Reviewerly/exHarmony huggingface dataset:
A Determination Scheme for Quasi-Identifiers Using Uniqueness and Influence for De-Identification of Clinical Data

Background : The accumulation and usefulness of clinical data have increased with IT development. While using clinical data that needs to be identifiable to obtain meaningful information, it is essential to ensure that data is de-identified. Methods : To formulate such a method, first, primary quasi-identifiers were selected by classifying information in 20 clinical personal information database tables into Direct-Identifier (DID), Quasi-Identifier (QI), Sensitive Attribute (SA), and Non-Sensitive Attribute (NSA) according to its type. Secondary QIs were then selected by assessing the risk for outliers by measuring uniqueness values of the selected data and scoring re-identification by calculating equivalence class of the influence on other data on QI removal. Third, the risk of re-identification of data users was numeralized and classified. Lastly, the final QI according to user class was determined by comparing the calculated re-identification scores to the threshold values of user classes. Results : Eventually, final QIs ranging 17 to 28 were selected by making an assumption. Conclusions : Therefore, clinical data users can securely and efficiently use clinical data containing personal information by objectively selecting QIs using the method proposed in the present study.
Twitter-based Opinion Mining for Flight Service utilizing Machine Learning

Twitter is one of the most prominent social networking platforms so far. Millions of users utilize Twitter to share their thoughts and views on various topics of interest every day resulting a huge amount of data. This data could be considered to have a rich source of useful information hidden inside. Using machine learning to this data may give rise to effective recommender frameworks for individuals to manage their lives in a much more convenient way. In this paper, we propose a machine learning approach to classify the passenger’s tweets regarding the airplane services to understand the pattern of emotions. We adopt Random Forest (RF) and Logistic Regression (LR) to classify each tweet into positive, negative and neutral sentiment. The evaluation of the collected real data demonstrates that these two methods are able to achieve an accuracy ≈80%.
Authenticated teleportation and verification in a noisy network

Authenticated teleportation aims to certify the transmission of a quantum state through teleportation, even in the presence of an adversary. This scenario can be pictured in terms of an untrusted source distributing a Bell state between two parties who wish to verify it using some simple tests. We propose a protocol that achieves this goal in a practical way, and analyse its performance and security when the parties have noisy measurement devices. Further, we model a realistic experimental scenario where the state is subject to noise and dephasing. We finally apply our analysis to the verification of graph states with noisy measurement devices.
Cryptanalysis of GSM Encryption in 2G/3G Networks Without Rainbow Tables

The GSM standard developed by ETSI for 2G networks adopts the A5/1 stream cipher to protect the over-the-air privacy in cell phone and has become the de-facto global standard in mobile communications, though the emerging of subsequent 3G/4G standards. There are many cryptanalytic results available so far and the most notable ones share the need of a heavy pre-computation with large rainbow tables or distributed cracking network. In this paper, we present a fast near collision attack on GSM encryption in 2G/3G networks, which is completely new and more threatening compared to the previous best results. We adapt the fast near collision attack proposed at Eurocrypt 2018 with the concrete irregular clocking manner in A5/1 to have a state recovery attack with a low complexity. It is shown that if the first 64 bits of one keystream frame are available, the secret key of A5/1 can be reliably found in $$2^{31.79}$$ cipher ticks, given around 1 MB memory and after the pre-computation of $$2^{20.26}$$ cipher ticks. Our current implementation clearly certified the validity of the suggested attack. Due to the fact that A5/3 and GPRS share the same key with A5/1, this can be converted into attacks against any GSM network eventually.
Classifying Unordered Feature Sets with Convolutional Deep Averaging Networks

Unordered feature sets are a nonstandard data structure that traditional neural networks are incapable of addressing in a principled manner. Providing a concatenation of features in an arbitrary order may lead to the learning of spurious patterns or biases that do not actually exist. Another complication is introduced if the number of features varies between each set. We propose convolutional deep averaging networks (CDANs) for classifying and learning representations of datasets whose instances comprise variable-size, unordered feature sets. CDANs are efficient, permutation-invariant, and capable of accepting sets of arbitrary size. We emphasize the importance of nonlinear feature embeddings for obtaining effective CDAN classifiers and illustrate their advantages in experiments versus linear embeddings and alternative permutation-invariant and -equivariant architectures. We also find that pooling via summation has a significant advantage in CDANs over average- or max-pooling
Hate speech and identity politics. An intercultural communication perspective

