Reviewerly/exHarmony huggingface dataset:
A Study of Integrative Bargaining Model with Argumentation-Based Negotiation

E-commerce is increasingly competitive and there is a constant need for new approaches and technology to facilitate exchange. Emerging techniques include the use of artificial intelligence (AI). One AI tool that has sparked interest in e-commerce is the automated negotiation agent (negotiation-agent). This study examines such agents, and proposes an offer strategy model of integrative negotiation for a negotiation-agent with a focus on negotiation agent-to-human interaction. More specifically, a new offer strategy was developed based on the integrative bargaining model, which emphasizes the importance of exchanging information among negotiators and multi-issue negotiation that includes package offers to achieve an integrative (win-win) outcome. This study incorporated an argumentation-based negotiation and the negotiation tactic of multiple equivalent simultaneous offers, which was programmed into the negotiation-agent. An experiment was conducted performing 49 negotiation-agent-to-human negotiations over three issues in online purchase tasks to demonstrate the effectiveness of the proposed strategy. Experimental results indicated that the proposed offer strategy with agent negotiation can enhance the persuasiveness of an offer and the performance of negotiation outcome (human counterpart’s perception toward negotiation process, opponent–agent and desire for future negotiation). The findings confirmed the effectiveness of the proposed design and demonstrated an innovative approach to e-commerce transactions.
Conceptual Approach to Building a Digital Twin of the Production System

The digital twin is an important component of the cyber-physical system. This new structure of the production system was the result of the development of information technology. The article shows that, despite the long history and success in the development of production information systems, the concept of building digital twins of production systems is at an early stage. One of the problems in creating digital twins is the need for integration and joint processing of a large amount of heterogeneous information. It is shown that the problem of reflecting the current state of the production system is in many ways similar to the problem of the internal representation of the surrounding world in living systems. It is proposed to choose the theory of the levels of the physiologist N. A. Bernstein as the basis of the conceptual approach to the development of digital twins. The mechanisms of forming models of the external world at every level are outlined. A description of the hierarchical system for processing different types of information and obtaining an invariant representation of the external world are presented. The principles of constructing a virtual image in the organization of motor activity are formulated. The implementation of these principles when building a digital twin of the production process will improve the efficiency of integration methods and joint processing of information.
PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis

With the current upsurge in the usage of social media platforms, the trend of using short text (microtext) in place of text with standard words has seen a significant rise. The usage of microtext poses a considerable performance issue to sentiment analysis, since models are trained on standard words. This paper discusses the impact of coupling sub-symbolic (phonetics) with symbolic (machine learning) Artificial Intelligence to transform the out-of-vocabulary (OOV) concepts into their standard in-vocabulary (IV) form. We develop binary classifier to detect OOV sentences and then they are transformed to phoneme subspace using grapheme to phoneme converter. We compare the phonetic and string distance using the Sorensen similarity algorithm. The phonetically similar IV concepts thus obtained are then used to compute the correct polarity value, which was previously being miscalculated because of the presence of microtext. Our proposed framework improves the accuracy of polarity detection by 6% as compared to the earlier model. In conclusion, we apply a grapheme to phoneme converter for microtext normalization and show its application on sentiment analysis.
Best practices for conducting fieldwork with marginalized communities

Fieldwork is indispensable for understanding, explaining, and predicting the role of information and communication technologies for development (ICT4D) of marginalized communities. Engaging with marginalized communities is at the heart of ICT4D fieldwork. However, vulnerabilities of marginalized communities can prevent them from participating in fieldwork. The goal of this paper is to report best practices for engaging with marginalized communities. The findings are grounded in our fieldwork that consisted of ten three-hour sessions, each including focus groups, surveys, and hands-on exercises, with 152 participants earning less than USD 2 a day, in their native language at ten rural and urban public libraries in India. We conclude that a combination of proactive planning around the vulnerabilities of marginalized communities, constant reflective monitoring of the vulnerabilities and resulting challenges during fieldwork, and appropriate responsive action to address the challenges is one of the best ways of conducting fieldwork, since it helped us manage a majority of the challenges resulting from geographic, temporal, technological, financial, educational, psychological, informational, infrastructural, social, and cultural vulnerabilities of the participants. Our organic and structured guidance in the form of lessons learned can help library and information science researchers and practitioners to customize fieldwork around the vulnerabilities of marginalized communities.
Multicarrier continuous-variable quantum key distribution

