Reviewerly/exHarmony huggingface dataset:
Extracting Opinion Targets Using Attention-Based Neural Model

Extracting opinion-target expression is a core subtask to perform aspect-based sentiment analysis which aims to identify the discussed aspects within a text associated with their opinion targets and classify the sentiment as positive, negative, or neutral. This paper proposes a deep learning model to tackle the opinion-target expression extraction task. The proposed model is composed of bidirectional long short-term memory as an encoder, long short-term memory as a decoder with an attention mechanism, and conditional random fields. This model, which operates at the sentence level, is designed to extract opinion targets for the Arabic language. The proposed model’s performance is evaluated using SemEval-2016 annotated dataset for the hotels’ domain. Experimental results show that the proposed model outperforms the baseline and the prior works, where it achieved an F1 measure of 72.83%.
A new cure rate model with flexible competing causes with applications to melanoma and transplantation data

In this article, we introduce a long‐term survival model in which the number of competing causes of the event of interest follows the zero‐modified geometric (ZMG) distribution. Such distribution accommodates equidispersion, underdispersion, and overdispersion and captures deflation or inflation of zeros in the number of lesions or initiated cells after the treatment. The ZMG distribution is also an appropriate alternative for modeling clustered samples when the number of competing causes of the event of interest consists of two subpopulations, one containing only zeros (cure proportion), while in the other (noncure proportion) the number of competing causes of the event of interest follows a geometric distribution. The advantage of this assumption is that we can measure the cure proportion in the initiated cells. Furthermore, the proposed model can yield greater or lower cure proportion than that of the geometric distribution when modeling the number of competing causes. In this article, we present some statistical properties of the proposed model and use the maximum likelihood method to estimate the model parameters. We also conduct a Monte Carlo simulation study to evaluate the performance of the estimators. We present and discuss two applications using real‐world medical data to assess the practical usefulness of the proposed model.
Estimation of pure quantum states in high dimension at the limit of quantum accuracy through complex optimization and statistical inference

Abstract Quantum tomography has become a key tool for the assessment of quantum states, processes, and devices. This drives the search for tomographic methods that achieve greater accuracy. In the case of mixed states of a single 2-dimensional quantum system adaptive methods have been recently introduced that achieve the theoretical accuracy limit deduced by Hayashi and Gill and Massar. However, accurate estimation of higher-dimensional quantum states remains poorly understood. This is mainly due to the existence of incompatible observables, which makes multiparameter estimation difficult. Here we present an adaptive tomographic method and show through numerical simulations that, after a few iterations, it is asymptotically approaching the fundamental Gill–Massar lower bound for the estimation accuracy of pure quantum states in high dimension. The method is based on a combination of stochastic optimization on the field of the complex numbers and statistical inference, exceeds the accuracy of any mixed-state tomographic method, and can be demonstrated with current experimental capabilities. The proposed method may lead to new developments in quantum metrology.
Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation

Suspense is a crucial ingredient of narrative fiction, engaging readers and making stories compelling.While there is a vast theoretical literature on suspense, it is computationally not well understood.We compare two ways for modelling suspense: surprise, a backward-looking measure of how unexpected the current state is given the story so far; and uncertainty reduction, a forward-looking measure of how unexpected the continuation of the story is.Both can be computed either directly over story representations or over their probability distributions.We propose a hierarchical language model that encodes stories and computes surprise and uncertainty reduction.Evaluating against short stories annotated with human suspense judgements, we find that uncertainty reduction over representations is the best predictor, resulting in near human accuracy.We also show that uncertainty reduction can be used to predict suspenseful events in movie synopses.
Enhanced two-parameter phase-space-displacement estimation close to a dissipative phase transition

I propose a quantum sensor based on driven-dissipative quantum system for the joint estimation of two conjugated variables characterizing the phase space displacement. The quantum probe consists of lattice system with two level atoms and bosonic modes which interact via dipolar coupling. Interplay between the coherent dynamics and dissipative processes of losses of bosonic excitations leads to a steady state which exhibits a non-analytical behaviour. I show that close to the dissipative phase transition the sensitivity of one of the conjugated parameters either the magnitude of the phase of the displacement can be significantly enhanced. Moreover, I show that the sum of the measurement uncertainties of the two parameters can overcome the standard quantum limit.
Signal Recognition Based On Federated Learning

