolen datasets with login information, or botnets available for hire. In this work, we analyze and discuss the challenges related to information gathering in the Dark Web for cyber security intelligence purposes. To facilitate information collection and the analysis of large amounts of unstructured data, we present BlackWidow, a highly automated modular system that monitors Dark Web services and fuses the collected data in a single analytics framework. BlackWidow relies on a Docker-based micro service architecture which permits the combination of both preexisting and customized machine learning tools. BlackWidow represents all extracted data and the corresponding relationships extracted from posts in a large knowledge graph, which is made available to its security analyst users for search and interactive visual exploration. Using BlackWidow, we conduct a study of seven popular services on the Deep and Dark Web across three different languages with almost 100,000 users. Within less than two days of monitoring time, BlackWidow managed to collect years of relevant information in the areas of cyber security and fraud monitoring. We show that BlackWidow can infer relationships between authors and forums and detect trends for cybersecurity-related topics. Finally, we discuss exemplary case studies surrounding leaked data and preparation for malicious activity.
Chaotic Image Encryption Algorithm Based on Bit-Combination Scrambling in Decimal System and Dynamic Diffusion

In this paper, an image encryption algorithm based on bit-combination scrambling and dynamic diffusion is proposed based on multiple chaotic systems. First, the plaintext image is scrambled. On the basis of the decimal system, the pixel value of the gray-scale plaintext image is divided into three groups: units, tens, and hundreds' digit. Each group performs different rounds of Arnold mapping, and the parameters of the Arnold mapping and the number of mapping wheels are determined by the plaintext image. The scrambled image is generated by combination. This algorithm avoids the periodicity of Arnold mapping. Second, the dynamic diffusion operation is designed. This operation can start from any position of the plaintext pixel value and combine chaotic sequence to do XOR around this point. Therefore, it has N modes of diffusion, where N is the number of pixel values. Third, this method is extended to color image encryption. The comparison test results show that this method has better security and can resist common attack methods.
<i>p</i><sup>1</sup>-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning

Abstract Recent advances in Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art machine learning techniques across a wide range of application, as well as automating the feature engineering process. In this paper, we broadly study the applicability of deep learning to website fingerprinting. First, we show that unsupervised DNNs can generate lowdimensional informative features that improve the performance of state-of-the-art website fingerprinting attacks. Second, when used as classifiers, we show that they can exceed performance of existing attacks across a range of application scenarios, including fingerprinting Tor website traces, fingerprinting search engine queries over Tor, defeating fingerprinting defenses, and fingerprinting TLS-encrypted websites. Finally, we investigate which site-level features of a website influence its fingerprintability by DNNs.
Detecting Aggression and Toxicity using a Multi Dimension Capsule Network

In the era of social media, hate speech, trolling and verbal abuse have become a common issue.We present an approach to automatically classify such statements, using a new deep learning architecture.Our model comprises of a Multi Dimension Capsule Network that generates the representation of sentences which we use for classification.We further provide an analysis of our model's interpretation of such statements.We compare the results of our model with state-of-art classification algorithms and demonstrate our model's ability.It also has the capability to handle comments that are written in both Hindi and English, which are provided in the TRAC dataset.We also compare results on Kaggle's Toxic comment classification dataset.
STSA: A sine Tree-Seed Algorithm for complex continuous optimization problems

Tree-Seed Algorithm (TSA) has good performance in solving various optimization problems. However, it is inevitable to suffer from slow exploitation when solving complex problems. This paper makes an intensive analysis of TSA. In order to keep the balance between exploration and exploitation, we propose an adaptive automatic adjustment mechanism. The number of seeds can be defined in the initialization process of the optimization algorithm. In order to further improve the convergence rate of TSA, we also modify the change model of seed numbers in the initialization process with randomly changing from more to less. With the improvement of two mechanisms, the main weakness of TSA has been overcome effectively. Based on the above two improvements, we propose a new algorithm-Sine Tree-Seed Algorithm (STSA). STSA achieves good results in solving high-dimensional complex optimization problems. The results obtained from 24 benchmark functions confirm the excellent performance of the proposed method.
Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm

In this paper, a new method for optimal enhancement of the contrast of a medical image is proposed. The main idea is to improve the Gamma correction method to enhance and highlight the image information and the details based on a new design of the World Cup Optimization (WCO) algorithm. Gamma correction is a suitable method for contrast enhancement with an efficiency that directly depends on the correct selection of the Gamma coefficient.In this study, a newly presented algorithm was employed for optimal selection of the Gamma value by considering the entropy, edge content, and multi-objective optimization.The simulation results were compared with 5 state of the art methods for presenting method efficiency. To do this, contrast, homogeneity, weighted average peak signal-to-noise ratio (WPSNR), measure of enhancement (EME), and contrast-to-noise ratio (CNR) were employed.Final results denote that the presented multi-objective optimization algorithm improves the quality of the image contrast and can provide more details and information than the other comparable methods.
Cerberus Channels: Incentivizing Watchtowers for Bitcoin

