Reviewerly/exHarmony huggingface dataset:
Bayesian estimation‐based sentiment word embedding model for sentiment analysis

Sentiment word embedding has been extensively studied and used in sentiment analysis tasks. However, most existing models have failed to differentiate high-frequency and low-frequency words. Accordingly, the sentiment information of low-frequency words is insufficiently captured, thus resulting in inaccurate sentiment word embedding and degradation of overall performance of sentiment analysis. A Bayesian estimation-based sentiment word embedding (BESWE) model, which aims to precisely extract the sentiment information of low-frequency words, has been proposed. In the model, a Bayesian estimator is constructed based on the co-occurrence probabilities and sentiment probabilities of words, and a novel loss function is defined for sentiment word embedding learning. The experimental results based on the sentiment lexicons and Movie Review dataset show that BESWE outperforms many state-of-the-art methods, for example, C&W, CBOW, GloVe, SE-HyRank and DLJT1, in sentiment analysis tasks, which demonstrate that Bayesian estimation can effectively capture the sentiment information of low-frequency words and integrate the sentiment information into the word embedding through the loss function. In addition, replacing the embedding of low-frequency words in the state-of-the-art methods with BESWE can significantly improve the performance of those methods in sentiment analysis tasks.
Thirty Years of Translational Research Behind MED-EL

(2021). Thirty Years of Translational Research Behind MED-EL. Acta Oto-Laryngologica: Vol. 141, Thirty Years of Translational Research Behind MED-EL, pp. (i)-(cxcvi).
INTERDISCIPLINARY APPROACH TO THE DEVELOPMENT OF IB RISK MANAGEMENT SKILLS ON THE BASIS OF DECISION-MAKING THEORY

This article is devoted to the problem of readiness of students majoring in 125 Cybersecurity at Borys Hrinchenko Kyiv University to manage information security risks (IS) based on the decision-making theory. The interdisciplinary approach in education, namely, the integration of the disciplines "Risk Theory" and "Decision Theory", allowed to implement in the educational process the formation of practical skills of risk management of future cybersecurity professionals. Based on the achievements of didactics and psychological theories, the analysis of concepts in the field of interdisciplinary methodology is carried out and the relevance and significance of its introduction into the educational process of institution of higher education is substantiated. The peculiarities of the organization of the educational process of training bachelors of information and cyber security in the context of an interdisciplinary approach are described. The elements of the methodology of formation of practical skills of students to make managerial decisions in the conditions of risk on the basis of interdisciplinary principles are developed, theoretically presented and substantiated. It is proved that interdisciplinary exchange, integration of theoretical knowledge of disciplines contribute to new fundamental results, create preconditions for the development of practical skills, provide a holistic image of training future specialists in information and cyber security.
Widening the sharpness modulation region of an entanglement-assisted sequential quantum random access code: Theory, experiment, and application

The authors provide a method to widen the sharpness modulation region of sequential entanglement-assisted quantum random access code and show its application for sequential device independent randomness expansion.
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix

Continual learning (CL) is a setting in which an agent has to learn from an incoming stream of data during its entire lifetime. Although major advances have been made in the field, one recurring problem which remains unsolved is that of Catastrophic Forgetting (CF). While the issue has been extensively studied empirically, little attention has been paid from a theoretical angle. In this paper, we show that the impact of CF increases as two tasks increasingly align. We introduce a measure of task similarity called the NTK overlap matrix which is at the core of CF. We analyze common projected gradient algorithms and demonstrate how they mitigate forgetting. Then, we propose a variant of Orthogonal Gradient Descent (OGD) which leverages structure of the data through Principal Component Analysis (PCA). Experiments support our theoretical findings and show how our method can help reduce CF on classical CL datasets.
Online-adaptive classification and regression network with sample-efficient meta learning for long-term tracking

Classification and regression-based trackers (CAR) are widely adopted to tackle the short-term visual tracking task. However, the existing CAR tackers either employ offline-trained regression models based on predefined anchor-boxes, or online update their models in a rough and inflexible way, which leads to the lack of long-term adaptability for target deformations and appearance variations. To overcome this limitation, we propose a novel long-term tracking framework LT-CAR utilizing sample-efficient meta learning to online optimize both the classification and regression model. Specifically, we first introduce the ridge regression to a fully convolutional network as our regression branch, and then implement a vertically stacked GRU module termed as Meta-Sample-Filter to keep historical information about the target as well as help our model learn what to learn. Moreover, we extend our framework for long-term tracking by introducing a carefully designed spatial–temporal verification network to identify tracking failures, and a query-guided detector to conduct global re-detection. Experimental results on LaSOT, VOT-LT2018, VOT-LT2019, and TLP benchmarks show that our LT-CAR achieves comparable performance to the state-of-the-art long-term algorithms.
Optimization of morpheme length: a cross-linguistic assessment of Zipf’s and Menzerath’s laws

