Reviewerly/exHarmony huggingface dataset:
Exploring Result Presentation in Conversational IR Using a Wizard-of-Oz Study

Recent researches in conversational IR have explored problems related to context enhancement, question-answering, and query reformulations. However, very few researches have focused on result presentation over audio channels. The linear and transient nature of speech makes it cognitively challenging for the user to process a large amount of information. Presenting the search results (from SERP) is equally challenging as it is not feasible to read out the list of results. In this paper, we propose a study to evaluate the users' preference of modalities when using conversational search systems. The study will help us to understand how results should be presented in a conversational search system. As we observe how users search using audio queries, interact with the intermediary, and process the results presented, we aim to develop an insight on how to present results more efficiently in a conversational search setting. We also plan on exploring the effectiveness and consistency of different media in a conversational search setting. Our observations will inform future designs and help to create a better understanding of such systems.
Dynamic Ranking System of Cloud SaaS Based on Consumer Preferences - Find SaaS M2NFCP

Software as a Service (SaaS) is a type of software application that runs and operates over a cloud computing infrastructure. SaaS has grown more dramatically compared to other cloud services delivery models (i.e. PaaS and IaaS) in terms of the number of available services. This rapid growth in SaaS brings a lot of challenges for consumers in selecting the optimum services. The aim of this article is to propose a ranking system for SaaS based on consumer’s preferences called Find SaaS M2NFCP. The proposed ranking system is based on measuring the shortest distance to the minimum and maximum of the selected consumer’s non-functional preferences. In addition, linguistic terms are taken into account to weight the most important non-functional preferences. The proposed system is evaluated against traditional SaaS ranking systems using data collected from online CRM SaaS and achieved improved results.
A Goal-Driven Context-Aware Architecture for Distributing Cognitive Service Group

Cognitive service is an emerging service paradigm in service-oriented computing. It can comprehend data in the same way as the human. Cognitive services require sufficient information to understand service scenarios. Actually, to achieve a goal sometimes requires multiple services with order dependencies and prerequisites to work collaboratively. When an exception event occurs during the service group working, the conventional approach is to restart or stop the service based on the exception type. If the environment information is changed much fast and retrieved unpractically, the exception event can cause the delayed response of the service group. If the goal of the service group is time-aware and service result is preferred, the regular policy is hard to match the requirement. In this paper, we address the problem of delayed response caused by exception events raised from the distributing cognitive service group. A novel architecture is proposed to ensure the overall consistency and real-time reaction of distributing cognitive service group.
QUANTIFIED INTUITIONISTIC LOGIC OVER METRIZABLE SPACES

Abstract In the topological semantics, quantified intuitionistic logic, QH, is known to be strongly complete not only for the class of all topological spaces but also for some particular topological spaces — for example, for the irrational line, ${\Bbb P}$ , and for the rational line, ${\Bbb Q}$ , in each case with a constant countable domain for the quantifiers. Each of ${\Bbb P}$ and ${\Bbb Q}$ is a separable zero-dimensional dense-in-itself metrizable space. The main result of the current article generalizes these known results: QH is strongly complete for any zero-dimensional dense-in-itself metrizable space with a constant domain of cardinality ≤ the space’s weight; consequently, QH is strongly complete for any separable zero-dimensional dense-in-itself metrizable space with a constant countable domain. We also prove a result that follows from earlier work of Moerdijk: if we allow varying domains for the quantifiers, then QH is strongly complete for any dense-in-itself metrizable space with countable domains.
Nonlinear Computing and Nonlinear Artificial Intelligence

The importance and the necessity of nonlinearity in Artificial Intelligence, AI, and deep learning are very well understood. A multi-layer neural network with linear activation function is equivalent to a single layer of neurons. It is nonlinearity of activation functions that adds complexity to each layer, transforming the network to a universal computing machine that can approximate any continuous function. However, nonlinearity and the complexity that it creates have not been investigated enough in AI and modern deep learning systems. NC State University’s Nonlinear Artificial Intelligence Lab focuses on nonlinearity and the complexity that comes with it, and investigates how this can be an engine of artificial intelligence. We peruse our research at different levels with different goals. In this article we explain our approach, and present an overview of our results.
Logo Generation with Generative Adversarial Networks Conditioned on Sentiment Terms

Logos are very important to represent the impression of companies and brands. Since many logos are created by designers, there is a problem of high cost both in terms of money and time. Since sentiment terms are important as keywords when designing logos, we propose a logo generation system using sentiment terms. Originally, we should create a dataset of logo images and sentiment terms directly, but it is difficult to collect these logo images and the corresponding sentiment terms. Therefore, we employ the domain adaptation method to the dataset of furniture images and sentiment terms to create dataset of the logos and sentiment terms. Finally, we can generate logos considering 17 sentiment terms using Generative Adversarial Networks. We evaluated the proposed system using MS-SSIM score and confirmed that it can generate logos with diversity. Moreover, we performed subjective experiments to confirm that it can generate logos reflecting sentiment terms.
A New World of Virtual Currency: Cryptocurrency

