Reviewerly/exHarmony huggingface dataset:
A note on the Signal-to-noise ratio of $ (n, m) $-functions

&lt;p style='text-indent:20px;'&gt;The concept of the signal-to-noise ratio (SNR) as a useful measure indicator of the robustness of &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ (n, m) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-functions &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ F = (f_1, \ldots, f_m) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; (cryptographic S-boxes) against differential power analysis (DPA), has received extensive attention during the previous decade. In this paper, we give an upper bound on the SNR of balanced &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ (n, m) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-functions, and a clear upper bound regarding unbalanced &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ (n, m) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-functions. Moreover, we derive some deep relationships between the SNR of &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ (n, m) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-functions and three other cryptographic parameters (the maximum value of the absolute value of the Walsh transform, the sum-of-squares indicator, and the nonlinearity of its coordinates), respectively. In particular, we give a trade-off between the SNR and the refined transparency order of &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ (n, m) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-functions. Finally, we prove that the SNR of &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ (n, m) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-functions is not affine invariant, and data experiments verify this result.&lt;/p&gt;
Privacy Preservation in Federated Learning: Insights from the GDPR Perspective.

Along with the blooming of AI and Machine Learning-based applications and services, data privacy and security have become a critical challenge. Conventionally, data is collected and aggregated in a data centre on which machine learning models are trained. This centralised approach has induced severe privacy risks to personal data leakage, misuse, and abuse. Furthermore, in the era of the Internet of Things and big data in which data is essentially distributed, transferring a vast amount of data to a data centre for processing seems to be a cumbersome solution. This is not only because of the difficulties in transferring and sharing data across data sources but also the challenges on complying with rigorous data protection regulations and complicated administrative procedures such as the EU General Data Protection Regulation (GDPR). In this respect, Federated learning (FL) emerges as a prospective solution that facilitates distributed collaborative learning without disclosing original training data whilst naturally complying with the GDPR. Recent research has demonstrated that retaining data and computation on-device in FL is not sufficient enough for privacy-guarantee. This is because ML model parameters exchanged between parties in an FL system still conceal sensitive information, which can be exploited in some privacy attacks. Therefore, FL systems shall be empowered by efficient privacy-preserving techniques to comply with the GDPR. This article is dedicated to surveying on the state-of-the-art privacy-preserving techniques which can be employed in FL in a systematic fashion, as well as how these techniques mitigate data security and privacy risks. Furthermore, we provide insights into the challenges along with prospective approaches following the GDPR regulatory guidelines that an FL system shall implement to comply with the GDPR.
SMYRF - Efficient Attention using Asymmetric Clustering

We propose a novel type of balanced clustering algorithm to approximate attention. Attention complexity is reduced from $O(N^2)$ to $O(N \log N)$, where $N$ is the sequence length. Our algorithm, SMYRF, uses Locality Sensitive Hashing (LSH) in a novel way by defining new Asymmetric transformations and an adaptive scheme that produces balanced clusters. The biggest advantage of SMYRF is that it can be used as a drop-in replacement for dense attention layers without any retraining. On the contrary, prior fast attention methods impose constraints (e.g. queries and keys share the same vector representations) and require re-training from scratch. We apply our method to pre-trained state-of-the-art Natural Language Processing and Computer Vision models and we report significant memory and speed benefits. Notably, SMYRF-BERT outperforms (slightly) BERT on GLUE, while using $50\%$ less memory. We also show that SMYRF can be used interchangeably with dense attention before and after training. Finally, we use SMYRF to train GANs with attention in high resolutions. Using a single TPU, we were able to scale attention to 128x128=16k and 256x256=65k tokens on BigGAN on CelebA-HQ.
Quantum Coupon Collector

We study how efficiently a k-element set S⊆[n] can be learned from a uniform superposition |S> of its elements. One can think of |S>=∑_{i∈S}|i>/√|S| as the quantum version of a uniformly random sample over S, as in the classical analysis of the collector problem. We show that if k is close to n, then we can learn S using asymptotically fewer quantum samples than random samples. In particular, if there are n-k=O(1) missing elements then O(k) copies of |S> suffice, in contrast to the Θ(k log k) random samples needed by a classical coupon collector. On the other hand, if n-k=Ω(k), then Ω(k log k) quantum samples are necessary.More generally, we give tight bounds on the number of quantum samples needed for every k and n, and we give efficient quantum learning algorithms. We also give tight bounds in the model where we can additionally reflect through |S>. Finally, we relate coupon collection to a known example separating proper and improper PAC learning that turns out to show no separation in the quantum case.
Machine Translation with Unsupervised Length-Constraints