Abstract Zipf’s Law of Abbreviation and Menzerath’s Law both make predictions about the length of linguistic units, based on corpus frequency and the length of the carrier unit. Each contributes to the efficiency of languages: for Zipf, units are more likely to be reduced when they are highly predictable, due to their frequency; for Menzerath, units are more likely to be reduced when there are more sub-units to contribute to the structural information of the carrier unit. However, it remains unclear how the two laws work together in determining unit length at a given level of linguistic structure. We examine this question regarding the length of morphemes in spoken corpora of nine typologically diverse languages drawn from the DoReCo corpus, showing that Zipf’s Law is a stronger predictor, but that the two laws interact with one another. We also explore how this is affected by specific typological characteristics, such as morphological complexity.
Mobile Devices Applied in Self-Studying English as a Foreign Language Among Non-Native Students in Vietnam and Japan

&lt;p class="Affiliation"&gt;&lt;strong&gt;Abstract – &lt;/strong&gt;In the era of technology, learning can be supported with mobile devices inside and outside of the classroom. In the hope of taking advantage of technology advances, teachers worldwide have integrated technologies into teaching to effectively deliver lesson content and address students’ needs. However, few non-native learners are exposed to mobile devices to optimize self-studying English outside the classroom. Thus, the present paper conducts a case study to look into mobile devices’ applications to enhance self-studying English as a foreign language. The study participants included 26 Japanese and Vietnamese learners divided into two groups over 15 weeks. Quantitative and qualitative methods were used to analyze and compare the results with R-Studio statistics software. The present study sought to encourage collaboration and foster the autonomy of learners with mobile devices’ assistance as supporting language self-learning tools. The findings of the present research will be discussed in terms of the efficiency and potentials of mobile devices as supportive tools outside of classroom activities among Vietnamese and Japanese students as non-native learners of English.&lt;/p&gt;
Generation and Frame Characteristics of Predefined Evenly-Distributed Class Centroids for Pattern Classification

Predefined evenly-distributed class centroids (PEDCC) can be widely used in models and algorithms of pattern classification, such as CNN classifiers, classification autoencoders, clustering, and semi-supervised learning, etc. Its basic idea is to predefine the class centers, which are evenly-distributed on the unit hypersphere in feature space, to maximize the inter-class distance. The previous method of generating PEDCC uses an iterative algorithm based on a charge model. The generated class centers will have some errors with the theoretically evenly-distributed points, and the generation time is long. This paper takes advantage of regular polyhedron in high-dimensional space and the evenly distributed points on the n dimensional hypersphere to generate PEDCC mathematically. Then, we discussed the basic and extended characteristics of the frames formed by PEDCC, and some meaningful conclusions are obtained. Finally, the effectiveness of the new algorithm and related conclusions are proved by experiments. The mathematical analysis and experimental results of this paper can provide a theoretical tool for using PEDCC to solve the key problems in the field of pattern recognition, such as interpretable supervised/unsupervised learning, incremental learning, uncertainty analysis and so on.
Spectral Distribution Aware Image Generation

Recent advances in deep generative models for photo-realistic images have led to high quality visual results. Such models learn to generate data from a given training distribution such that generated images can not be easily distinguished from real images by the human eye. Yet, recent work on the detection of such fake images pointed out that they are actually easily distinguishable by artifacts in their frequency spectra. In this paper, we propose to generate images according to the frequency distribution of the real data by employing a spectral discriminator. The proposed discriminator is lightweight, modular and works stably with different commonly used GAN losses. We show that the resulting models can better generate images with realistic frequency spectra, which are thus harder to detect by this cue.
H2O: Secure Interactions in IoT via Behavioral Fingerprinting

Sharing data and services in the Internet of Things (IoT) can give rise to significant security concerns with information being sensitive and vulnerable to attacks. In such an environment, objects can be either public resources or owned by humans. For this reason, the need of monitoring the reliability of all involved actors, both persons and smart objects, assuring that they really are who they claim to be, is becoming an essential property of the IoT, with the increase in the pervasive adoption of such a paradigm. In this paper, we tackle this problem by proposing a new framework, called H2O (Human to Object). Our solution is able to continuously authenticate an entity in the network, providing a reliability assessment mechanism based on behavioral fingerprinting. A detailed security analysis evaluates the robustness of the proposed protocol; furthermore, a performance analysis shows the feasibility of our approach.
NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data

