Reviewerly/exHarmony huggingface dataset:
Experimental decoy-state Bennett-Brassard 1984 quantum key distribution through a turbulent channel

In free-space Quantum Key Distribution in turbulent conditions, scattering and beam wandering cause intensity fluctuations which increase the detected signal-to-noise ratio. This effect can be mitigated by rejecting received bits when the channel's transmittance is below a threshold. Thus, the overall error rate is reduced and the secure key rate increases despite the deletion of bits. In Decoy State BB84 QKD, several methods to find the ideal threshold have already been proposed. One promising method is the Prefixed-Threshold Real-time Selection (P-RTS) where a cutoff can be chosen prior to data collection and independently of the intensity distribution. In this work we perform finite-size Decoy State BB84 QKD in a laboratory setting where we simulate the atmospheric turbulence using an acousto-optical modulator. We show that P-RTS can yield considerably higher secure key rates, especially at higher losses. In addition, we verify that P-RTS gives a robust cutoff even when the predicted transmittance does not match the actual transmittance.
WASP

Adaptability is critical for stream processing systems to ensure stable, low-latency, and high-throughput processing of long-running queries. Such adaptability is particularly challenging for wide-area stream processing due to the highly dynamic nature of the wide-area environment, which includes unpredictable workload patterns, variable network bandwidth, occurrence of stragglers, and failures. Unfortunately, existing adaptation techniques typically achieve these performance goals by compromising the quality/accuracy of the results, and they are often application-dependent. In this work, we rethink the adaptability property of wide-area stream processing systems and propose a resource-aware adaptation framework, called WASP. WASP adapts queries through a combination of multiple techniques: task re-assignment, operator scaling, and query re-planning, and applies them in a WAN-aware manner. It is able to automatically determine which adaptation action to take depending on the type of queries, dynamics, and optimization goals. We have implemented a WASP prototype on Apache Flink. Experimental evaluation with the YSB benchmark and a real Twitter trace shows that WASP can handle various dynamics without compromising the quality of the results.
Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning

Abstract Fueled by advertising companies’ need of accurately tracking users and their online habits, web fingerprinting practice has grown in recent years, with severe implications for users’ privacy. In this paper, we design, engineer and evaluate a methodology which combines the analysis of JavaScript code and machine learning for the automatic detection of web fingerprinters. We apply our methodology on a dataset of more than 400, 000 JavaScript files accessed by about 1, 000 volunteers during a one-month long experiment to observe adoption of fingerprinting in a real scenario. We compare approaches based on both static and dynamic code analysis to automatically detect fingerprinters and show they provide different angles complementing each other. This demonstrates that studies based on either static or dynamic code analysis provide partial view on actual fingerprinting usage in the web. To the best of our knowledge we are the first to perform this comparison with respect to fingerprinting. Our approach achieves 94% accuracy in small decision time. With this we spot more than 840 fingerprinting services, of which 695 are unknown to popular tracker blockers. These include new actual trackers as well as services which use fingerprinting for purposes other than tracking, such as anti-fraud and bot recognition.
Technology and language learning

A considerable number of studies on mobile-assisted language learning have been conducted, but less attention has been paid to online informal learning of English as a Foreign Language (EFL) performed using smartphones among undergraduate EFL learners in Indonesia. Thus, this study was specifically aimed at investigating EFL learners’ most frequently-performed EFL learning activities through smartphones, the predominant online language use, and the relationship between EFL learners’ predominant online language use and their perceived EFL proficiency. The study adopted a quantitative approach. The findings reveal that the participants still more frequently access content and information from their smartphones for receptive rather than interactive/productive online activities. Indonesian, which is the participants’ first language, is still predominantly used for their online activities. The mean score of the perceived EFL proficiency of those who frequently perform online activities in English is statistically and significantly higher than those performing online activities in Indonesian. Overall, the mean score of the perceived EFL proficiency of those performing online activities in English is higher than those in Indonesian, although the difference is not statistically significant. Finally, the study’s implications with suggestions for future research are discussed.
Cryptanalysis of an embedded systems’ image encryption