We have seen significant improvements in machine translation due to the usage of deep learning. While the improvements in translation quality are impressive, the encoder-decoder architecture enables many more possibilities. In this paper, we explore one of these, the generation of constraint translation. We focus on length constraints, which are essential if the translation should be displayed in a given format. In this work, we propose an end-to-end approach for this task. Compared to a traditional method that first translates and then performs sentence compression, the text compression is learned completely unsupervised. By combining the idea with zero-shot multilingual machine translation, we are also able to perform unsupervised monolingual sentence compression. In order to fulfill the length constraints, we investigated several methods to integrate the constraints into the model. Using the presented technique, we are able to significantly improve the translation quality under constraints. Furthermore, we are able to perform unsupervised monolingual sentence compression.
A Homogeneous-Heterogeneous Ensemble of Classifiers

In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each other after using random projections, we construct the homogeneous module by applying random projections on the training data to obtain the new training sets. In the heterogeneous module, several learning algorithms will train on the new training sets to generate the base classifiers. We propose four combining algorithms based on Sum Rule and Majority Vote Rule for the proposed ensemble. Experiments on some popular datasets confirm that the proposed ensemble method is better than several well-known benchmark algorithms proposed framework has great flexibility when applied to real-world applications. The proposed framework has great flexibility when applied to real-world applications by using any techniques that make rich training data for the homogeneous module, as well as using any set of learning algorithms for the heterogeneous module.
Improving Event Duration Prediction via Time-aware Pre-training

End-to-end models in NLP rarely encode external world knowledge about length of time. We introduce two effective models for duration prediction, which incorporate external knowledge by reading temporal-related news sentences (time-aware pre-training). Specifically, one model predicts the range/unit where the duration value falls in (R-PRED); and the other predicts the exact duration value (E-PRED). Our best model – E-PRED, substantially outperforms previous work, and captures duration information more accurately than R-PRED. We also demonstrate our models are capable of duration prediction in the unsupervised setting, outperforming the baselines.
Interleaving Generation Evolutionary Algorithm with Precedence Evaluation of Tentative Offspring

This paper proposes the method to improve the CPU utilization by using precedence evaluation of tentative offspring for the previous method. The previous research proposed the Interleaving Generation Evolutionary Algorithm (IGEA) that generates individuals which parents are evaluated before the evaluation of all individuals completed. IGEA can reduce the execution time for the optimization with EA. We compare the proposed method with the original IGEA and investigate the effectiveness of the proposed method. The experimental results show that the proposed method has higher CPU utilization than the original IGEA.
Nash Equilibrium and Bisimulation Invariance

Game theory provides a well-established framework for the analysis ofconcurrent and multi-agent systems. The basic idea is that concurrent processes(agents) can be understood as corresponding to players in a game; playsrepresent the possible computation runs of the system; and strategies definethe behaviour of agents. Typically, strategies are modelled as functions fromsequences of system states to player actions. Analysing a system in such asetting involves computing the set of (Nash) equilibria in the concurrent game.However, we show that, with respect to the above model of strategies (arguably,the standard model in the computer science literature), bisimilarity does notpreserve the existence of Nash equilibria. Thus, two concurrent games which arebehaviourally equivalent from a semantic perspective, and which from a logicalperspective satisfy the same temporal logic formulae, may nevertheless havefundamentally different properties (solutions) from a game theoreticperspective. Our aim in this paper is to explore the issues raised by thisdiscovery. After illustrating the issue by way of a motivating example, wepresent three models of strategies with respect to which the existence of Nashequilibria is preserved under bisimilarity. We use some of these models ofstrategies to provide new semantic foundations for logics for strategicreasoning, and investigate restricted scenarios where bisimilarity can be shownto preserve the existence of Nash equilibria with respect to the conventionalmodel of strategies in the computer science literature.
Automatically Augmented Reality with GeoGebra