Signal modulation recognition is widely used in spectrum detection, channel estimation, and interference recognition, and is a prerequisite step for signal decoding and demodulation. With the development of artificial intelligence, great progress has been made in signal recognition using deep learning methods. However, the method of deep learning requires a large amount of data. Today, with more and more emphasis on data privacy and security protection, there are usually hard-to-break barriers between data sources. This makes the data limited and of poor quality, which is not enough to support deep learning training. Federated learning may be a feasible direction to solve this problem. In this article, we will discuss signal modulation recognition based on federated learning, and the results show that an acceptable recognition rate is achieved while satisfying privacy protection and data security.
Forensic Investigation of Google Assistant

Abstract Google Nest devices have seen a rise in demand especially with Google’s huge advantage in search engine results and a complex ecosystem that consists of a range of companion devices and compatible mobile applications integrated and interacting with its virtual assistant, Google Assistant. This study undertakes the forensics extraction and analysis of client-centric and cloud-native data remnants left behind on Android smartphones by the Google Home and Google Assistant apps used to control a Google Nest device. We identified the main database and file system storage location central to the Google Assistant ecosystem. From our analysis, we show forensic artifacts of interest associated with user account information, the chronology and copies of past voice conversations exchanged, and record of deleted data. The findings from this study describe forensic artifacts that could assist forensic investigators and can facilitate a criminal investigation.
Fake News (Hoax) Identification on Social Media Twitter using Decision Tree C4.5 Method

Social media is a means to communicate and exchange information between people, and Twitter is one of them. But the information disseminated is not entirely true, but there is some news that is not in accordance with the truth or often called hoaxes. There have been many cases of spreading hoaxes that cause concern and often harm a particular individual or group. So in this research, the authors build a system to identify hoax news on social media Twitter using the Decision Tree C4.5 classification method to the 50,610 tweet data. What distinguishes this research from some researches before is the existence of several test scenarios, classification only, classification using weighting feature, and also classification using weighting feature and feature selection. The weighting method used is TF-IDF, and the feature selection uses Information Gain. The features used are also generated using n-grams consisting of unigram, bigram, and also trigrams. The final results show that the classification test that uses weighting feature and feature selection produces the best accuracy of 72.91% with a ratio of 90% training data and 10% test data (90:10) and the number of features used is 5000 in unigram features.&#x0D;
El área de información y alfabetización informacional de la competencia digital docente

Information and Communication Technologies (ICT) are an integral part of our lives. This fact is reflected in the educational system, where teachers must have acquired a series of digital skills that should be developed in students. The study tries to describe the area of digital competence, information, and information literacy in the students of the Master’s Degree in Compulsory Secondary Education, Baccalaureate, Vocational Training, and Language Teaching in the Campus of Ceuta. A quantitative method has been used; it has a descriptive scope and a non-experimental design of a longitudinal paradigm. The sample is 153 students. The instrument implemented is a questionnaire based on the Common Framework of Digital Teacher Competence of 2017, with a Likert scale of 6 points, and subdivided into three blocks: A (A1 and A2), considered as a basic level, B (B1 and B2), as an intermediate level, and C (C1 and C2), as an advanced level. The results show that future teachers have a medium level in the area of information competence and information literacy, presenting higher values in the storage and retrieval of information, data, and digital content. They also have average levels in the storage and retrieval of information, data and digital content, and lower levels in evaluating information, data, and digital content. We can conclude that training centered on developing digital teaching competencies is necessary, requiring, on the part of the competent authorities, adequate training plans.
Environmental disclosure and sentiment analysis: state of the art and opportunities for public-sector organisations