Bitcoin and similar blockchain systems have a limited transaction throughput because each transaction must be processed by all parties, on-chain. Payment channels relieve the blockchain by allowing parties to execute transactions off-chain while maintaining the on-chain security guarantees, i.e., no party can be cheated out of their funds. However, to maintain these guarantees all parties must follow blockchain updates ardently. To alleviate this issue, a channel party can hire a “watchtower” to periodically check the blockchain for fraud on its behalf. However, watchtowers will only do their job properly if there are financial incentives, fees, and punishments. There are known solutions, but these need complex smart contracts, and as such are not applicable to Bitcoin’s simple script language. This raises the natural question of whether incentivized watchtowers are at all possible in a system like Bitcoin. In this work, we answer this question affirmatively, by introducing Cerberus channels, an extension of Lightning channels. Cerberus channels reward watchtowers while remaining secure against bribing and collusion; thus participants can safely go offline for an extended period of time. We show that Cerberus channels are correct, and provide a proof-of-concept implementation in the Bitcoin script language.
Named Entity Recognition with Partially Annotated Training Data

Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated.We study the problem of Named Entity Recognition (NER) with partially annotated training data in which a fraction of the named entities are labeled, and all other tokens, entities or otherwise, are labeled as non-entity by default.In order to train on this noisy dataset, we need to distinguish between the true and false negatives.To this end, we introduce a constraintdriven iterative algorithm that learns to detect false negatives in the noisy set and downweigh them, resulting in a weighted training set.With this set, we train a weighted NER model.We evaluate our algorithm with weighted variants of neural and non-neural NER models on data in 8 languages from several language and script families, showing strong ability to learn from partial data.Finally, to show real-world efficacy, we evaluate on a Bengali NER corpus annotated by non-speakers, outperforming the prior state-of-the-art by over 5 points F1.
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension

Conversational machine comprehension requires deep understanding of the dialogue flow, and the prior work proposed FlowQA to implicitly model the context representations in reasoning for better understanding.This paper proposes to explicitly model the information gain through dialogue reasoning in order to allow the model to focus on more informative cues.The proposed model achieves stateof-the-art performance in a conversational QA dataset QuAC and sequential instruction understanding dataset SCONE, which shows the effectiveness of the proposed mechanism and demonstrates its capability of generalization to different QA models and tasks 1
Describing increasing proficiency in teachers’ knowledge of the effective use of digital technology

This paper aims to contribute to the theoretical framing of Technological Pedagogical Content Knowledge (TPACK) by exploring how a measurement approach can be used to address the need to improve the prescriptive value of the framework. Building on and extending the work described in Saubern, Urbach, Koehler and Phillips (2019), this paper describes the development of an empirically derived qualitative description of increasing proficiency in TPACK Confidence and TPACK Usefulness. Using the results of a partial credit Rasch analysis of survey responses, five bands of proficiency in TPACK Confidence and five bands of proficiency in TPACK Usefulness were delineated and described. The study found that teachers at higher levels of TPACK proficiency more strongly believe in the value of using technology to facilitate deep thinking and learning and are more confident to use technology to support and facilitate deeper thinking and learning in and across curriculum areas than teachers with lower levels of proficiency. By providing a description of lower and higher proficiency and an inferred typical order of acquisition, the resulting construct maps can be used by researchers to help develop and test hypotheses about teachers' acquisition of TPACK and improve the validity and precision of TPACK survey tools and by teacher educators to better understand and evaluate the TPACK of student teachers and inform the development of teacher education curricula.
Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models

The impressive performance of neural networks on natural language processing tasks attributes to their ability to model complicated word and phrase compositions. To explain how the model handles semantic compositions, we study hierarchical explanation of neural network predictions. We identify non-additivity and context independent importance attributions within hierarchies as two desirable properties for highlighting word and phrase compositions. We show some prior efforts on hierarchical explanations, e.g. contextual decomposition, do not satisfy the desired properties mathematically, leading to inconsistent explanation quality in different models. In this paper, we start by proposing a formal and general way to quantify the importance of each word and phrase. Following the formulation, we propose Sampling and Contextual Decomposition (SCD) algorithm and Sampling and Occlusion (SOC) algorithm. Human and metrics evaluation on both LSTM models and BERT Transformer models on multiple datasets show that our algorithms outperform prior hierarchical explanation algorithms. Our algorithms help to visualize semantic composition captured by models, extract classification rules and improve human trust of models. Project page: https://inklab.usc.edu/hiexpl/
MTRNet: A Generic Scene Text Eraser