In this chapter we describe a scenario in which automated reasoning and augmented reality are interrelated, yielding an Automatically Augmented Reality (in short: AAR). This is exemplified in the context of “math promenades”, in which GeoGebra’s recently implemented features for the automated manipulation of geometric facts are the basis for the automated development of mathematical layers on top of monuments’ photographic images, captured by smartphones as we walk along these cultural or touristic attractions, as a way to highlight their geometric properties for mathematical education or dissemination purposes.
Silicon photonics for artificial intelligence applications

Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference.
Reference-frame-independent measurement-device-independent quantum key distribution with mismatched-basis statistics

The source flaw associated with the basis vector in the reference-frame-independent measurement-device-independent quantum key distribution (RFI-MDI-QKD) has not been systematically studied. As a result, it is often assumed that bit error is equal to phase error, which is not theoretically rigorous. Here, we propose a postprocessing method to estimate the phase error rate from the discarded mismatched-basis statistics, where the qubit source does not need to be characterized in detail. The source flaw in the basis vector of the RFI-MDI-QKD protocol can thus be corrected using this method. The numerical simulation results clearly demonstrate that the RFI-MDI-QKD protocol with uncharacterized sources is also insensitive to the misalignment of the reference frame.
Symmetry-protection of multiphoton states of light

Abstract In this manuscript we analyze the emergence of protected multiphoton states in scattering problems with cylindrical symmetry. In order to do that, we first provide a formal definition of the concept of postselected symmetry-protection. We show that the notion of symmetry-protection is not limited to one- or two-photon states, on the contrary, it can be formally extended to the multiphoton case. In addition, we prove for the case of cylindrical symmetry that all possible multiphoton protected states are constructed from a small set of one- and two-photon states. Finally, we point out possible applications that symmetry-protected states may have in quantum communications, concretely, in the construction of decoherence-free subspaces.
Harmony Search Algorithms for Optimizing Extreme Learning Machines

Al-Shamiri, Abobakr Khalil Sadollah, Ali Kim, Joong HoonExtreme learning machine (ELM) is a non-iterative algorithm for training single-hidden layer feedforward neural network (SLFN). ELM has been shown to have good generalization performance and faster learning speed than conventional gradient-based learning algorithms. However, due to the random determination of the hidden neuron parameters (i.e., input weights and biases) ELM may require a large number of neurons in the hidden layer. In this paper, the original harmony search (HS) and its variants, namely, improved harmony search (IHS), global-best harmony search (GHS), and intelligent tuned harmony search (ITHS) are used to optimize the input weights and hidden biases of ELM. The output weights are analytically determined using the Moore–Penrose (MP) generalized inverse. The performance of the hybrid approaches is tested on several benchmark classification problems. The simulation results show that the integration of HS algorithms with ELM has obtained compact network architectures with good generalization performance.
Single Sheet Separation Method from Piled Fabrics by Roller Hand Mechanism

In order to automate the work in a garment factory, we developed a method of single sheet separation from piled fabrics by a roller hand mechanism with a rotating mechanism added to the tip of a robot hand. As a result, we succeeded in separating only single sheet of fabrics with a high probability. It is necessary to eliminate failures for practical use. However, it is difficult to eliminate failure completely due to the characteristics of fabrics. Therefore, we devised a method to determine the success of separation, created an actual machine, and evaluated the performance of actual machine by experimentation using an actual machine. As a result, we could not accurately measure the thickness of the fabric, but we found the possibility of realizing the judgment mechanism of separation success by improving the mechanism.
The Effect of Using Speed Reading Technique to the Students’ Ability in Comprehending a Text

This research aims to determine the effect of applying the effect of speed reading technique on the students’ ability in comprehending a text at grade X of SMA N 4 Pematangsiantar. The problems of this study are “What is the effect of using speed reading technique to the students’ ability in comprehending a text?” and “Is the effect of using speed reading technique more significant to the students’ ability in comprehending a text?” The method used in this research was quantitative research design. The population of this research is the tenth grade students of SMA N 4 Pematangsiantar academic year of 2019/2020. Populations of this research are 235 students. Sample of this research are two classes which are divided into control (X PMIA-6) and experimental class (X PMIA-7). The research instrument in the form of multiple choice test. There are 10 questions.After the data had been analyzed, the researchers conclude that the effect of speed reading technique on the students’ ability in comprehending a text was high. It was found that the value of t-test > t-table (1, 96 > 1, 67). It means that the use of speed reading technique on the students’ ability in comprehending a text is significant effective. Based on the result, it can be seen that the students’ reading comprehension ability increases. They can understand the passages easier and efficiently. Moreover, the students are not bored in learning English since the technique used. They can get maximum result with minimum effort.
A Third-Party Intelligent System for Preventing Call Phishing and Message Scams