Fairly recently, a new encryption scheme for embedded systems based on continuous third-order hyperbolic sine chaotic system was proposed by Z. Lin et al. The cryptosystem's main objective is to provide a faster algorithm with lowest computational time in order to be qualified for use in embedded systems especially on a program of UAV (unmanned aerial vehicle). In this paper, we scrutinize the design architecture of this recently proposed scheme against conventional attacks e.g., chosen plaintext attack, differential attack, known plaintext attack. We prove in this paper that, negatively, the studied system is vulnerable. For differential attack, only two chosen plain images are required to recover the full equivalent key. Moreover, only one 3x400 size image is sufficient to break the cryptosystem under chosen plaintext attack considering stability of sort algorithm. Therefore, the proposed scheme is not recommended for security purposes.
A Survey on Cyberspace Search Engines

Abstract This paper introduces the concept of cyberspace search engine, and makes a deep survey on 5 well-known search engines, say Shodan, Censys, BinaryEdge, ZoomEye and Fofa, by querying official websites, analyzing APIs, and making academic research. We discuss the following items in details: Supporting internet protocols, Total amounts of detected devices, Device information, Scanning frequency, System architecture, The third party databases, Probes distribution, etc. We give a comprehensive comparison of the detecting abilities and working principles of the cyberspace search engines.
Text Mining Techniques for Cyberbullying Detection: State of the Art

The dramatic growth of social media during the last years has been associated with the emergence of a new bullying types.Platforms such as Facebook, Twitter, YouTube, and others are now privileged ways to disseminate all kinds of information.Indeed, communicating through social media without revealing the real identity has emerged an ideal atmosphere for cyberbullying, where people can pour out their hatred.Therefore, become very urgent to find automated methods to detect cyberbullying through text mining techniques.So, many researchers have recently investigated various approaches, and the number of scientific studies about this topic is growing very rapidly.Nonetheless, the methods are used to classify the phenomenon and evaluation methods are still under discussion.Subsequently, comparing the results between the studies and identifying their performance is still difficult.Therefore, the current systematic review has been conducted with the aim of survey the researches and studies that have been conducted so far by the research community in the topic of cyberbullying classification based on text language.In order to direct future studies on the topic to a more consistent and compatible perspective on recent works, we undertook a deep review of evaluation methods, features, dataset size, language, and dataset source of the latest research in this field.We made a choice to focus more on techniques that adopted neural networks and machine learning algorithms.After conducting systematic searches and applying the inclusion criteria, 16 different studies were included.It was found that the best accuracy was achieved when a deep learning approach is used particularly CNN approach.It was found also that, SVM is the most common classifier in both Arabic and Latin languages and outperformed the other classifiers.Also, the most widely used feature is N-Gram especially bigram and trigram.Furthermore, results show that Twitter is the main source for the collected datasets, and there are no unified datasets.There is also a shortage of studies in Arabic texts for cyberbullying identification in contrast with English texts.
Visually aware recommendation with aesthetic features

Visual information plays a critical role in human decision-making process. Recent developments on visually aware recommender systems have taken the product image into account. We argue that the aesthetic factor is very important in modeling and predicting users’ preferences, especially for some fashion-related domains like clothing and jewelry. This work is an extension of our previous paper (Yu et al., in: WWW, pp 649–658, 2018), where we addressed the need of modeling aesthetic information in visually aware recommender systems. Technically speaking, we make three key contributions in leveraging deep aesthetic features. In Yu et al. (in: WWW, pp 649–658, 2018), (1) we introduced the aesthetic features extracted from product images by a deep aesthetic network to describe the aesthetics of products. We incorporated these features into recommender system to model users’ preferences in the aesthetic aspect. (2) Since in clothing recommendation, time is very important for users to make decision, we designed a new tensor decomposition model for implicit feedback data. The aesthetic features were then injected to the basic tensor model to capture the temporal dynamics of aesthetic preferences. In this extended version, we try to explore aesthetic features in negative sampling to get further benefit in recommendation tasks. In implicit feedback data, we only have positive samples. Negative sampling is performed to get negative samples. In conventional sampling strategy, uninteracted items are selected as negative samples randomly. However, we may sample potential samples (preferred but unseen items) as negative ones by mistake. To address this gap, (3) we use the aesthetic features to optimize the sampling strategy. We enrich the pairwise training samples by considering the similarity among items in the aesthetic space (and also in the semantic space and graphs). The key idea is that a user may likely have similar perception on similar items. We perform extensive experiments on several real-world datasets and demonstrate the usefulness of aesthetic features and the effectiveness of our proposed methods.
Robustness of noisy quantum networks

