Reviewerly/exHarmony huggingface dataset:
Emulation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> -photon Jaynes-Cummings and anti-Jaynes-Cummings models via parametric modulation of a cyclic qutrit

We study a circuit QED setup involving a single cavity mode and a cyclic qutrit whose parameters are time modulated externally. It is shown that in the dispersive regime this system behaves as a versatile platform to implement effective $n$-photon Jaynes-Cummings (JC) and anti-Jaynes-Cummings (AJC) models by suitably setting the modulation frequency. The atomic levels and the cavity Fock states involved in the effective Hamiltonians can be controlled through adjustment of the system parameters, and different JC and AJC interactions can be implemented simultaneously using multitone modulations. Moreover, one can implement some models that go beyond simple JC and AJC-like interaction, such as multiphoton coupling between certain entangled states. We estimate analytically the associated transition rates and study numerically the dynamics in the presence of Markovian dissipation, demonstrating that lower-order effective Hamiltonians can be implemented with current technology.
Building minority dependency treebanks, dictionaries and computational grammars at the same time—an experiment in Karelian treebanking

Building a treebank from scratch can easily be an elaborate, highly time consuming task, especially when working with a minority language with moderately complex morphology and no existing resources.It is also then typically true that language experts and informants with suitable skill sets are a very scarce resource.In this experiment I have attempted to work in parallel on building NLP resources while gathering and annotating the treebank.In particular, I aim to build a decent coverage morphologically annotated lexicon suitable for rule-based morphological analysis as well as accompanying rules for basic morphosyntactic analysis.I propose here a workflow, that I have found useful in avoiding redoing same work with related NLP resource construction.
WiP: Generative Adversarial Network for Oversampling Data in Credit Card Fraud Detection

In this digital world, numerous credit card-based transactions take place all over the world. Concomitantly, gaps in process flows and technology result in many fraudulent transactions. Owing to the spurt in the number of reported fraudulent transactions, customers and credit card service providers incur significant financial and reputation losses respectively. Therefore, building a powerful fraud detection system is paramount. It is noteworthy that fraud detection datasets, by nature, are highly unbalanced. Consequently, almost all of the supervised classifiers, when built on the unbalanced datasets, yield high false negative rates. But, the extant oversampling methods while reducing the false negatives, increase the false positives. In this paper, we propose a novel data oversampling method using Generative Adversarial Network (GAN). We use GAN and its variant to generate synthetic data of fraudulent transactions. To evaluate the effectiveness of the proposed method, we employ machine learning classifiers on the data balanced by GAN. Our proposed GAN-based oversampling method simultaneously achieved high precision, F1-score and dramatic reduction in the count of false positives compared to the state-of-the-art synthetic data generation based oversampling methods such as Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN) and random oversampling. Moreover, an ablation study involving the oversampling based on the ensemble of SMOTE and GAN/WGAN generated datasets indicated that it is outperformed by the proposed methods in terms of F1 score and false positive count.
Multifeature Interactive Fusion Model for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis technology. In recent years, neural networks are widely used to extract features of aspects and contexts and proven to have a dramatic improvement in retrieving the sentiment feature from comments. However, due to the increasing complexity of comment information, only considering sentence or word features, respectively, may cause the loss of key text information. Besides, characters have more microscopic features, so the fusion of features between three different levels, such as sentences, words, and characters, should be taken into consideration for exploring their internal relationship among different granularities. According to the above analysis, we propose a multifeature interactive fusion model for aspect-based sentiment analysis. Firstly, the text is divided into two parts: contexts and aspects; then word embedding and character embedding are associated to further explore the potential features. Secondly, to establish a close relationship between contexts and aspects, features fusion of both aspects and contexts are exploited in our model. Moreover, we apply the attention mechanism to calculate fusion weight of features, so that the key features information plays a more significant role in the sentiment analysis. Finally, we experimented on the three datasets of SemEval2014. The results of experiment showed that our model has a better performance compared with the baseline models.
Suspicion-Free Adversarial Attacks on Clustering Algorithms