The damage from voice phishing reaches one trillion won (around 0,84 billion USD) in the past five years, following a report of Business Korea on August 28, 2018. Voice phishing and mobile phone scams are recognized as a top concern in Korea and the world in recent years. In this paper, we propose an efficient system to identify the caller and alert or prevent dangerous to users. Our system includes a mobile application and web server using client and server architecture. It aims to automatically display the information of unidentified callers and warnings to the user when they receive a call or message. A mobile application installs on a mobile phone to automatically get the caller’s phone number and send it to the server through web services to verify it. The web server applies machine learning to data on a global phone book with Blacklist and Whitelist to validate its phone number.
Application of the Canny’s operation and the template matching method to improve the training set in computer vision training’s issue system

Abstract This article focuses on solving the problem of increasing the efficiency of the process of forming a training set in computer vision training’s issue system based on an artificial neural network. The solution to this problem is implemented by developing software for searching for contours using the canny method in combination with the method of searching for identical samples and then deleting them by using the templatematching algorithm. The implementation of a software tool for expeditious preparation of a high-quality training sample for training a neural network based on the YOLOv3 architecture is described. As the initial data, we used a video stream received from cameras located in the working area of computer vision - on the shelf of a smart refrigerator of the Robimarket system.
Analysis and Enhancement of a Lattice-Based Data Outsourcing Scheme With Public Integrity Verification

Recently, Zhang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed a lattice-based data outsourcing scheme with public integrity verification (DOPIV), which enables an original data owner to delegate a proxy to generate the signatures of data and outsource them to the cloud server. They employed a third party auditor (TPA) to check the integrity of the outsourced data and any TPA can verify the data integrity efficiently. DOPIV is claimed to achieve proxy-oriented secure data outsourcing as well as storage correctness guarantee. Unfortunately, we find that there exist vulnerabilities in DOPIV which allow the cloud server to simply delete the received data without being noticed by the TPA. Fortunately, we come up with a simple and efficient solution to thwart the proposed attack. Our improved scheme maintains all the features claimed in DOPIV.
Learning Object Depth from Camera Motion and Video Object Segmentation

Video object segmentation, i.e., the separation of a target object from background in video, has made significant progress on real and challenging videos in recent years. To leverage this progress in 3D applications, this paper addresses the problem of learning to estimate the depth of segmented objects given some measurement of camera motion (e.g., from robot kinematics or vehicle odometry). We achieve this by, first, introducing a diverse, extensible dataset and, second, designing a novel deep network that estimates the depth of objects using only segmentation masks and uncalibrated camera movement. Our data-generation framework creates artificial object segmentations that are scaled for changes in distance between the camera and object, and our network learns to estimate object depth even with segmentation errors. We demonstrate our approach across domains using a robot camera to locate objects from the YCB dataset and a vehicle camera to locate obstacles while driving.
Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations

Summaries generated from medical conversations can improve recall and understanding of care plans for patients and reduce documentation burden for doctors. Recent advancements in automatic speech recognition (ASR) and natural language understanding (NLU) offer potential solutions to generate these summaries automatically, but rigorous quantitative baselines for benchmarking research in this domain are lacking. In this paper, we bridge this gap for two tasks: classifying utterances from medical conversations according to (i) the SOAP section and (ii) the speaker role. Both are fundamental building blocks along the path towards an end-to-end, automated SOAP note for medical conversations. We provide details on a dataset that contains human and ASR transcriptions of medical conversations and corresponding machine learning optimized SOAP notes. We then present a systematic analysis in which we adapt an existing deep learning architecture to the two aforementioned tasks. The results suggest that modelling context in a hierarchical manner, which captures both word and utterance level context, yields substantial improvements on both classification tasks. Additionally, we develop and analyze a modular method for adapting our model to ASR output.