Purpose Because of the expansion of the internet and Web 2.0 phenomenon, new challenges are emerging in the disclosure practises adopted by organisations in the public-sector. This study aims to examine local governments’ (LGOs) use of social media (SM) in disclosing environmental actions/plans/information as a new way to improve accountability to citizens to obtain organisational legitimacy and the related sentiment of citizens’ judgements. Design/methodology/approach This paper analyses the content of 39 Italian LGOs’ public pages on Facebook. After the distinction between five classes of environmental issues (air, water, energy, waste and territory), an initial study is performed to detect possible sub-topics applying latent Dirichlet allocation. Having a list of posts related to specific environmental themes, the researchers computed the sentiment of citizens’ comments. To measure sentiment, two different approaches were implemented: one based on a lexicon dictionary and the other based on convolutional neural networks. Findings Facebook is used by LGOs to disclose environmental issues, focussing on their main interest in obtaining organisational legitimacy, and the analysis shows an increasing impact of Web 2.0 in the direct interaction of LGOs with citizens. On the other hand, there is a clear divergence of interest on environmental topics between LGOs and citizens in a dialogic accountability framework. Practical implications Sentiment analysis (SA) could be used by politicians, but also by managers/entrepreneurs in the business sector, to analyse stakeholders’ judgements of their communications/actions and plans on corporate social responsibility. This tool gives a result on time (i.e. not months or years after, as for the reporting system). It is cheaper than a survey and allows a first “photograph” of stakeholders’ sentiment. It can also be a useful tool for supporting, developing and improving environmental reporting. Originality/value To the best of the authors’ knowledge, this paper is one of the first to apply SA to environmental disclosure via SM in the public sphere. The study links modern techniques in natural language processing and machine learning with the important aspects of environmental communication between LGOs and citizens.
Issues of modeling smart personality – human image of the digital age

Digitalization, human immersion in the Internet space, Smart technologies create a world of new materiality, embrace everyday life, thinking, expanding the boundaries and possibilities of human communication and self-expression, which was the hypothetical basis for the idea of the need to transform lives in the direction of Smart. This circumstance necessitates modelling the image of a person in the digital era, taking into account the models of human action and its results developed by the social and humanitarian sciences. The study is based on conceptual, system-functional approaches, and it uses a set of general scientific and specific scientific methods, the modelling method served as a way to create an image (model) of a Smart personality. Research results: the concept of “Smart personality” has been introduced into scientific circulation, highlighting such competencies as possession of methods of critical thinking, effective teamwork and interaction with other people, adaptation to changes, decision-making and independent organization of activities, work in conditions of uncertainty and performing complex analytical tasks that require improvisation and creativity; the factors of development and formation of the Smart personality were identified: implementation of the concept of Smart-education and values of self-development, approval of the concept of “Lifelong learning education” and self-motivation, development of cognitive skills instead of “loading” knowledge; it is substantiated that the development of a Smart personality model is possible with an interdisciplinary approach, using the methodology of different areas of socio-humanitarian knowledge
SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home

As aging populations continue to grow, primarily in developed countries, there are increasing demands for the system that monitors the activities of elderly people while continuing to allow them to pursue their individual, healthy, and independent lifestyles. Therefore, it is required to develop the activity of daily living (ADL) sensing systems that are based on high-performance sensors and information technologies. However, most of the systems that have been proposed to date have only been investigated and/or evaluated in experimental environments. When considering the spread of such systems to typical homes inhabited by elderly people, it is clear that such sensing systems will need to meet the following five requirements: (1) be inexpensive; (2) provide robustness; (3) protect privacy; (4) be maintenance-free; and, (5) work with a simple user interface. In this paper, we propose a novel senior-friendly ADL sensing system that can fulfill these requirements. More specifically, we achieve an easy collection of ADL data from elderly people while using a proposed system that consists of a small number of inexpensive energy harvesting sensors and simple annotation buttons, without the need for privacy-invasive cameras or microphones. In order to evaluate the practicality of our proposed system, we installed it in ten typical homes with elderly residents and collected the ADL data over a two-month period. We then visualized the collected data and performed activity recognition using a long short-term memory (LSTM) model. From the collected results, we confirmed that our proposed system, which is inexpensive and non-invasive, can correctly collect resident ADL data and could recognize activities from the collected data with a high recall rate of 72.3% on average. This result shows a high potential of our proposed system for application to services for elderly people.
Towards Memorable Information Retrieval

Information overload is a problem many of us can relate to nowadays. The deluge of user generated content on the Internet, and the easy accessibility to a vast amount of data compounds the problem of remembering and retaining information that is consumed. To make information consumed more memorable, strategies such as note-taking have been found to be effective by augmenting human memory under specific conditions. This is based on the rationale that humans tend to recall information better if they have produced the information themselves. Previous works in online education have shown that conversational systems can improve learning effects. Although memorization is an important part of learning, the effect of conversation on human memorability remains unexplored. We aim to address this knowledge gap through an experimental study, by investigating human memorability in a classical information retrieval setup. We explore the impact of note-taking affordances and conversational interfaces on the memorability of information consumed by users. Our results show that traditional web search and note-taking have positive effects on knowledge gain, while the search engine with a conversational interface has the potential to augment long-term memorability. This work highlights the benefits of using note-taking and conversational interfaces to aid human memorability. Our findings have important implications on building information retrieval systems that cater to optimizing memorability of information consumed.
Blockchains for Transactive Energy Systems: Opportunities, Challenges, and Approaches