Hate speech has become a key element of contemporary political discourse. It has also changed the very structure of communication. With the access to public sphere provided by social media, hate speech engages people in connective action, which allows it to construct and deconstruct collective identity. By doing this, hate speech undermines the idea of multicultural society. In order to succeed, such a society needs to engage its members in inclusive intercultural dialogue while hate speech strongly excludes all dissident voices, deepening political polarisation. This article presents an extensive analysis of hate speech from the perspective of intercultural communication. Drawing from available research and literature, the author puts forward the thesis that hate speech is a communicative phenomenon that not only disrupts intercultural dialogue, but also leads to the disintegration of multicultural society.
Increasing the classical data throughput in quantum networks by combining quantum linear network coding with superdense coding

This paper shows how network coding and superdense coding can be combined to increase the classical data throughput by a factor $2\ensuremath{-}\ensuremath{\epsilon}$ (for some $\ensuremath{\epsilon}>0$) compared to the maximum that could be achieved using either network coding or superdense coding alone. Additionally, a general decomposition of a mixed network (i.e., consisting of classical and quantum links) is given, and it is reasoned that, owing to the inherent hardness of finding network codes, this may well lead to an increase in classical data throughput in practice, should a scenario arise in which quantum networks are used to transfer classical information.
Using Google Apps as Learning Strategy to Enhance ESL Writing

ICT has given a lot of assistance to advanceteachers’ pedagogical approaches. Every moment our education strategies havebeen evolving with the advent of new technology that simplifies teachers’workload in preparing materials and aids students engaged in their learning. Infact, there are lots of research papers about the effectiveness of using ICTthat could engage students in their learning. Writing, known as the hardestskills to master by students as it could be a boring and labouring task.Furthermore, the lack of vocabulary had contributed little confidence and highlevel of anxiety that made them struggle to write. Based on Krashen’s AffectiveFilter Hypothesis, learners with low affective filter, high motivation,self-confidence and a low level of anxiety are better equipped for success insecond language acquisition. It is believed that ICT integration using GoogleApps in the learning process would enhance students’ writing skills. Thus, thisconceptual paper attempts to discuss the potential benefits of using GoogleApps as learning strategy to enhance ESL writing. Some implications andrecommendations are also discussed in the conclusion.
Scalable Resilience Against Node Failures for Communication-Hiding Preconditioned Conjugate Gradient and Conjugate Residual Methods

The observed and expected continued growth in the number of nodes in large-scale parallel computers gives rise to two major challenges: global communication operations are becoming major bottlenecks due to their limited scalability, and the likelihood of node failures is increasing. We study an approach for addressing these challenges in the context of solving large sparse linear systems. In particular, we focus on the pipelined preconditioned conjugate gradient (PPCG) method, which has been shown to successfully deal with the first of these challenges. In this paper, we address the second challenge. We present extensions to the PPCG solver and two of its variants which make them resilient against the failure of a compute node while fully preserving their communication-hiding properties and thus their scalability. The basic idea is to efficiently communicate a few redundant copies of local vector elements to neighboring nodes with very little overhead. In case a node fails, these redundant copies are gathered at a replacement node, which can then accurately reconstruct the lost parts of the solver's state. After that, the parallel solver can continue as in the failure free scenario. Experimental evaluations of our approach illustrate on average very low runtime overheads compared to the standard non-resilient algorithms. This shows that scalable algorithmic resilience can be achieved at low extra cost.
Unsupervised Clustering using Pseudo-semi-supervised Learning

In this paper, we propose a framework that leverages semi-supervised models to improve unsupervised clustering performance. To leverage semi-supervised models, we first need to automatically generate labels, called pseudo-labels. We find that prior approaches for generating pseudo-labels hurt clustering performance because of their low accuracy. Instead, we use an ensemble of deep networks to construct a similarity graph, from which we extract high accuracy pseudo-labels. The approach of finding high quality pseudo-labels using ensembles and training the semi-supervised model is iterated, yielding continued improvement. We show that our approach outperforms state of the art clustering results for multiple image and text datasets. For example, we achieve 54.6% accuracy for CIFAR-10 and 43.9% for 20news, outperforming state of the art by 8-12% in absolute terms.
On Possibilities to Improve the evaluation of Effectiveness of Research-Educational

