Reviewerly/exHarmony huggingface dataset:
Subjective Search Intent Predictions using Customer Reviews

Query intent prediction is a component of information retrieval which improves result relevance through an understanding of latent user intents in addition to explicit query keywords. We target context-of-use intents, such as the activity for which a product is used and the target audience for a product, which are subjective and not usually indexed as product attributes in the catalog. We describe a method to predict latent query intents: we extract intents from product reviews on amazon.com and, using behavioral purchase signals that associate queries with the reviewed products, train query classifiers that label queries with the intents extracted from reviews. For example, we predict the activity "running" for the query "adidas mens pants." We show that our method can predict latent intents not indexed directly in the product catalog.
Knowledge based Authentication Techniques and Challenges

Knowledge-based Authentication (KBA) is an authentication approach, which verifying the user identity when accessing services such as finical websites.KBA requests specific information to prove personal identity of the owner.This paper discusses the challenges that are faced by KBA techniques.Memorability is the main obstacle in KBA since the users trying to utilize simple passwords or unify the passwords in various services, a step that cause problems and issues with compliance with security policies.Furthermore, the technique of mixing username/password is considered as another important challenge of KBA due to the recall-based authentication.This discussion includes a comparative analysis of KBA's techniques based on trade-off criteria to support making of decision.This study's results can support organizations in the recommendations process of a suitable KBA technique for organizations.
AN EMPIRICAL STUDY IN ALBANIA OF FOREIGN DIRECT INVESTMENTS AND ECONOMIC GROWTH RELATIONSHIP

Albania has managed to attract increasing foreign direct investments (FDI) inflows as a result of achieving political and macroeconomic stability, improving the business climate and legislation. FDI is an essential source of sustained economic growth for Albania, bringing in capital investment, advanced technology, and highly qualified management. FDIs have helped the country finance existing account deficits, further develop the financial sector, and increase employment possibilities. This paper aims to study the empirical relationship between FDI and economic growth in Albania from 2002 to 2017 using time series data. This research will assess whether the inflow from foreign direct investors into Albania has created a positive cointegration relationship on economic growth. Keywords: Foreign Direct Investment, economic growth, development, cointegration, VECM. JEL Classifications: F21, F43, C23, 010 DOI: https://doi.org/10.32479/ijefi.9294
Using wavelets to verify the scale structure of precipitation forecasts

Abstract. Recently developed verification tools based on local wavelet spectra can isolate errors in the spatial structure of quantitative precipitation forecasts, thereby answering the question of whether the predicted rainfall variability is distributed correctly across a range of spatial scales. This study applies the wavelet-based structure scores to real numerical weather predictions and radar-derived observations for the first time. After tackling important practical concerns such as uncertain boundary conditions and missing data, the behaviour of the scores under realistic conditions is tested in selected case studies and analysed systematically across a large data set. Among the two tested wavelet scores, the approach based on the so-called map of central scales emerges as a particularly convenient and useful tool: summarizing the local spectrum at each pixel by its centre of mass results in a compact and informative visualization of the entire wavelet analysis. The histogram of these scales leads to a structure score which is straightforward to interpret and insensitive to free parameters like wavelet choice and boundary conditions. Its judgement is largely the same as that of the alternative approach (based on the spatial mean wavelet spectrum) and broadly consistent with other, established structural scores.
Pseudo Labeling and Negative Feedback Learning for Large-Scale Multi-Label Domain Classification

In large-scale domain classification, an utterance can be handled by multiple domains with overlapped capabilities. However, only a limited number of ground-truth domains are provided for each training utterance in practice while knowing as many as correct target labels is helpful for improving the model performance. In this paper, given one ground-truth domain for each training utterance, we regard domains consistently predicted with the highest confidences as additional pseudo labels for the training. In order to reduce prediction errors due to incorrect pseudo labels, we leverage utterances with negative system responses to decrease the confidences of the incorrectly predicted domains. Evaluating on user utterances from an intelligent conversational system, we show that the proposed approach significantly improves the performance of domain classification with hypothesis reranking.
An improved semantic similarity algorithm based on HowNet and CiLin

This paper explores an improved method for the semantic similarity calculation of words combined with HowNet and CiLin. Firstly, we designing the algorithm based on HowNet’s sememe similarity improvement calculation, comprehensively considering the influence of each part of sememe on the overall meaning, and improving the calculation of word similarity based on HowNet by changing the specific calculation method of each part of sememe. At the same time, we adopt different strategies for the different results obtained in the similarity calculation of CiLin. The experimental RG data set proves that the modified Pearson coefficient of the method reaches 0.87.
Integrating Acting, Planning, and Learning in Hierarchical Operational Models