The field of neuroimaging can greatly benefit from building machine learning models to detect and predict diseases, and discover novel biomarkers, but much of the data collected at various organizations and research centers is unable to be shared due to privacy or regulatory concerns (especially for clinical data or rare disorders). In addition, aggregating data across multiple large studies results in a huge amount of duplicated technical debt and the resources required can be challenging or impossible for an individual site to build. Training on the data distributed across organizations can result in models that generalize much better than models trained on data from any of organizations alone. While there are approaches for decentralized sharing, these often do not provide the highest possible guarantees of sample privacy that only cryptography can provide. In addition, such approaches are often focused on probabilistic solutions. In this paper, we propose an approach that leverages the potential of datasets spread among a number of data collecting organizations by performing joint analyses in a secure and deterministic manner when only encrypted data is shared and manipulated. The approach is based on secure multiparty computation which refers to cryptographic protocols that enable distributed computation of a function over distributed inputs without revealing additional information about the inputs. It enables multiple organizations to train machine learning models on their joint data and apply the trained models to encrypted data without revealing their sensitive data to the other parties. In our proposed approach, organizations (or sites) securely collaborate to build a machine learning model as it would have been trained on the aggregated data of all the organizations combined. Importantly, the approach does not require a trusted party (i.e. aggregator), each contributing site plays an equal role in the process, and no site can learn individual data of any other site. We demonstrate effectiveness of the proposed approach, in a range of empirical evaluations using different machine learning algorithms including logistic regression and convolutional neural network models on human structural and functional magnetic resonance imaging datasets.
Conditions of blended learning implementation in H. S. Skovoroda Kharkiv National Pedagogical University: experience of Physics and Mathematics Faculty

The entire education system of Ukraine in the realities of the COVID-19 pandemic in 2020 found itself in a situation of the challenge for the immediate implementation of distance learning in educational institutions of all levels. Despite the presence of a relatively large number of scientific studies on the features of distance education, they were not enough for its practical implementation. A blended form of learning requires a combination of asynchronous and synchronous modes of distance learning, finding optimal tools and ways to interact with students, adapting traditional methods of studying disciplines to new conditions, overcoming inequality of participants in the educational process in technical support, mastering digital technologies. Analysis of the experience of the Faculty of Physics and Mathematics at H. S. Skovoroda Kharkiv National Pedagogical University showed that the conditions for the introduction of blended learning in institutions of higher pedagogical education are: advanced training and appropriate retraining of teaching staff; building a model of blended learning as a starting point for distance learning; development of educational and methodical support of educational disciplines taking into account the requirements of blended learning; implementation of all elements of the educational process in blended learning; providing participants of the educational process with organizational, methodological, technical and psychological support.
Tailoring Job Shop Scheduling Problem Instances Through Unified Particle Swarm Optimization

Problem instances are paramount when testing the performance of any learning algorithm. For this reason, it is customary to use widespread problems known as benchmark instances. Nonetheless, these are usually generated disregarding heuristics and their nature. For Job Shop Scheduling problem, researchers have created such instances based on random distributions. This idea may bias conclusions about the algorithm under test since a practitioner can only observe performance from a limited perspective, which may not even reflect real-life situations. However, addressing this issue implies tackling the instance generation problem while considering the nature of the solution approach. Hence, in this work, we propose an instance generator based on the Unified Particle Swarm Optimization algorithm, which can tailor instances to different goals. To validate our approach, we include instances generated to different heuristics and instances tailored to a variety of features. In the first case, we seek to favor or hinder one heuristic whereas doing the opposite for the remaining ones. In the second one, we explore instances with specific feature values. Our data reveal that the proposed approach fulfills the expectations and can effectively deal with different kinds of instances. We analyse the nature of the generated instances and their insights, which can be used to further the study about heuristics and problem features.
Aspect Based Sentimental Analysis of Hotel Reviews: A Comparative Study

