Reviewerly/exHarmony huggingface dataset:
On the Mediation Price War of 5G Providers

Virtualization guarantees that, moving toward 5G, online services will be versatile and the creation of those will be quick, satisfying the interest of end-clients to a higher degree than what is plausible today. Telcos, cloud administrators, and online application suppliers will unite for conveying those services to clients around the world. Thus, in order to help their portability, or the simple geographic range of the offered application, the business arrangements among the actors must scale over numerous domains and a guaranteed nature of joint effort among different stakeholders is important. Therefore, the vision of the 5G environment is majorly established on the federation of these partners in which they can consistently strive towards the objective of making reliable resource slices and deploying applications within for a maximal geographic reach of clients. In this environment, business perspectives will significantly impact the technical capacity of the system: the business arrangements of the providers will innately decide the accessibility and the end-client costs of certain services. In this work, we model the business relations of infrastructure providers as a variation of network formation games. We infer conditions under which the current transit-peering structure of network providers stays unblemished, and we also draw the specifics of an envisioned setup in which providers create business links among each other starting with a clean slate.
A Showcase of the Use of Autoencoders in Feature Learning Applications

Autoencoders are techniques for data representation learning based on artificial neural networks. Differently to other feature learning methods which may be focused on finding specific transformations of the feature space, they can be adapted to fulfill many purposes, such as data visualization, denoising, anomaly detection and semantic hashing. This work presents these applications and provides details on how autoencoders can perform them, including code samples making use of an R package with an easy-to-use interface for autoencoder design and training, ruta. Along the way, the explanations on how each learning task has been achieved are provided with the aim to help the reader design their own autoencoders for these or other objectives.
TextAttack: Lessons learned in designing Python frameworks for NLP

TextAttack is an open-source Python toolkit for adversarial attacks, adversarial training, and data augmentation in NLP.TextAttack unites 15+ papers from the NLP adversarial attack literature into a single framework, with many components reused across attacks.This framework allows both researchers and developers to test and study the weaknesses of their NLP models.To build such an open-source NLP toolkit requires solving some common problems: How do we enable users to supply models from different deep learning frameworks?How can we build tools to support as many different datasets as possible?We share our insights into developing a well-written, well-documented NLP Python framework in hope that they can aid future development of similar packages.
Effects of Collaborative Augmented Reality on Communication and Interaction in Learning Contexts – Results of a Qualitative Pre-Study

Modern digital technologies like Augmented Reality (AR) are assumed to foster the learning process due to their hands-on nature.AR has the advantage of visualising processes, objects or data and information that would under regular circumstances not be visible or perceptible for the user, since it integrates virtual objects into the real world by an overlay display of these objects.Moreover, collaboration via AR can be realised by using smartphones and tablets.This comprises the advantage of using devices which the students do not need to learn the operation of since they are in everyday use.However, there is still a lack of research on the impact the use of AR has especially on team communication, learning processes, and the general outcomes of collaborative teamwork.Against this background, a prototype of a collaborative AR app was developed as well as a study design was set up for investigating the effects of AR on the teamwork in collaborative learning processes.The study comprises a pre-post-test design in combination with an experimental setting.While the pre-and post-tests are realised by standardised questionnaires and qualitative, semi-standardised focus group interviews, the experimental setting is recorded by video camera in order to qualitatively analyse the video data of the communicative and work-based processes and interactions of the collaboration.
EXPLORING THE BERT CROSS-LINGUAL TRANSFER FOR READING COMPREHENSION

Multilingual BERT has been shown to generalize well in a zero-shot crosslingual setting. This generalization was measured on POS and NER tasks. We explore the multilingual BERT cross-language transferability on the reading comprehension task. We compare different modes of training of question-answering model for a non-English language using both English and language-specific data. We demonstrate that the model based on multilingual BERT is slightly behind the monolingual BERT-based on Russian data, however, it achieves comparable results with the language-specific variant on Chinese. We also show that training jointly on English data and additional 10,000 monolingual samples allows it to reach the performance comparable to the one trained on monolingual data only.
Correction to: The RBF partition of unity method for solving the Klein‑Gordon equation

The article, “The RBF partition of unity method for solving the Klein‑Gordon equation”, written by Mohammadreza Ahmadi Darani1 was originally published electronically on the publisher's internet portal (currently SpringerLink) on 19 September 2020 with open access.
CXP949 at WNUT-2020 Task 2: Extracting Informative COVID-19 Tweets - RoBERTa Ensembles and The Continued Relevance of Handcrafted Features