Clustering algorithms are used in a large number of applications and play an important role in modern machine learning– yet, adversarial attacks on clustering algorithms seem to be broadly overlooked unlike supervised learning. In this paper, we seek to bridge this gap by proposing a black-box adversarial attack for clustering models for linearly separable clusters. Our attack works by perturbing a single sample close to the decision boundary, which leads to the misclustering of multiple unperturbed samples, named spill-over adversarial samples. We theoretically show the existence of such adversarial samples for the K-Means clustering. Our attack is especially strong as (1) we ensure the perturbed sample is not an outlier, hence not detectable, and (2) the exact metric used for clustering is not known to the attacker. We theoretically justify that the attack can indeed be successful without the knowledge of the true metric. We conclude by providing empirical results on a number of datasets, and clustering algorithms. To the best of our knowledge, this is the first work that generates spill-over adversarial samples without the knowledge of the true metric ensuring that the perturbed sample is not an outlier, and theoretically proves the above.
One-Shot Learning for Long-Tail Visual Relation Detection

The aim of visual relation detection is to provide a comprehensive understanding of an image by describing all the objects within the scene, and how they relate to each other, in &lt; object-predicate-object &gt; form; for example, &lt; person-lean on-wall &gt; . This ability is vital for image captioning, visual question answering, and many other applications. However, visual relationships have long-tailed distributions and, thus, the limited availability of training samples is hampering the practicability of conventional detection approaches. With this in mind, we designed a novel model for visual relation detection that works in one-shot settings. The embeddings of objects and predicates are extracted through a network that includes a feature-level attention mechanism. Attention alleviates some of the problems with feature sparsity, and the resulting representations capture more discriminative latent features. The core of our model is a dual graph neural network that passes and aggregates the context information of predicates and objects in an episodic training scheme to improve recognition of the one-shot predicates and then generate the triplets. To the best of our knowledge, we are the first to center on the viability of one-shot learning for visual relation detection. Extensive experiments on two newly-constructed datasets show that our model significantly improved the performance of two tasks PredCls and SGCls from 2.8% to 12.2% compared with state-of-the-art baselines.
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos

In this paper, we propose a spatial-temporal relational reasoning networks (STRRN) approach to investigate the problem of omni-supervised face alignment in videos. Unlike existing fully supervised methods which rely on numerous annotations by hand, our learner exploits large scale unlabeled videos plus available labeled data to generate auxiliary plausible training annotations. Motivated by the fact that neighbouring facial landmarks are usually correlated and coherent across consecutive frames, our approach automatically reasons about discriminative spatial-temporal relationships among landmarks for stable face tracking. Specifically, we carefully develop an interpretable and efficient network module, which disentangles facial geometry relationship for every static frame and simultaneously enforces the bi-directional cycle-consistency across adjacent frames, thus allowing the modeling of intrinsic spatial-temporal relations from raw face sequences. Extensive experimental results demonstrate that our approach surpasses the performance of most fully supervised state-of-the-arts.
Two decades into Speaker Recognition Evaluation - are we there yet?

The human voice differs substantially between individuals. This facilitates automatic speaker verification (ASV) — recognizing a person from his/her voice. ASV accuracy has substantially increased throughout the past decade due to recent advances in machine learning, particularly deep learning methods. An unfortunate downside has been substantially increased complexity of ASV systems. To help non-experts to kick-start reproducible ASV development, a state-of-the-art toolkit implementing various ASV pipelines and functionalities is required. To this end, we introduce a new open-source toolkit, ASVtorch, implemented in Python using the widely used PyTorch machine learning framework.
Predicting and Integrating Expected Answer Types into a Simple Recurrent Neural Network Model for Answer Sentence Selection

Since end-to-end deep learning models have started to replace traditional pipeline architectures of question answering systems, features such as expected answer types which are based on the question semantics are seldom used explicitly in the models. In this paper, we propose a convolution neural network model to predict these answer types based on question words and a recurrent neural network model to find sentence similarity scores between question and answer sentences. The proposed model outperforms the current state of the art results on an answer sentence selection task in open domain question answering by 1.88% on MAP and 2.96% on MRR scores.
Study of atomic geometry and its effect on photon generation and storage [Invited]

We report the result of our study on the dependency of the photon generation and storage to atomic geometry in an optical resonator. We show that the geometry of atoms in an ensemble can be engineered to control collective excitations in a way to achieve high degree of correlation between photons. Moreover, we discuss the role of geometry in such structures to efficiently store photons among a small number of atomic regions.
EyeGAN: Gaze–Preserving, Mask–Mediated Eye Image Synthesis