Text removal algorithms have been proposed for uni-lingual scripts with regular shapes and layouts. However, to the best of our knowledge, a generic text removal method which is able to remove all or user-specified text regions regardless of font, script, language or shape is not available. Developing such a generic text eraser for real scenes is a challenging task, since it inherits all the challenges of multi-lingual and curved text detection and inpainting. To fill this gap, we propose a mask-based text removal network (MTRNet). MTRNet is a conditional adversarial generative network (cGAN) with an auxiliary mask. The introduced auxiliary mask not only makes the cGAN a generic text eraser, but also enables stable training and early convergence on a challenging large-scale synthetic dataset, initially proposed for text detection in real scenes. What's more, MTRNet achieves state-of-the-art results on several real-world datasets including ICDAR 2013, ICDAR 2017 MLT, and CTW1500, without being explicitly trained on this data, outperforming previous state-of-the-art methods trained directly on these datasets.
Quantum Teleportation of Shared Quantum Secret

Quantum teleportation is a fundamental building block of quantum communications and quantum computations, transferring quantum states between distant physical entities. In the context of quantum secret sharing, the teleportation of quantum information shared by multiple parties without concentrating the information at any place is essential, and this cannot be realized by any previous scheme. We propose and experimentally demonstrate a novel teleportation protocol that enables one to perform this task. It is jointly performed by distributed participants, while none of them can fully access the information. Our scheme can be extended to arbitrary numbers of senders and receivers and to fault-tolerant quantum networks by incorporating error-correction codes.
A Contingency Theory of Representational Complexity in Organizations

A long-standing question in the organizations literature is whether firms are better off by using simple or complex representations of their task environment. We address this question by developing a formal model of how firm performance depends on the process by which firms learn and use representations. Building on ideas from cognitive science, our model conceptualizes this process in terms of how firms construct a representation of the environment and then use that representation when making decisions. Our model identifies the optimal level of representational complexity as a function of (a) the environment’s complexity and uncertainty and (b) the firm’s experience and knowledge about the environment’s deep structure. We use this model to delineate the conditions under which firms should use simple versus complex representations; in doing so, we provide a coherent framework that integrates previous conflicting results on which type of representation leaves firms better off. Among other results, we show that the optimal representational complexity generally depends more on the firm’s knowledge about the environment than it does on the environment’s actual complexity. We also show that the relative advantage of heuristics vis-à-vis more complex representations critically depends on an unstated assumption of “informedness”: that managers can know what are the most relevant variables to pay attention to. We show that when this assumption does not hold, complex representations are usually better than simpler ones.
A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.
Explainable image classification with evidence counterfactual

Abstract The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. A counterfactual explanation highlights the parts of an image which, when removed, would change the predicted class. Both legal scholars and data scientists are increasingly turning to counterfactual explanations as these provide a high degree of human interpretability, reveal what minimal information needs to be changed in order to come to a different prediction and do not require the prediction model to be disclosed. Our literature review shows that existing counterfactual methods for image classification have strong requirements regarding access to the training data and the model internals, which often are unrealistic. Therefore, SEDC is introduced as a model-agnostic instance-level explanation method for image classification that does not need access to the training data. As image classification tasks are typically multiclass problems, an additional contribution is the introduction of the SEDC-T method that allows specifying a target counterfactual class. These methods are experimentally tested on ImageNet data, and with concrete examples, we illustrate how the resulting explanations can give insights in model decisions. Moreover, SEDC is benchmarked against existing model-agnostic explanation methods, demonstrating stability of results, computational efficiency and the counterfactual nature of the explanations.
MetaNet: a new paradigm for data sharing in photonics research

Optimization methods are playing an increasingly important role in all facets of photonics engineering, from integrated photonics to free space diffractive optics. However, efforts in the photonics community to develop optimization algorithms remain uncoordinated, which has hindered proper benchmarking of design approaches and access to device designs based on optimization. We introduce MetaNet, an online database of photonic devices and design codes intended to promote coordination and collaboration within the photonics community. Using metagratings as a model system, we have uploaded over one hundred thousand device layouts to the database, as well as source code for implementations of local and global topology optimization methods. Further analyses of these large datasets allow the distribution of optimized devices to be visualized for a given optimization method. We expect that the coordinated research efforts enabled by MetaNet will expedite algorithm development for photonics design.