The multicarrier continuous-variable quantum key distribution (CVQKD) protocol is defined. In a CVQKD protocol, the information is conveyed by coherent quantum states. The quantum continuous variables are sent through a noisy quantum channel. For a quantum channel with additive-multiplicative noise both additive and multiplicative disturbances are present in the transmission. The multiplicative disturbance is an inherent attribute of diverse physical environments. Physical links with additive and multiplicative disturbances can represent a more general approach than purely additive noise links in several practical scenarios. In a standard CVQKD setting, the noise is modeled as an additive white Gaussian noise caused by an eavesdropper (Gaussian quantum link). As a corollary, standard CVQKD protocols are not optimal for arbitrary Gaussian quantum channels if multiplicative disturbances are also present in the physical link. Here, we define the adaptive multicarrier quadrature division (AMQD) modulation technique for CVQKD. The AMQD method is optimal for arbitrary Gaussian quantum channels with arbitrary multiplicative disturbances. The protocol granulates the Gaussian random input into Gaussian subcarrier continuous variables in the encoding phase, which are then decoded by a continuous unitary transformation. The subcarrier coherent variables formulate sub-channels from the physical link which leads to improved transmission efficiency, higher tolerable loss, and excess noise in comparison to standard CVQKD protocols. We also derive the security proof of multicarrier CVQKD at optimal Gaussian attacks in the finite-size and asymptotic regimes.
Early Detecting In-Hospital Cardiac Arrest Based on Machine Learning on Imbalanced Data

In-hospital cardiac arrest (IHCA) diminish the survival rate of patients, despite most of the IHCA cases are preventable. More than 54% IHCA patient had abnormal clinical manifestation before they suffered a cardiac arrest. If appropriate steps were taken, patients' survival rate would be higher and medical expense would be decreased. This paper proposes a novel approach to detect IHCA before the event occurred. We construct two types of shifting windows (corresponding to two tasks) that allow machine learning to be applied for our dataset which is severely imbalanced. The results show that our approach can effectively handle the imbalanced dataset for detecting cardiac arrest. As the selection of performance index, we used the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). In our experiments, the best classifier is random forest for task 1, with AUROC of 0.88. LSTM is the best for task 2, with AUPRC of 0.71 for the second task.
Protecting patient privacy in survival analyses

Survival analysis is the cornerstone of many healthcare applications in which the "survival" probability (eg, time free from a certain disease, time to death) of a group of patients is computed to guide clinical decisions. It is widely used in biomedical research and healthcare applications. However, frequent sharing of exact survival curves may reveal information about the individual patients, as an adversary may infer the presence of a person of interest as a participant of a study or of a particular group. Therefore, it is imperative to develop methods to protect patient privacy in survival analysis.We develop a framework based on the formal model of differential privacy, which provides provable privacy protection against a knowledgeable adversary. We show the performance of privacy-protecting solutions for the widely used Kaplan-Meier nonparametric survival model.We empirically evaluated the usefulness of our privacy-protecting framework and the reduced privacy risk for a popular epidemiology dataset and a synthetic dataset. Results show that our methods significantly reduce the privacy risk when compared with their nonprivate counterparts, while retaining the utility of the survival curves.The proposed framework demonstrates the feasibility of conducting privacy-protecting survival analyses. We discuss future research directions to further enhance the usefulness of our proposed solutions in biomedical research applications.The results suggest that our proposed privacy-protection methods provide strong privacy protections while preserving the usefulness of survival analyses.
Towards natural interaction in immersive reality with a cyber-glove

Over the past few years, virtual and mixed reality systems have evolved significantly yielding high immersive experiences. Most of the metaphors used for interaction with the virtual environment do not provide the same meaningful feedback, to which the users are used to in the real world. This paper proposes a cyber-glove to improve the immersive sensation and the degree of embodiment in virtual and mixed reality interaction tasks. In particular, we are proposing a cyber-glove system that tracks wrist movements, hand orientation and finger movements. It provides a decoupled position of the wrist and hand, which can contribute to a better embodiment in interaction and manipulation tasks. Additionally, the detection of the curvature of the fingers aims to improve the proprioceptive perception of the grasping/releasing gestures more consistent to visual feedback. The cyber-glove system is being developed for VR applications related to real estate promotion, where users have to go through divisions of the house and interact with objects and furniture. This work aims to assess if glove-based systems can contribute to a higher sense of immersion, embodiment and usability when compared to standard VR hand controller devices (typically button-based). Twenty-two participants tested the cyber-glove system against the HTC Vive controller in a 3D manipulation task, specifically the opening of a virtual door. Metric results showed that 83% of the users performed faster door pushes, and described shorter paths with their hands wearing the cyber-glove. Subjective results showed that all participants rated the cyber-glove based interactions as equally or more natural, and 90% of users experienced an equal or a significant increase in the sense of embodiment.
Looking at Hands in Autonomous Vehicles: A ConvNet Approach Using Part Affinity Fields