Quantum networks are a new paradigm of complex networks, allowing us to harness networked quantum technologies and to develop a quantum internet. But how robust is a quantum network when its links and nodes start failing? We show that quantum networks based on typical noisy quantum-repeater nodes are prone to discontinuous phase transitions with respect to the random loss of operating links and nodes, abruptly compromising the connectivity of the network, and thus significantly limiting the reach of its operation. Furthermore, we determine the critical quantum-repeater efficiency necessary to avoid this catastrophic loss of connectivity as a function of the network topology, the network size, and the distribution of entanglement in the network. In particular, our results indicate that a scale-free topology is a crucial design principle to establish a robust large-scale quantum internet.
Quantum-walk-based search algorithms with multiple marked vertices

The quantum walk is a powerful tool to develop quantum algorithms, which usually are based on searching for a vertex in a graph with multiple marked vertices, Ambainis's quantum algorithm for solving the element distinctness problem being the most shining example. In this work, we address the problem of calculating analytical expressions of the time complexity of finding a marked vertex using quantum walk-based search algorithms with multiple marked vertices on arbitrary graphs, extending previous analytical methods based on Szegedy's quantum walk, which can be applied only to bipartite graphs. Two examples based on the coined quantum walk on two-dimensional lattices and hypercubes show the details of our method.
Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm

In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.
Resource theories of multi-time processes: A window into quantum non-Markovianity

We investigate the conditions under which an uncontrollable background processes may be harnessed by an agent to perform a task that would otherwise be impossible within their operational framework. This situation can be understood from the perspective of resource theory: rather than harnessing 'useful' quantum states to perform tasks, we propose a resource theory of quantum processes across multiple points in time. Uncontrollable background processes fulfil the role of resources, and a new set of objects called superprocesses, corresponding to operationally implementable control of the system undergoing the process, constitute the transformations between them. After formally introducing a framework for deriving resource theories of multi-time processes, we present a hierarchy of examples induced by restricting quantum or classical communication within the superprocess - corresponding to a client-server scenario. The resulting nine resource theories have different notions of quantum or classical memory as the determinant of their utility. Furthermore, one of these theories has a strict correspondence between non-useful processes and those that are Markovian and, therefore, could be said to be a true 'quantum resource theory of non-Markovianity'.
On the Analysis of Variance-reduced and Randomized Projection Variants of Single Projection Schemes for Monotone Stochastic Variational Inequality Problems

Classical extragradient schemes and their stochastic counterpart represent a cornerstone for resolving monotone variational inequality problems. Yet, such schemes have a per-iteration complexity of two projections onto a convex set and require two evaluations of the map, the former of which could be relatively expensive. We consider two related avenues where the per-iteration complexity is significantly reduced: (i) A stochastic projected reflected gradient method requiring a single evaluation of the map and a single projection; and (ii) A stochastic subgradient extragradient method that requires two evaluations of the map, a single projection onto the associated feasibility set, and a significantly cheaper projection (onto a halfspace) computable in closed form. Under a variance-reduced framework reliant on a sample-average of the map based on an increasing batch-size, we prove almost sure convergence of the iterates to a random point in the solution set for both schemes. Additionally, non-asymptotic rate guarantees are derived for both schemes in terms of the gap function; notably, both rates match the best-known rates obtained in deterministic regimes. To address feasibility sets given by the intersection of a large number of convex constraints, we adapt both of the aforementioned schemes to a random projection framework. We then show that the random projection analogs of both schemes also display almost sure convergence under a weak-sharpness requirement; furthermore, without imposing the weak-sharpness requirement, both schemes are characterized by the optimal rate in terms of the gap function of the projection of the averaged sequence onto the set as well as the infeasibility of this sequence. Preliminary numerics support theoretical findings and the schemes outperform standard extragradient schemes in terms of the per-iteration complexity.
Predicting Loan Approval of Bank Direct Marketing Data Using Ensemble Machine Learning Algorithms