Automatic synthesis of realistic eye images with prescribed gaze direction is important for multiple application domains. We introduce EyeGAN, an algorithm to generate eye images in the style of a desired target domain, that inherit annotations available in images from a source domain. EyeGAN takes in input ternary masks, which are used as domain-independent proxies for gaze direction. We evaluate EyeGAN against competing eye image synthesis algorithms by measuring a specific gaze consistency index. In addition, we present results from multiple experiments (involving eye region segmentation, pupil localization, and gaze direction estimation) showing that the use of EyeGANgenerated images with inherited annotations for network training leads to superior performances compared to other domain transfer algorithms.
Towards implementing an AI chatbot platform for museums

Recently, understanding their unique role in storytelling and aiming to attract more visitors, several museums have integrated modern ICT technologies. The problem with these technologies however is that gradually tend to be of no real interest to visitors, lack of significant interaction, cannot be continuously updated, and eventually distract visitors from experiencing the exhibits. Museum visitors do not need to be impressed by a technological application but need to learn about the stories of the exhibits in a creative, human-centered and interactive manner. This paper presents an ongoing work towards implementing a new interactive technological trend for museums, i.e., a museum chatbot platform, namely MuBot. The MuBot platform aims to provide museums the opportunity to create simple, interactive and human-friendly apps for their visitors. Such apps will integrate an intelligent chatbot that uses some of the most advanced AI technologies of Machine Learning, Natural Language Processing/Generation, and the Semantic Web. Museum visitors will be able to use a chatbot application that will be created through the MuBot platform, to chat with a ‘smart’ exhibit. They will be able to ask questions through text or voice (in natural language) and receive audible or written answers. The more the visitors ask, the more MuBot will learn and store new knowledge in its knowledge base. The paper presents a preliminary design of the proposed MuBot platform, experimenting with first prototype implementations using the well-known Dialogflow framework, as well as using a Knowledge Graph-based approach.
Multi-scale Gated Fully Convolutional DenseNets for semantic labeling of historical newspaper images

Historical newspaper image analysis is a challenging task due to the complex layout of newspapers and its variability among collections. While traditional approaches are rule-based methods with many successive steps, recent works show that deep learning approaches can be successfully used to provide a pixel labeling of the various fields occurring in a page. This allows the automatic extraction of the document structure and accessing the different semantic entities. Recent improvements proposed to strengthen convolutional neural network capacities such as gated mechanism may also apply well to to task at end. In this respect, we propose a fully convolutional neural network architecture (FCN) that outputs a pixel-labeling of the various semantic entities that occur in historical newspaper images. Our model is based on a novel Multi-Scale Gated Block architecture (MSGB), made of dense connections and gating mechanisms that handle a multi-scale analysis of the input image with self-attention. Evaluations conducted on 4 historical newspaper datasets including up to 11 semantic classes show that our proposition outperforms standard FCN architectures.
Analysis of the Effect of Quality Mulawarman University Language Center websites on User Satisfaction Using the Webqual 4.0 Method

Mulawarman University Language center website and aims to see the responses of website users and measuring website quality. The Webqual 4.0 method consists of 3 variables, usability, information quality and service interaction quality the addition of the dependent variable, user satisfaction. Validity and reliability tests are used as a way to test this research. The results showed that variable usability, information quality and service interaction quality partially had a strong influence on user satisfaction and simultaneously had a very strong influence on user satisfaction with a percentage effect of 64.2% and the remaining 35.8% influenced by other variables not examined in this study. Usability variables have the highest correlation value which indicates that usability variables are the variables that most influence user satisfaction.
What is the relationship between marketing of library sources and services and social media? A literature review paper