Evaluation of the effectiveness of scientific research produced, as the rule, by using the bibliometric instruments of the analytics of research publications (level of publications, measured by its reference with the quartiles of research journals in main bibliometric international bases of knowledge Web of Sciences, Scopus, Google Scholar, etc.; exploration of its attributing to the lists of leading international publishing houses; citation indexes of publications). The analytics of research publications in first line is used for the purposes to find, are researchers are fulfilled their KPI, or not, are their research is valuable for scientific community. Same time, other instruments of the effectiveness evaluation of research exists, that are taking into account its complexity, its scientific and educational nature. Such evaluations are mostly integrated into the evaluation of effectiveness (research excellence) of universities and other research organizations. Combination of expert, bibliometric evaluations of research, its usage in educational programs is the specific feature of the complex evaluation of effectiveness in the terms of research excellence evaluation procedures. Unfortunately, economic (applied) instruments of research effectiveness evaluation are not introduced properly yet, it is mostly evaluated as the part of resource base of universities and scientific organizations (resources obtained from research-based innovative products are considered as the part of general resources). Due to the recent establishing of Research-educational Centers (REC) in Russian Federation, the necessity to explore complex multi-component evaluations of REC, including not 2, but 3 main components, as effectiveness of research in REC itself, as their educational programs economic effectiveness, and the research outcomes for real sector of economy. Unfortunately, 2nd and 3rd components evaluations are underdeveloped right now. In the article the indicative instruments for evaluations of research are analyzed with the purpose to apply it for REC and for the quality of educational programs evaluation takin in mind its input into the economic effectiveness of REC. Such an instruments are capable to provide the accurate measuring of economic effectiveness of research and educational programs of REC. We are propose the instruments that are allows to integrate the indicators of regional effectiveness introduced by President of Russia Decree (2019) with some from indicators of effectiveness of REC that are introduced by Government of Russia (2019) and can be attributed to economical effectiveness of REC and of educational programs that are realized in the Centers of Competency Development (CCD) belonged to REC.
Experimentally feasible scheme for a high-fidelity controlled-phase gate with general cat-state qubits

We propose an efficient scheme for implementing the universal controlled-phase gate with logical qubits encoded in the superpositions of $2d$ circularly distributed coherent states ($d$ is a positive integer), which have the advantage of protecting quantum information against the $(d\ensuremath{-}1)$-photon loss errors. We first demonstrate explicitly how to engineer dispersive interaction between the microwave cavity modes and multilevel transmon ancilla in circuit quantum electro-dynamical (QED) system. With the cat-state qubits, we realize the two-qubit controlled phase gate, and its operation time scales as $1/d$. This means that it will be less susceptible to environmental disturbances if we employ larger $d$ to encode information. Finally, we employ the experimentally reachable parameters in circuit QED system to numerically study the influence of the photon loss, the qubit relaxation, and the Kerr nonlinearity. The average fidelity for a controlled-phase gate has reached 0.9955 for $d=8$ when the system imperfections are considered. The results demonstrate the great potential of our scheme for quantum computation in circuit QED system.
Coordinate calculation for direct shape measurement based on optical flow

A quite novel surface profilometry is proposed, which adopts a single optical grating projection setup with a small projection angle. The height distribution of the measured surface is retrieved by calculating the coordinates of the intersection between the projecting ray and the observing sight line. While the position of the observing point in the deformed fringe pattern can be detected by fringe optical flow. The relationship between optical flow and the height distribution of the tested surface is established. Simulations and some primary experiment results are completed to prove that the proposed method is feasible to measure a complex surface. The main advantage of the proposed method is obviously that the height distribution of the measured surface can be obtained directly without phase-to-height transformation.
ON PROFITABILITY OF NAKAMOTO DOUBLE SPEND

Nakamoto double spend strategy, described in Bitcoin foundational article, leads to total ruin with positive probability and does not make sense from the profitability point of view. The simplest strategy that can be profitable incorporates a stopping threshold when success is unlikely. We solve and compute the exact profitability for this strategy. We compute the minimal amount of the double spend that is profitable. For a given amount of the transaction, we determine the minimal number of confirmations to be requested by the recipient such that this double spend strategy is non-profitable. We find that this number of confirmations is only 1 or 2 for average transactions and a small hashrate of the attacker. This is substantially lower than the original Nakamoto numbers that are widely used and are only based on the success probability instead of the profitability.
Compressed Self-Attention for Deep Metric Learning

In this paper, we aim to enhance self-attention (SA) mechanism for deep metric learning in visual perception, by capturing richer contextual dependencies in visual data. To this end, we propose a novel module, named compressed self-attention (CSA), which significantly reduces the computation and memory cost with a neglectable decrease in accuracy with respect to the original SA mechanism, thanks to the following two characteristics: i) it only needs to compute a small number of base attention maps for a small number of base feature vectors; and ii) the output at each spatial location can be simply obtained by an adaptive weighted average of the outputs calculated from the base attention maps. The high computational efficiency of CSA enables the application to high-resolution shallow layers in convolutional neural networks with little additional cost. In addition, CSA makes it practical to further partition the feature maps into groups along the channel dimension and compute attention maps for features in each group separately, thus increasing the diversity of long-range dependencies and accordingly boosting the accuracy. We evaluate the performance of CSA via extensive experiments on two metric learning tasks: person re-identification and local descriptor learning. Qualitative and quantitative comparisons with latest methods demonstrate the significance of CSA in this topic.
Low-cost and Small-sample Fault Diagnosis for 3D Printers Based on Echo State Networks