In the context of autonomous driving, where humans may need to take over in the event where the computer may issue a takeover request, a key step towards driving safety is the monitoring of the hands to ensure the driver is ready for such a request. This work, focuses on the first step of this process, which is to locate the hands. Such a system must work in real-time and under varying harsh lighting conditions. This article introduces a fast ConvNet approach, based on the work of original work of OpenPose by Cao, et al. for full body joint estimation. The network is modified with fewer parameters and retrained using our own day-time naturalistic autonomous driving dataset to estimate joint and affinity heatmaps for driver and passenger's wrist and elbows, for a total of 8 joint classes and part affinity fields between each wrist-elbow pair. The approach runs real-time on real-world data at 40 fps on multiple drivers and passengers. The system is extensively evaluated both quantitatively and qualitatively, showing at least 95% detection performance on joint localization and arm-angle estimation.
The ability of forecasting flapping frequency of flexible filament by artificial neural network

Artificial Neural Networks (ANNs) are reliable and computationally inexpensive compared to numerical methods such as CFD simulations and experimental investigations in aerodynamics research. In this article, an Artificial Neural Network (ANN) has been introduced to predict the flapping frequencies of a filament placed in a 2-D soap-film tunnel. The multi-layer perception (MLP) networks have been used in developing the Artificial Neural Network while the backpropagation Levenberg-Marquardt algorithm was used to perform the training of the ANN. A part of the experimental data was considered for the training process while the rest for the prediction test of the suggested ANN. The ANN results indicate that it can predict the frequencies of the periodic flapping with good accuracy. However, it fails when the flapping presents amplitude modulation.
An LLL Algorithm for Module Lattices

The LLL algorithm takes as input a basis of a Euclidean lattice, and, within a polynomial number of operations, it outputs another basis of the same lattice but consisting of rather short vectors. We provide a generalization to R-modules contained in $$K^n$$ for arbitrary number fields K and dimension n, with R denoting the ring of integers of K. Concretely, we introduce an algorithm that efficiently finds short vectors in rank-n modules when given access to an oracle that finds short vectors in rank-2 modules, and an algorithm that efficiently finds short vectors in rank-2 modules given access to a Closest Vector Problem oracle for a lattice that depends only on K. The second algorithm relies on quantum computations and its analysis is heuristic.
Detecting bursty terms in computer science research

Abstract Research topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts ; for example “deep learning”, “internet of things” and “big data”. Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate.
Quantum Multi-User Broadcast Protocol for the “Platform as a Service” Model

Quantum Cloud Computing is the technology which has the capability to shape the future of computing. In "Platform as a Service (PaaS)" type of cloud computing, the development environment is delivered as a service. In this paper, a multi-user broadcast protocol in network is developed with the mode of one master and N slaves together with a sequence of single photons. It can be applied to a multi-node network, in which a single photon sequence can be sent to all the slave nodes simultaneously. In broadcast communication networks, these single photons encode classical information directly through noisy quantum communication channels. The results show that this protocol can realize the secret key generation and sharing of multiple nodes. The protocol we propose is also proved to be unconditionally secure in theory, which indicates its feasibility in theoretical application.
Quantum Algorithms for the Approximate k-List Problem and Their Application to Lattice Sieving