We present new planning and learning algorithms for RAE, the Refinement Acting Engine (Ghallab, Nau, and Traverso 2016). RAE uses hierarchical operational models to perform tasks in dynamically changing environments. Our planning procedure, UPOM, does a UCT-like search in the space of operational models in order to find a near optimal method to use for the task and context at hand. Our learning strategies acquire, from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. Our experimental results show that UPOM and our learning strategies significantly improve RAE's performance in four test domains using two different metrics: efficiency and success ratio.
Secure Blockchain-Based Traffic Load Balancing Using Edge Computing and Reinforcement Learning

Congestion represents one of the major problems in constantly growing cities, and the expansion and modernization of the traffic system are a major priority to all government infrastructures. In effect, more efficient traffic represents a more efficient economy. Solving the issue of congestion by increasing the number of roads is not always the most cost-effective solution as it represents massive changes in city infrastructures that have been present for decades. Urban planning shapes the environment around us but fails to address specifically the future traffic clogging. Our research tackles the problem of traffic congestion by proposing a system for vehicle detection, identification, and count, allied with reinforcement learning for traffic congestion anticipation and prediction. The need for a real-time and efficient system led us to push the research and development onto an Edge Computing platform using IoT and secure transactions using Hyperledger Fabric blockchain. Blockchain intervenes as a security protocol in the proposed system. The native form of Internet of Things does not include security protocols which are important to a large scale implementation. Ensuring the security of transactions and permanent access control is the main reason why blockchain is rooted in our system architecture. Our project aims to reduce the traffic load on roads experiencing significant congestion, and improve overall city traffic system without costly investment into new communication infrastructures and city planning.
A Hierarchical Structured Multi-Head Attention Network for Multi-Turn Response Generation

As a crucial task in conversation systems, response generation for multi-turn conversation aims to generate a coherent, informative and diverse response according to the conversation context. Existing models of this task are limited in their ability to capture long-term dependencies within and between utterances and to identify pertinent or important information in the context. Inspired by the Transformer neural network based solely on attention mechanisms recently proposed in machine translation, we propose a novel hierarchical structured multi-head attention network (HMAN) model to address both problems. Specifically, the context sequences are encoded in a hierarchical structure in which multi-head self-attention is first employed to compute the representation matrix of each utterance, and then, these utterance matrices are integrated to form a context representation with the complicated dependencies learned using multi-head attention. The experimental results on two public conversation datasets demonstrate that our proposed model significantly outperforms several state-of-the-art baselines with respect to both automatic evaluation and human evaluation and can generate more diverse and informative responses.
Designing a Supermarket Service Robot Based on Deep Convolutional Neural Networks

In order to save people’s shopping time and reduce labor cost of supermarket operations, this paper proposes to design a supermarket service robot based on deep convolutional neural networks (DCNNs). Firstly, according to the shopping environment and needs of supermarket, the hardware and software structure of supermarket service robot is designed. The robot uses a robot operating system (ROS) middleware on Raspberry PI as a control kernel to implement wireless communication with customers and staff. So as to move flexibly, the omnidirectional wheels symmetrically installed under the robot chassis are adopted for tracking. The robot uses an infrared detection module to detect whether there are commodities in the warehouse or shelves or not, thereby grasping and placing commodities accurately. Secondly, the recently-developed single shot multibox detector (SSD), as a typical DCNN model, is employed to detect and identify objects. Finally, in order to verify robot performance, a supermarket environment is designed to simulate real-world scenario for experiments. Experimental results show that the designed supermarket service robot can automatically complete the procurement and replenishment of commodities well and present promising performance on commodity detection and recognition tasks.
Resilience of the superradiant phase against <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow><mml:mi mathvariant="normal">A</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:math> effects in the quantum Rabi dimer