The emergence of blockchains and smart contracts has renewed interest in electrical cyberphysical systems, especially transactive energy systems. To address the associated challenges, we present TRANSAX, a blockchain-based transactive energy system that provides an efficient, safe, and privacy-preserving market built on smart contracts.
A real-world information security performance assessment using a multidimensional socio-technical approach

Measuring the performance of information security is an essential part of the information security management system within organisations. Studies in the past mainly focused on establishing qualitative measurement approaches. Since these can lead to ambiguous conclusions, quantitative metrics are being increasingly proposed as a useful alternative. Nevertheless, the literature on quantitative approaches remains scarce. Thus, studies on the evaluation of information security performance are challenging, especially since many approaches are not tested in organisational settings. The paper aims to validate the model used for evaluating the performance of information security management system through a multidimensional socio-technical approach, in a real-world settings among medium-sized enterprises in Slovenia. The results indicate that information security is strategically defined and compliant, however, measures are primarily implemented at technical and operational levels, while its strategic management remains underdeveloped. We found that the biggest issues are related to information resources and risk management, where information security measurement-related activities proved to be particularly problematic. Even though enterprises do possess certain information security capabilities and are aware of the importance of information security, their current practices make it difficult for them to keep up with the fast-paced technological and security trends.
Lessons Learned From the Training of GANs on Artificial Datasets

Generative Adversarial Networks (GANs) have made great progress in synthesizing realistic images in recent years. However, they are often trained on image datasets with either too few samples or too many classes belonging to different data distributions. Consequently, GANs are prone to underfitting or overfitting, making the analysis of them difficult and constrained. Therefore, in order to conduct a thorough study on GANs while obviating unnecessary interferences introduced by the datasets, we train them on artificial datasets where there are infinitely many samples and the real data distributions are simple, high-dimensional and have structured manifolds. Moreover, the generators are designed such that optimal sets of parameters exist. Empirically, we find that under various distance measures, the generator fails to learn such parameters with the GAN training procedure. In addition, we confirm that using mixtures of GANs is more beneficial than increasing the network depth or width when the model complexity is high enough. The benefit is partially due to the division of the generation and discrimination tasks across multiple generators and discriminators. We find that a mixture of generators can discover different modes or different classes automatically in the unsupervised setting. As an example of the generalizability of our conclusions to realistic datasets, we train a mixture of GANs on the CIFAR-10 dataset and our method significantly outperforms the state-of-the-art in terms of popular metrics, i.e., Inception Score (IS) and Fréchet Inception Distance (FID).
CAPTCHA Recognition Using Deep Learning with Attached Binary Images

Websites can increase their security and prevent harmful Internet attacks by providing CAPTCHA verification for determining whether end-user is a human or a robot. Text-based CAPTCHA is the most common and designed to be easily recognized by humans and difficult to identify by machines or robots. However, with the dramatic advancements in deep learning, it becomes much easier to build convolutional neural network (CNN) models that can efficiently recognize text-based CAPTCHAs. In this study, we introduce an efficient CNN model that uses attached binary images to recognize CAPTCHAs. By making a specific number of copies of the input CAPTCHA image equal to the number of characters in that input CAPTCHA image and attaching distinct binary images to each copy, we build a new CNN model that can recognize CAPTCHAs effectively. The model has a simple structure and small storage size and does not require the segmentation of CAPTCHAs into individual characters. After training and testing the proposed CAPTCHA recognition CNN model, the achieved experimental results reveal the strength of the model in CAPTCHA character recognition.
On the Estimation of Synchronous Scramblers in Direct Sequence Spread Spectrum Systems

In a non-cooperative context, the receiver has no information about communication parameters; therefore, it must perform communication forensics, which is the process of identifying what information it can from the collected data.This paper proposes a novel method for parameter estimation of synchronous scramblers in direct sequence spread spectrum systems.For the estimation, we use the bitwise relations inherent in the scrambling sequence and the repetitive patterns by the spreading code inherent in the scrambled sequence.Regarding computational complexity, previous studies of parameter estimation of synchronous scramblers require exponential computational complexities.Unlike the existing methods, our proposed method can practically estimate the feedback polynomial and initial state of the synchronous scrambler with polynomial computational complexity.