The Shortest Vector Problem (SVP) is one of the mathematical foundations of lattice based cryptography. Lattice sieve algorithms are amongst the foremost methods of solving SVP. The asymptotically fastest known classical and quantum sieves solve SVP in a d-dimensional lattice in $$2^{\mathsf {c}d + o(d)}$$ time steps with $$2^{\mathsf {c}' d + o(d)}$$ memory for constants $$c, c'$$ . In this work, we give various quantum sieving algorithms that trade computational steps for memory. We first give a quantum analogue of the classical k-Sieve algorithm [Herold–Kirshanova–Laarhoven, PKC'18] in the Quantum Random Access Memory (QRAM) model, achieving an algorithm that heuristically solves SVP in $$2^{0.2989d + o(d)}$$ time steps using $$2^{0.1395d + o(d)}$$ memory. This should be compared to the state-of-the-art algorithm [Laarhoven, Ph.D Thesis, 2015] which, in the same model, solves SVP in $$2^{0.2653d + o(d)}$$ time steps and memory. In the QRAM model these algorithms can be implemented using $$\mathrm {poly}(d)$$ width quantum circuits. Secondly, we frame the k-Sieve as the problem of k-clique listing in a graph and apply quantum k-clique finding techniques to the k-Sieve. Finally, we explore the large quantum memory regime by adapting parallel quantum search [Beals et al., Proc. Roy. Soc. A'13] to the 2-Sieve, and give an analysis in the quantum circuit model. We show how to solve SVP in $$2^{0.1037d + o(d)}$$ time steps using $$2^{0.2075d + o(d)}$$ quantum memory.
Manipulating Attributes of Natural Scenes via Hallucination

In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network that can hallucinate images of a scene as if they were taken in a different season (e.g., during winter), weather condition (e.g., on a cloudy day), or at a different time of the day (e.g., at sunset). Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic details intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches. Moreover, it allows to simultaneously manipulate a given scene according to a diverse set of transient attributes within a single model, eliminating the need of training multiple networks per each translation task. Our comprehensive set of qualitative and quantitative results demonstrates the effectiveness of our approach against the competing methods.
The selection of Calcium Milk Products that are appropriate for advanced age using PROMETHEE II Algorithm

Abstract The selection of Calcium Dairy Products that are suitable for old age uses the PROMETHEE II Algorithm. This study aims to recommend calcium milk products that are appropriate for elderly people based on consumer selection. This research was conducted in Simalungun district using interview techniques, observation and questionnaires to 350 elderly respondents randomly. This study also uses a quantitative data approach, which is testing four calcium milk products (alternatives) that are considered appropriate for use in old age. The four dairy products are widely used for the elderly, namely: Anlene Gold (A1), Entrasol (A2), Prolansia (A3) and Appeton 60+ (A4). The assessment criteria used in the selection of calcium milk recommendations appropriate for old age are: price (C1), content (C2), side effects (C3) and taste variants (A4). The solution used is to use a decision support system with PROMETHEE II algorithm. The results of PROMETHEE II calculation show that Anlene (A1) is obtained as the first recommendation of the right calcium milk with a value (net flow 0.5) and Appeton 60+ (A4) as the second recommendation with a value (net flow 0.484).
Gaussian Process Modeling of Heterogeneity and Discontinuities Using Voronoi Tessellations

Abstract Many methods for modeling functions over high-dimensional spaces assume global smoothness properties; such assumptions are often violated in practice. We introduce a method for modeling functions that display heterogeneity or contain discontinuities. The heterogeneity is dealt with by using a combination of Voronoi tessellation, to partition the input space, and separate Gaussian processes to model the function over different regions of the partitioned space. The proposed method is highly flexible since it allows the Voronoi cells to combine to form regions, which enables nonconvex and disconnected regions to be considered. In such problems, identifying the borders between regions is often of great importance and we propose an adaptive sampling method to gain extra information along such borders. The method is illustrated by simulated examples and an application to real data, in which we see improvements in prediction error over the commonly used stationary Gaussian process and other nonstationary variations. In our application, a computationally expensive computer model that simulates the formation of clouds is investigated, the proposed method more accurately predicts the underlying process at unobserved locations than existing emulation methods. Supplementary materials for this article are available online.
The Evaluation of Word Embedding Models and Deep Learning Algorithms for Turkish Text Classification

The use of word embedding models and deep learning algorithms are currently the most common and popular trends to enhance the overall performance of a text classification/categorization system. Word embedding models are vectors that provide a mapping of words with similar meaning to own a similar representation which is learned from a corpus. Deep learning algorithms successful produce more successful results in many areas of their applications when they are compared to the conventional machine learning algorithms. In this study, three different word embedding models Word2Vec, Glove, and FastText are employed for word representation. Instead of using conventional classification algorithms, three different deep learning architectures Recurrent Neural Networks (RNN), Long Short Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN) are used for classification task by performing experiments on collections of different Turkish documents. Experimental results show that the usage of deep learning algorithms together with word embedding models advances the performance of text classification systems.