This paper presents our submission to Task 2 of the Workshop on Noisy User-generated Text.We explore improving the performance of a pre-trained transformer-based language model fine-tuned for text classification through an ensemble implementation that makes use of corpus level information and a handcrafted feature.We test the effectiveness of including the aforementioned features in accommodating the challenges of a noisy data set centred on a specific subject outside the remit of the pre-training data.We show that inclusion of additional features can improve classification results and achieve a score within 2 points of the top performing team.
Analisis Kerentanan Serangan Cross Site Scripting (XSS) pada Aplikasi Smart Payment Menggunakan Framework OWASP

E-commerce that is growing so rapidly can provide space for unauthorized parties in carrying out cybercrime, security anticipation is needed so that e-commerce applications can be protected from harassment or hacking attacks such as cross-site scripting (XSS), malware, exploits, and database injection. This research was conducted to determine the vulnerability of the Smart Payment application by self-test using the ZAP tool. This test is carried out to secure applications that serve as recommendations for follow-up in securing the Smart Payment application. The results of this study found vulnerabilities in the Smart Payment application. Vulnerabilities found were Information Disclosure-Suspicious Comments, X-Frame-Options Header not Set, X-Content-Type-Options Header Missing, Timestamp Disclosure-Unix, XSS Protection Not Enabled Web Browsers, and Directory Browsing. In addition to obtaining vulnerabilities from the Smart Payment application, solutions are also provided to overcome vulnerabilities in the Smart Payment application.
Perturbation Methods for Protection of Sensitive Location Data: Smartphone Travel Survey Case Study

Smartphone based travel data collection has become an important tool for the analysis of transportation systems. Interest in sharing travel survey data has gained popularity in recent years as “open data initiatives” by governments seek to allow the public to use these data, and hopefully to contribute their findings and analysis to the public sphere. The public release of such precise information, particularly location data such as place of residence, opens the risk of privacy violation. At the same time, in order for such data to be useful, as much spatial resolution as possible is desirable for utility in transportation applications and travel demand modeling. This paper evaluates the efficacy of two geographic random perturbation methods—geo-indistinguishability and donut geomask—in protecting the privacy of respondents whose residential location may be published. The study measures the performance of location privacy methods, preservation of utility, and randomness in the distribution of perturbation distances with varying parameters. It is found that both methods produce distributions of spatial perturbations that conform closely to common probability distributions and, as a result, that the original locations can be inferred with little information and a high degree of precision. It is also found that while achieved k -estimate anonymity increases linearly with desired anonymity for the donut geomask, geo-indistinguishability is highly dependent upon its privacy budget factor and is not very effective at assuring desired achieved k -estimate anonymity.
Toward Stance-based Personas for Opinionated Dialogues

In the context of chit-chat dialogues it has been shown that endowing systems with a persona profile is important to produce more coherent and meaningful conversations.Still, the representation of such personas has thus far been limited to a fact-based representation (e.g."I have two cats.").We argue that these representations remain superficial w.r.t. the complexity of human personality.In this work, we propose to make a step forward and investigate stance-based persona, trying to grasp more profound characteristics, such as opinions, values, and beliefs to drive language generation.To this end, we introduce a novel dataset allowing to explore different stance-based persona representations and their impact on claim generation, showing that they are able to grasp abstract and profound aspects of the author persona.
What Do Neural Networks Learn When Trained With Random Labels

We study deep neural networks (DNNs) trained on natural image data with entirely random labels. Despite its popularity in the literature, where it is often used to study memorization, generalization, and other phenomena, little is known about what DNNs learn in this setting. In this paper, we show analytically for convolutional and fully connected networks that an alignment between the principal components of network parameters and data takes place when training with random labels. We study this alignment effect by investigating neural networks pre-trained on randomly labelled image data and subsequently fine-tuned on disjoint datasets with random or real labels. We show how this alignment produces a positive transfer: networks pre-trained with random labels train faster downstream compared to training from scratch even after accounting for simple effects, such as weight scaling. We analyze how competing effects, such as specialization at later layers, may hide the positive transfer. These effects are studied in several network architectures, including VGG16 and ResNet18, on CIFAR10 and ImageNet.
Constructing Extremal Compatible Quantum Observables by Means of Two Mutually Unbiased Bases

We describe a particular class of pairs of quantum observables which are extremal in the convex set of all pairs of compatible quantum observables. The pairs in this class are constructed as uniformly noisy versions of two mutually unbiased bases (MUB) with possibly different noise intensities affecting each basis. We show that not all pairs of MUB can be used in this construction, and we provide a criterion for determining those MUB that actually do yield extremal compatible observables. We apply our criterion to all pairs of Fourier conjugate MUB, and we prove that in this case extremality is achieved if and only if the quantum system Hilbert space is odd-dimensional. Remarkably, this fact is no longer true for general non-Fourier conjugate MUB, as we show in an example. Therefore, the presence or the absence of extremality is a concrete geometric manifestation of MUB inequivalence, that already materializes by comparing sets of no more than two bases at a time.
Characterizing non-deterministic noiseless linear amplifiers at the quantum limit