Purpose The aim of this study is to determine the relationship between social media and libraries in terms of marketing. The present research is to find out the factors and impact as well as the issues related to libraries and marketing using social media. This study will give massive information regarding social media use for the marketing of library sources and services. Design/methodology/approach This study so far has been taken into account to review all the related literature of social media and library marketing. All the related literature studies were reviewed according to the suggested objectives of the study. Findings The present study reveals after reviewing literature that there is a great impact of social media on libraries. The literature shows that social media is the biggest change in the twenty-firstst century for library professionals and librarians. The reviewed literature studies have discovered that the libraries are facing too many challenges and issues to the adoption of social media for the marketing of library sources and services. The collected and analyzed works of the literature indicated that social media would be the best tool to promote library sources and services. Research limitations/implications This research or study can serve the LIS professionals and librarians in the field of library and information science and librarianships that they are looking for the adoption and application of social media in the marketing of library sources and services to the targeted customers. Practical implications This research or study can serve the LIS professionals and Librarians in the field of library and information science and librarianships that they are looking for the adoption and application of social media in the marketing of library sources and services to the targeted customers. Originality/value To the best of the authors’ knowledge, this study is one of the first among those researches that is to disclose the close relationship between social media and libraries in terms of marketing of library sources and services.
Real-Time Haze Removal Using Normalised Pixel-Wise Dark-Channel Prior and Robust Atmospheric-Light Estimation

This study proposes real-time haze removal from a single image using normalised pixel-wise dark-channel prior (DCP). DCP assumes that at least one RGB colour channel within most local patches in a haze-free image has a low-intensity value. Since the spatial resolution of the transmission map depends on the patch size and it loses the detailed structure with large patch sizes, original work refines the transmission map using an image-matting technique. However, it requires high computational cost and is not adequate for real-time application. To solve these problems, we use normalised pixel-wise haze estimation without losing the detailed structure of the transmission map. This study also proposes robust atmospheric-light estimation using a coarse-to-fine search strategy and down-sampled haze estimation for acceleration. Experiments with actual and simulated haze images showed that the proposed method achieves real-time results of visually and quantitatively acceptable quality compared with other conventional methods of haze removal.
Collaborative Learning: The Educational Perspective of Facebook

The present research evidences the practical experience of incorporation of the Facebook platform as a new educational scenario to analyze interventions and theatrical performances from different contexts at the global level, based on the synchronous and asynchronous interaction of students. In turn, this study demonstrates how the use of this social network in education can favor aspects such as management of knowledge and foster the development of skills training in students, at present this medium is used as a form of communication, leisure and socialization but its educational applications turn out to be very promising, these aspects generate a positive perception by the uses of this social network in the field of education, thus providing a new online learning system that can be replicated in various contexts of Peru and Latin America, helping to improve the academic performance of students, according to the new learning styles of this technological age. The results show that the educational incorporation of this social network has a high level of replication, in various fields and educational subjects, since it greatly favors collaborative work, to achieve the construction of new knowledge, generation of significant learning and the achievement of skills. In conclusion, this work is a contribution, which seeks to strengthen the levels of synchronous and asynchronous communication, based on interaction, to improve digital skills that allow educational socialization.
SHA-512 based Wireless Authentication Scheme for Smart Battery Management Systems

Modern cyber-physical systems depend on the battery backup systems for continuous service. Due to excessive wiring requirements, the traditional methods used for battery authentication cannot be applied to modern smart batteries, especially when they are composed of large number of cells. In this work, we proposed a wireless battery authentication scheme for use with the battery management system and the cells to prevent potential hardware attacks through the trojan cells. We designed a SHA-512 IP Core which operates at 90MHz, and occupies 976 CLB slices and one block RAM on a Xilinx Artix-7 FPGA device. We integrated the SHA-516 module with a synthesizable CPU to be able to create a real-world test scenario and finally we verified the correct operation of an example authentication protocol through a wireless communication channel established with the use of two ESP8266 based WiFi modules.
RETRACTED ARTICLE: Massive information management system of digital library based on deep learning algorithm in the background of big data

We, the Editors and Publisher of the journal Behaviour & Information Technology, have retracted the following article which was part of the Special Issue on Behaviour Monitoring and Management of Customers, People and Organizations using Deep Learning: Jianhang Wang (2020) Massive information management system of digital library based on deep learning algorithm in the background of big data, Behaviour & Information Technology, DOI: 10.1080/0144929X.2020.1730441After publication it came to our attention that the person named as the Guest Editor of the Special Issue was impersonated by a fraudulent entity and the articles were not reviewed fully in line with the journal’s peer review standards and policy. We did not find any evidence of misconduct by the authors. However, in order to ensure full assessment has been conducted, we sought expert advice on the validity and quality of the published articles from independent peer reviewers. Following this post publication peer review, the editor has determined that the articles do not meet the required scholarly standards to remain published in the journal, and therefore has taken the decision to retract the articles. The authors have been informed of this decision.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.