The Bank Marketing data set at Kaggle is mostly used in predicting if bank clients will subscribe a long-term deposit. We believe that this data set could provide more useful information such as predicting whether a bank client could be approved for a loan. This is a critical choice that has to be made by decision makers at the bank. Building a prediction model for such high-stakes decision does not only require high model prediction accuracy, but also needs a reasonable prediction interpretation. In this research, different ensemble machine learning techniques have been deployed such as Bagging and Boosting. Our research results showed that the loan approval prediction model has an accuracy of 83.97%, which is approximately 25% better than most state-of-the-art other loan prediction models found in the literature. As well, the model interpretation efforts done in this research was able to explain a few critical cases that the bank decision makers may encounter; therefore, the high accuracy of the designed models was accompanied with a trust in prediction. We believe that the achieved model accuracy accompanied with the provided interpretation information are vitally needed for decision makers to understand how to maintain balance between security and reliability of their financial lending system, while providing fair credit opportunities to their clients.
Fast and Accurate Neural Machine Translation with Translation Memory

It is generally believed that a translation memory (TM) should be beneficial for machine translation tasks.Unfortunately, existing wisdom demonstrates the superiority of TMbased neural machine translation (NMT) only on the TM-specialized translation tasks rather than general tasks, with a non-negligible computational overhead.In this paper, we propose a fast and accurate approach to TM-based NMT within the Transformer framework: the model architecture is simple and employs a single bilingual sentence as its TM, leading to efficient training and inference; and its parameters are effectively optimized through a novel training criterion.Extensive experiments on six TM-specialized tasks show that the proposed approach substantially surpasses several strong baselines that use multiple TMs, in terms of BLEU and running time.In particular, the proposed approach also advances the strong baselines on two general tasks (WMT news Zh→En and En→De).
Taxonomy Completion via Triplet Matching Network

Automatically constructing taxonomy finds many applications in e-commerce and web search. One critical challenge is as data and business scope grow in real applications, new concepts are emerging and needed to be added to the existing taxonomy. Previous approaches focus on the taxonomy expansion, i.e. finding an appropriate hypernym concept from the taxonomy for a new query concept. In this paper, we formulate a new task, “taxonomy completion”, by discovering both the hypernym and hyponym concepts for a query. We propose Triplet Matching Network (TMN), to find the appropriate pairs for a given query concept. TMN consists of one primal scorer and multiple auxiliary scorers. These auxiliary scorers capture various fine-grained signals (e.g., query to hypernym or query to hyponym semantics), and the primal scorer makes a holistic prediction on triplet based on the internal feature representations of all auxiliary scorers. Also, an innovative channel-wise gating mechanism that retains task-specific information in concept representations is introduced to further boost model performance. Experiments on four real-world large-scale datasets show that TMN achieves the best performance on both taxonomy completion task and the previous taxonomy expansion task, outperforming existing methods.
Relation Extraction with Type-aware Map Memories of Word Dependencies

Relation extraction is an important task in information extraction and retrieval that aims to extract relations among the given entities from running texts.To achieve a good performance for this task, previous studies have shown that a good modeling of the contextual information is required, where the dependency tree of the input sentence can be a beneficial source among different types of contextual information.However, most of these studies focus on the dependency connections between words with limited attention paid to exploiting dependency types.In addition, they often treat different dependency connections equally in modeling so that suffer from the noise (inaccurate dependency parses) in the auto-generated dependency tree.In this paper, we propose a neural approach for relation extraction, with type-aware map memories (TaMM) for encoding dependency types obtained from an off-theshelf dependency parser for the input sentence.Specifically, for each word in an entity, TaMM maps all associated words along with the dependencies among them to memory slots and then assigns a weight to each slot according to its contribution to relation extraction.Our approach not only leverages dependency connections and types between words, but also distinguishes reliable dependency information from noisy ones and appropriately model them.The effectiveness of our approach is demonstrated by the experiments on two English benchmark datasets, where our approach achieves state-ofthe-art performance on both datasets.