We address the characterisation of non-deterministic noiseless linear amplifiers (NLAs) and compare the performances of different estimation strategies. In particular, we focus on estimation of the gain, whose value may result from a combination of different experimental effects, as those governing the overall efficiency of the detector or the transmissivity of the involved optical components. At first, we show that, contrary to naive expectations, post-selecting only the amplified states does not offer the most accurate estimate. We then focus on minimal implementations of a NLA, i.e. those obtained by coupling the input state to a two-level system, and show that the maximal amount of information about the gain of the NLA is obtained by measuring the whole composite system. The quantum Fisher information (QFI) of this best-case scenario is analysed in some detail, and compared to the QFI of the post-selected states, both for successful and unsuccessful amplification. Eventually, we show that full extraction of the available information is achieved when the non-deterministic process is implemented by a Lüders instrument. We also analyse the precision attainable by probing NLAs by single-mode pure states and measuring the field or the number of quanta, and discuss in some detail the specific cases of squeezed vacuum and coherent states.
General mapping of multiqudit entanglement conditions to nonseparability indicators for quantum-optical fields

A method is given to transform any multi-qubit entanglement witness into an entanglement indicator for optical fields. This approach can be useful in situations where intensity correlations are measured, and for states for which photon numbers are undefined. When one moves to run-by-run correlations of intensity rates (intensities at a given detector divided by the total locally observed intensity at all detectors at a given station) the approach allows to transform any qudit Bell inequality into a Bell inequality for quantum optical fields.
SAAGs: Biased stochastic variance reduction methods for large-scale learning

Stochastic approximation is one of the effective approach to deal with the large-scale machine learning problems and the recent research has focused on reduction of variance, caused by the noisy approximations of the gradients. In this paper, we have proposed novel variants of SAAG-I and II (Stochastic Average Adjusted Gradient) (Chauhan et al. 2017), called SAAG-III and IV, respectively. Unlike SAAG-I, starting point is set to average of previous epoch in SAAG-III, and unlike SAAG-II, the snap point and starting point are set to average and last iterate of previous epoch in SAAG-IV, respectively. To determine the step size, we have used Stochastic Backtracking-Armijo line Search (SBAS) which performs line search only on selected mini-batch of data points. Since backtracking line search is not suitable for large-scale problems and the constants used to find the step size, like Lipschitz constant, are not always available so SBAS could be very effective in such cases. We have extended SAAGs (I, II, III and IV) to solve non-smooth problems and designed two update rules for smooth and non-smooth problems. Moreover, our theoretical results have proved linear convergence of SAAG-IV for all the four combinations of smoothness and strong-convexity, in expectation. Finally, our experimental studies have proved the efficacy of proposed methods against the state-of-art techniques.
Quantum entanglement, supersymmetry, and the generalized Yang-Baxter equation

Entangled states, such as the Bell and GHZ states, are generated from separable states using matrices known to satisfy the Yang-Baxter equation and its generalization. This remarkable fact hints at the possibility of using braiding operators as quantum entanglers, and is part of a larger speculated connection between topological and quantum entanglement. We push the analysis of this connection forward, by showing that supersymmetry algebras can be used to construct large families of solutions of the spectral parameter-dependent generalized Yang-Baxter equation. We present a number of explicit examples and outline a general algorithm for arbitrary numbers of qubits. The operators we obtain produce, in turn, all the entangled states in a multi-qubit system classified by the Stochastic Local Operations and Classical Communication protocol introduced in quantum information theory.
Integrating Association Rules with Decision Trees in Object-Relational Databases

Research has provided evidence that associative classification produces more accurate results compared to other classification models. The Classification Based on Association (CBA) is one of the famous Associative Classification algorithms that generates accurate classifiers. However, current association classification algorithms reside external to databases, which reduces the flexibility of enterprise analytics systems. This paper implements the CBA in Oracle database using two variant models: hardcoding the CBA in Oracle Data Mining (ODM) package and Integrating Oracle Apriori model with the Oracle Decision tree model. We compared the proposed model performance with Naive Bayes, Support Vector Machine, Random Forests, and Decision Tree over 18 datasets from UCI. Results showed that our models outperformed the original CBA model with 1 percent and is competitive to chosen classification models over benchmark datasets.
The peculiar statistical mechanics of optimal learning machines