With the 3D printing rapidly expanding into various fields, 3D printers, as the equipment, should adopt a low-cost and small-sample fault diagnosis methods. A fault diagnosis method based on echo state networks (ESN) for 3D printers is proposed in this paper. A low-cost attitude sensor installed on the 3D printer is employed to collect raw fault data. Subsequently, feature extraction is carried out on the raw fault data. Using these features, ESN, as a shallow learning network, is modeled to diagnose faults of 3D printers. Experimental results show that the fault diagnosis method based on ESN still effective for 3D printers in low-cost and small-sample, which can make the fault recognition accuracy of 3D printer reach to 97.26%. Furthermore, contrast results indicated that the fault diagnosis accuracy of ESN is higher and most stable when compare with support vector machine (SVM), locality preserving projection support vector machine (LPPSVM) and principal component analysis support vector machine (PCASVM).
A Knowledge Transfer Framework for Differentially Private Sparse Learning

We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example. We develop a differentially private high-dimensional sparse learning framework using the idea of knowledge transfer. More specifically, we propose to distill the knowledge from a “teacher” estimator trained on a private dataset, by creating a new dataset from auxiliary features, and then train a differentially private “student” estimator using this new dataset. In addition, we establish the linear convergence rate as well as the utility guarantee for our proposed method. For sparse linear regression and sparse logistic regression, our method achieves improved utility guarantees compared with the best known results (Kifer, Smith and Thakurta 2012; Wang and Gu 2019). We further demonstrate the superiority of our framework through both synthetic and real-world data experiments.
Variational Adversarial Kernel Learned Imitation Learning

Imitation learning refers to the problem where an agent learns to perform a task through observing and mimicking expert demonstrations, without knowledge of the cost function. State-of-the-art imitation learning algorithms reduce imitation learning to distribution-matching problems by minimizing some distance measures. However, the distance measure may not always provide informative signals for a policy update. To this end, we propose the variational adversarial kernel learned imitation learning (VAKLIL), which measures the distance using the maximum mean discrepancy with variational kernel learning. Our method optimizes over a large cost-function space and is sample efficient and robust to overfitting. We demonstrate the performance of our algorithm through benchmarking with four state-of-the-art imitation learning algorithms over five high-dimensional control tasks, and a complex transportation control task. Experimental results indicate that our algorithm significantly outperforms related algorithms in all scenarios.
A Blind Signature from Module Latices

Since its birth by Chaum, blind signatures have become one of the fundamental components in the so-called e-cash or e-voting. The scheme ensures a message to be blinded before being signed. Among all lattice-based signatures submitted to NIST post-quantum cryptographic standardization, Dilithium is a very promising candidate. The scheme has some advantages such as simple to implement securely, conservative with parameters and minimal in total size of public key and signature. In this paper, we propose a blind signature scheme based on the framework of Dilithium in order to take advantages of Dilithium. The proposed scheme is blind and one-more unforgeable secure in the random oracle model under the hardness of the module Learning with Errors problem MLWE and the module short integer solution problem MSIS.
An Approach for Ex-Post-Facto Analysis of Knowledge Graph-Driven Chatbots – The DBpedia Chatbot

As chatbots are gaining popularity for simplifying access to information and community interaction, it is essential to examine whether these agents are serving their intended purpose and catering to the needs of their users. Therefore, we present an approach to perform an ex-post-facto analysis over the logs of knowledge base-driven dialogue systems. Using the DBpedia Chatbot as our case study, we inspect three aspects of the interactions, (i) user queries and feedback, (ii) the bot’s response to these queries, and (iii) the overall flow of the conversations. We discuss key implications based on our findings. All the source code used for the analysis can be found at https://github.com/dice-group/DBpedia-Chatlog-Analysis .
An AI-Based Chatbot Using Deep Learning

Chatbot is a vivid human-like contrivance which bounces chat. The main impartial of the chatbot is to authorization the customers and the machines to associate each other to altercation their chats. The way a machine can know human chats and how they response to the clients is a stimulating effort. Chatbot by means of neural-network and deep learning models give tremendously good consequences in carrying a human verbal exchange. There are many present chats models; however, they have some problems. In this chapter, we discuss about sequence-to-sequence neural-network model which ends up some responses by training with datasets. So, a bot can hardly replace a human, but it is a great help to accomplish specific objectives with a limited reach. Receding from the general use chatbots are given, a useful product can be obtained, one that allows clients to have a different experience without feeling plagued with useless and senseless information.