We explore the quantum criticality of a two-site model combining quantum Rabi models with hopping interaction. Through a combination of analytical and numerical approaches, we find that the model allows the appearance of a superradiant quantum phase transition (QPT) even in the presence of strong ${\mathbf{A}}^{2}$ terms, preventing single-site superradiance. In the two-site model the effect of ${\mathbf{A}}^{2}$ terms can be surmounted by the photon delocalization from hopping, and a reversed superradiant QPT occurs as a consequence of the competition between ${\mathbf{A}}^{2}$ terms and the hopping interaction. We characterize the phase diagram and scaling functions and extract the critical exponents in the vicinity of the critical point, thus establishing the universal behavior of the second-order phase transition. Remarkably the effective hopping strength will be enhanced if more cavities are cascaded. We also prove that the multiqubit counterpart of the quantum Rabi dimer, i.e., the Dicke dimer, has the same properties in beating the ${\mathbf{A}}^{2}$ effect. Our work provides a path to the study of phase transitions in the presence of the ${\mathbf{A}}^{2}$ terms and offers the prospect of investigating quantum-criticality physics and quantum devices in many-body systems.
Intrinsic degree of coherence of two-qubit states and measures of two-particle quantum correlations

Recently, a basis-invariant measure of coherence known as the intrinsic degree of coherence has been established for classical and single-particle quantum states [ J. Opt. Soc. Am. B 36 , 2765 ( 2019 ) JOBPDE 0740-3224 10.1364/JOSAB.36.002765 ]. In this article, we use the same mathematical construction to define the intrinsic degree of coherence of two-qubit states and demonstrate its usefulness in quantifying two-particle quantum correlations and entanglement. In this context, we first show that the intrinsic degree of coherence of a two-qubit state puts an upper bound on the violations of Bell inequalities that can be achieved with the state and that a two-qubit state with an intrinsic degree of coherence less than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>1</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:msqrt> <mml:mn>3</mml:mn> </mml:msqrt> </mml:math> cannot violate Bell inequalities. We then show that the quantum discord of a two-qubit state, which quantifies the amount of quantum correlations available in the two-qubit state for certain tasks, is bounded from above by the intrinsic degree of coherence of the state. Next, in the context of two-particle entanglement, we show that the range of values that the concurrence of a two-qubit state can take is decided by the intrinsic degree of coherence of the two-qubit state together with that of the individual qubits. Finally, for the polarization two-qubit states generated by the parametric down-conversion of a pump photon, we propose an experimental scheme to measure the intrinsic degree of coherence of two-qubit states. We also present our theoretical study that shows how the intrinsic degree of coherence of a pump photon dictates the maximum intrinsic degree of coherence of the generated two-qubit state.
Feature Selection in Text Clustering Applications of Literary Texts: A Hybrid of Term Weighting Methods

The recent years have witnessed an increasing use of automated text clustering approaches and more particularly Vector Space Clustering (VSC) methods in the computational analysis of literary data including genre classification, theme analysis, stylometry, and authorship attribution.In spite of the effectiveness of VSC methods in resolving different problems in these disciplines and providing evidence-based research findings, the problem of feature selection remains a challenging one.For reliable text clustering applications, a clustering structure should be based on only and all the most distinctive features within a corpus.Although different term weighting approaches have been developed, the problem of identifying the most distinctive variables within a corpus remains challenging especially in the document clustering applications of literary texts.For this purpose, this study proposes a hybrid of statistical measures including variance analysis, term frequency-inverse document frequency, TF-IDF, and Principal Component Analysis (PCA) for selecting only and all the most distinctive features that can be usefully used for generating more reliable document clustering that can be usefully used in authorship attribution tasks.The study is based on a corpus of 74 novels written by 18 novelists representing different literary traditions.Results indicate that the proposed model proved effective in the successful extraction of the most distinctive features within the datasets and thus generating reliable clustering structures that can be usefully used in different computational applications of literary texts.
Powtoon: A Digital Medium to Optimize Students’ Cultural Presentation in ELT Classroom

Facing industrial revolution 4.0 requires university students to provide themselves with a skill that they can use to compete with machines or computers. One of the skills is negotiation which involves mastering language, especially English as a means of international communication. However, learning English as a foreign language is not as easy as it seems. The students need to use a proper learning media which match their characteristics as digital native and motivate them in learning English such as multimedia. By using multimedia, the students will not only learn about language but also the skill about how to use the media or a computer to come up with industrial revolution 4.0. Thus, the researchers who are English lecturers aimed to encourage students in Universitas Teknokrat Indonesia (UTI) to use more web-based medium as a medium in learning English in a class, exclusively Powtoon. This research used a qualitative method since it disclosed how UTI students use Powtoon in a class and what their opinion toward Powtoon for learning English. During the research, the students used Powtoon in a class as a presentation medium for a half-semester because after mid-test they were divided into 13 groups to present the topics given by the lecturer. For each meeting, there were 2-3 groups presentation. At the end of the semester, the students were given questionnaire related to multimedia usage and responded that they felt motivated in learning English by using technology especially Powtoon. As a result, using a web-based medium in learning English can increase not only student’s ability to language but also technology.
MSDC-Net: Multi-Scale Dense and Contextual Networks for Stereo Matching