The increasing use of the internet enables users to share their opinion about what they like and dislike regarding products and services. For efficient decision making, there is a need to analyze these reviews. Sentiment analysis or opinion mining is commonly used to detect polarity (positive or negative) of reviews. But, it does not show the aspect or orientation of the text. In this study, state-of-art approaches based on supervised machine learning employed to perform three tasks on the dataset provided by SemEval. Tasks A and B are related to predicting the aspect of the restaurant’s reviews, whereas task C shows their polarity. Additionally, this study aims to compare the performance of two feature engineering techniques and five machine learning algorithms to evaluate their performance on a publicly available dataset named SemEval-2015 Task 12. The experimental results showed that the word2vec features when used with the support vector machine algorithm outperformed by giving 76%, 72% and 79% off overall accuracies for Task A, Task B, and Task C respectively. Our comparative study holds practical significance and can be used as a baseline study in the domain of aspect-based sentiment analysis.
Designing Website-Based Scholarship Management Application for Teaching of Analytical Hierarchy Process (AHP) in Decision Support Systems (DSS) Subjects

Teaching Analytical Hierarchy Process (AHP) in Decision Support Systems (DSS) subjects encourages students to know how a good decision is taken. AHP itself in the field of education and teaching has been widely used as an effective and efficient method in solving educational problems. The purpose of this paper is to designing website-based scholarship management application for teaching of Analytical Hierarchy Process (AHP) in Decision Support Systems (DSS) subjects. Scholarship management using AHP is choosing the right criteria is very important. This system uses 5 criteria, namely academic value, academic achievement, non-academic achievement, parents 'income, and parents' dependents, as a basis for producing decision-supporting information. The scholarship management system is embodied in a website-based computer application. Utilizing this application provides more assistance to donors, so that the results of this learning are not only proven in theory but also in practice in real conditions. In addition, the Website-Based Scholarship Management Application can be run on a mobile basis and can be accessed wherever and whenever they are.
Cloud Data Services: Workloads, Architectures and Multi-Tenancy

Enterprises are moving their business critical workloads to public clouds at an accelerating pace. Cloud data services for Online Transaction Processing (OLTP), Data Analytics and NoSQL are essential building blocks for enterprise applications. Multi-tenancy is a crucial tenet for cloud data service providers that allows sharing of data center resources across tenants, thereby reducing cost. In this monograph, the authors review architectures of today’s cloud data services and identify trends and challenges that arise in multi-tenant cloud data services. They survey techniques that have been developed for enabling elasticity, providing SLAs, ensuring performance isolation and reducing cost. The emerging paradigm of serverless databases is reviewed and opportunities and challenges highlighted. Finally, the authors identify open research problems in the fast-changing landscape of cloud data services. This timely overview, written by recognized experts in the field provides readers with essential insights into designing cloud data services to day and in the future. This concise and accessible review will save designers and researchers hours by having it at their fingertips.
Secret Sharing-Based IoT Text Data Outsourcing: A Secure and Efficient Scheme

Secret Sharing has been recently used as an alternative approach to solve privacy-preserving issues in cloud-based data outsourcing, for overcoming the challenges faced when encryption-based methods are adopted. In this work we revisit secret sharing-based text data outsourcing schemes and focus on their applications into an Internet-of-Things (IoT) system with resource constrained IoT devices as clients. We propose a new method which is secure against common attacks to secret sharing-based text data outsourcing schemes. Compared with the existing works under the same assumption that the cloud servers are possibly colluded, our scheme is more efficient and supports multiplication based operations.
Supervised learning in the presence of concept drift: a modelling framework

Abstract We present a modelling framework for the investigation of supervised learning in non-stationary environments. Specifically, we model two example types of learning systems: prototype-based learning vector quantization (LVQ) for classification and shallow, layered neural networks for regression tasks. We investigate so-called student–teacher scenarios in which the systems are trained from a stream of high-dimensional, labeled data. Properties of the target task are considered to be non-stationary due to drift processes while the training is performed. Different types of concept drift are studied, which affect the density of example inputs only, the target rule itself, or both. By applying methods from statistical physics, we develop a modelling framework for the mathematical analysis of the training dynamics in non-stationary environments. Our results show that standard LVQ algorithms are already suitable for the training in non-stationary environments to a certain extent. However, the application of weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes. Furthermore, we investigate gradient-based training of layered neural networks with sigmoidal activation functions and compare with the use of rectified linear units. Our findings show that the sensitivity to concept drift and the effectiveness of weight decay differs significantly between the two types of activation function.
Quantum pattern matching oracle construction

We propose a couple of oracle construction methods for quantum pattern matching. We in turn show that one of the construct can be used with the Grover's search algorithm for exact and partial pattern matching, deterministically. The other one also points to the matched indices, but primarily provides a means to generate the Hamming distance between the pattern to be searched and all the possible sub strings in the input string, in a probabilistic way.