Optimal learning machines (OLM) are systems that extract maximally informative representation of the environment they are in contact with, or of the data they are presented. It has recently been suggested that these systems are characterised by an exponential distribution of energy levels. In order to understand the peculiar properties of OLM within a broader framework, I consider an ensemble of optimisation problems over functions of many variables, part of which describe a sub-system and the rest account for its interaction with a random environment. The number of states of the sub-system with a given value of the objective function obeys a stretched exponential distribution, with exponent , and the interaction part is drawn at random from the same distribution, independently for each configuration of the whole system. Systems with then correspond to OLM, and we find that they sit at the boundary between two regions with markedly different properties. For all the system exhibits a freezing phase transition. The transition is discontinuous for and it is continuous for . The region corresponds to learnable energy landscapes and the behaviour of the sub-system becomes predictable as the size of the environment exceeds a critical threshold. For , instead, the energy landscape is unlearnable and the behaviour of the system becomes more and more unpredictable as the size of the environment increases. Sub-systems with (OLM) feature a behaviour which is independent of the relative size of the environment. This is consistent with the expectation that efficient representations should be largely independent of the level of detail of the description of the environment.
Exploring the Potential of GPT-2 for Generating Fake Reviews of Research Papers

Modern tools for natural language generation may enable novel forms of scholarly fraud based on the automatic generation of fake review reports for academic papers, i.e., of a few sentences broadly related to the textual content of a submission and written with the style of an anonymous reviewer. A tool capable of generating such reports automatically and for free could enable various forms of unethical behavior by publishers and researchers. In this work we experiment with a simple heuristic that makes use of widely available and easy to use tools for natural language generation, including the Generative Pretrained Transformer 2 (GPT-2), in order to craft fake reviews automatically. We also perform a small user study for assessing the credibility of those reviews. Our analysis suggests that academic frauds based on fake reviews may indeed be feasible and ready to be deployed in the wild.
Looking inside Noun Compounds: Unsupervised Prepositional and Free Paraphrasing

A noun compound is a sequence of contiguous nouns that acts as a single noun, although the predicate denoting the semantic relation between its components is dropped.Noun Compound Interpretation is the task of uncovering the relation, in the form of a preposition or a free paraphrase.Prepositional paraphrasing refers to the use of preposition to explain the semantic relation, whereas free paraphrasing refers to invoking an appropriate predicate denoting the semantic relation.In this paper, we propose an unsupervised methodology for these two types of paraphrasing.We use pre-trained contextualized language models to uncover the 'missing' words (preposition or predicate).These language models are usually trained to uncover the missing word/words in a given input sentence.Our approach uses templates to prepare the input sequence for the language model.The template uses a special token to indicate the missing predicate.As the model has already been pre-trained to uncover a missing word (or a sequence of words), we exploit it to predict missing words for the input sequence.Our experiments using four datasets show that our unsupervised approach (a) performs comparably to supervised approaches for prepositional paraphrasing, and (b) outperforms supervised approaches for free paraphrasing.Paraphrasing (prepositional or free) using our unsupervised approach is potentially helpful for NLP tasks like machine translation and information extraction.
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model

Across languages, multiple consecutive adjectives modifying a noun (e.g."the big red dog") follow certain unmarked ordering rules.While explanatory accounts have been put forward, much of the work done in this area has relied primarily on the intuitive judgment of native speakers, rather than on corpus data.We present the first purely corpus-driven model of multi-lingual adjective ordering in the form of a latent-variable model that can accurately order adjectives across 24 different languages, even when the training and testing languages are different.We utilize this novel statistical model to provide strong converging evidence for the existence of universal, cross-linguistic, hierarchical adjective ordering tendencies.
Information Communication and Technology (ICT) Adoption in Asian Countries: An Empirical Evidence of Economic and Socio-Cultural Factors

The research dealt with factors affected the different Information Communication and Technology (ICT) adoption rate among Asian countries. The primary purpose was to examine the impact of national wealth and culture on the behavioral adoption of ICT empirically among selected countries in Asia. Data were collected from the World Bank, World Economic Forum, and Hofstede’s cultural dimension index. Statistical correlation and multiple regression were utilized to analyze the dataset, such as Gross Domestic Product (GDP) as a proxy for national wealth and Hofstede’s cultural dimension as a proxy for culture. The result shows a significant relationship between GDP per capita and uncertainty avoidance on the national ICT adoption index. This finding contributes to improving the acknowledgment of the GDP and national culture as the antecedent of ICT adoption in the global field. Practically, the research updates the previous similar ones and proposes further empirical research to extend the scope of analysis.