Disparity prediction from stereo images is essential to computer vision applications such as autonomous driving, 3D model reconstruction, and object detection. To more accurately predict disparity map, a novel deep learning architecture (called MSDC-Net) for detecting the disparity map from a rectified pair of stereo images is proposed. Our MSDC-Net contains two modules: the multi-scale fusion 2D convolution module and the multi-scale residual 3D convolution module. The multi-scale fusion 2D convolution module exploits the potential multi-scale features, which extracts and fuses the different scale features by Dense-Net. The multi-scale residual 3D convolution module learns the different scale geometry context from the cost volume which aggregated by the multi-scale fusion 2D convolution module. Experimental results on Scene Flow and KITTI datasets demonstrate that our MSDC-Net significantly outperforms other approaches in the non-occluded region.
A Block Coordinate Descent Algorithm for Sparse Gaussian Graphical Model Inference with Laplacian Constraints

We consider the problem of inferring sparse Gaussian graphical models with Laplacian constraints, which can also be viewed as learning a graph Laplacian such that the observed graph signals are smooth with respect to it. A block coordinate descent algorithm is proposed for the resulting linearly constrained log-determinant maximum likelihood estimation problem with sparse regularization. Simulation results on synthetic data show the efficiency of our proposed algorithm.
A Silent Password Recognition Framework Based on Lip Analysis

Securing passwords either written or spoken is considered one of the challenging authentication issues faced by individuals and organizations. Written passwords could be easily stolen by look over, or man-behind, while spoken passwords could be recorded and replayed by attackers. A proposed silent password which is based on a dual security model for lip movement analysis will be a promoting solution to these attacks. The goal of the current research is to propose a hybrid voting framework for silent passwords recognition using lip movement analysis. The proposed framework is built for Arabic language by extracting figures from predefined Arabic lexicon. The predefined lexicon mainly contains the Arabic figures from zero to nine with different shapes. The framework takes a video or sequence of images as an input, and outputs the corresponding silent password for the frames extracted from the input video. In this paper, three techniques will be employed to extract effective visual features from mouth-lip movement. Such techniques are SURF, HoG and Haar feature extractor. The resultant features in each technique are fed separately into a classification model, namely, the hidden Markov model (HMM). The HMM identifies corresponding Arabic figure from a predefined lexicon based on input features. The final classification models that are produced from the three techniques have been grouped in a voting scheme to produce the final classification result. The proposed model will be tested on handcrafted data set of lip movement, and it has shown a promising result with improved accuracy of Arabic figures recognition.
Development of a Programming Course for Students of a Teacher Training Higher Education Institution Using the Programming Language Python

At present, teaching programming to future teachers is superficial or is excluded from computer science curricular. Only programming fundamentals are taught during computer studies, and skills acquired during the training are not applied in practice. The aims of this article are 1) to develop a programming course for teacher training disciplines that are taught in training units of the Ammosov North-Eastern Federal University (NEFU), and 2) give recommendations to apply features of the programming language Python in the professional activities of future teachers. This work applies the iterative methodological approach to undertake the study in various groups of students of NEFU from 2016 to 2019. Quantitative and objective indicators are used. Results show which modules of Python are the most appropriate for students to study for their future professional teaching activities. The training course "Use of the language Python by future teachers" is developed, and will be taught in all training units of the university, that have teacher training disciplines. The article determines a direction of future research related to teaching programming, which is important for all computer studies.
A Performance Comparison of WireGuard and OpenVPN

A fundamental problem that confronts virtual private network (VPN) applications is the overhead on throughput, ease of deployment and use, and overall utilization. WireGuard is a recently introduced light and secure cross-platform VPN application. It aims to simplify the process of setting up a secure connection while utilizing the multi-threading capability and minimizing the use of bandwidth. There have been several follow-up studies on WireGuard since its birth, most of which focus on the security analysis of the protocol. Despite the author's claim that WireGuard has impressive wins over OpenVPN and IPsec, there is no rigorous analysis of its performance to date. This paper presents a performance comparison of WireGuard and its main rival OpenVPN on various metrics. We construct an automated test framework and deploy it on a total of eight nodes, including remote AWS instances and local virtual machines. Our test results clearly show two main edges that WireGuard has over OpenVPN, its performance on multi-core machines and its light codebase.