After virtual games and friends, the world is using virtual currency. It is an alternative method of payment and digital assets. This digital currency launched in 2009, has created surge in the market and has gained attention across the world. It has gathered all appreciation and criticism. Some governments want to ban it, some countries like Hong Kong, Abu Dhabi, Australia are contemplating as to how to use this innovation. New York State Department of Financial Services of US issues “Bit License regime” for using it. The paper discusses all pro and cons of cryptocurrency.
Content-Based Image Retrieval : A Comprehensive Study

Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci?cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.
Research on Immersion of Interactive Image Design in Visual Communication

In the course of new media art development, immersion is an ineluctable topic. Under the context of design, interactive image features immersion. The role of immersive design is not to add a new property, but to enhance existing properties. Virtual interface and individual interaction, as two main characteristics of immersion in visual communication, testify it on the other side. The paper discusses and analyzes narrative modes of visual immersion and structure of immersion in works from the perspective of visual language, and concentrates on non-structure design language adopted to organize visual information to develop a new road for experience design languages.
Multi-modal Image Stitching with Nonlinear Optimization

Despite significant advances in recent years, the problem of image stitching still lacks a robust solution. Most of the feature based image stitching algorithms perform image alignment based on either homography-based transformation or content-preserving warping. Pairwise homography-based approach miserably fails to handle parallax whereas content-preserving warping approach does not preserve the structural property of the images. In this paper, we propose a nonlinear optimization to find out the global homographies using pairwise homography estimates and point correspondences. We further compute local warping based alignment to mitigate the aberration caused by noises in the global homography estimation. To this end, we incorporate geometric as well as photometric constraints to design our cost function which is minimized to obtain better alignment after the global registration, thus producing accurate image stitching. Experimental results on various open datasets demonstrate that our proposed method outperforms state-of-the-art image stitching algorithms.
THE INTELLIGENT STUDENT, TRENDS OF DEVELOPMENT AND DECLINE, EGO AND FUTURE

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 2488-2496ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0681Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Interval Quadratic Connected Control Systems in Industrial Robotics and Mechatronics

Industrial robotic and mechatronic systems in real machinery are quadratically connected dynamical systems with parametric uncertainties, whose mathematical models can be extremely complex. Program motion stabilization of such systems, especially those described in quasi-coordinates, is a topical scientific problem, which does not yet have an acceptable for engineering developments comprehensive solution. The article proposes a possible concept of such a solution, based on the Lyapunov’s direct method, extended to the interval dynamic systems domain with time derivatives of state variables written in the interval cubic forms. The authors conceptual approach is based on new concepts introduced in the article: covering form, form compensator, modular variables form. According to this approach, first of all, the transition to new complex system state variables formed with signatures is carried out. The mathematical apparatus is based on special interval matrix algebra transformations, which could not be included in the article because of the volume, and therefore are only stated. As a result, the authors obtain a new class of quasi-relay type controls with system state dependent modulated impact jumps, and therefore combining the properties of both soft smooth and hard forcing types.
Census of bounded curvature paths

A bounded curvature path is a continuously differentiable piece-wise $$C^2$$ path with bounded absolute curvature connecting two points in the tangent bundle of a surface. These paths have been widely considered in computer science and engineering since the bound on curvature models the trajectory of the motion of robots under turning circle constraints. Analyzing global properties of spaces of bounded curvature paths is not a simple matter since the length variation between length minimizers of arbitrary close endpoints or directions is in many cases discontinuous. In this note, we develop a simple technology allowing us to partition the space of spaces of bounded curvature paths into one-parameter families. These families of spaces are classified in terms of the type of connected components their elements have (homotopy classes, isotopy classes, or isolated points) as we vary a parameter defined in the reals. Consequently, we answer a question raised by Dubins (Pac J Math 11(2):471–481, 1961).
Learning Methods for Fuzzy Inference System Using Vector Quantization

ファジィ推論システムの学習に関する多くの研究が行われている. これらの研究 の多くが最急降下法を用いているが, パラメータの初期値設定により, 学習後 のモデルの精度やルール数が影響をうけることが指摘されている. これを改善 するための多くの方法が提案されているが, 必ずしも満足できるシステム構成 とはなっていない. ベクトル量子化を使って, パラメータの初期値を設定する 方法は, 少ないルール数で高い精度を実現する方法の一つとして知られている. 一方で, この方法の問題点としては, 後件部の重みパラメータの初期値設定を 行う効率的な方法が知られていないことである. それゆえ, 前件部パラメータ と後件部パラメータの初期値を設定する方法を最急降下法に取り入れることで, この学習法の能力改善が期待できる. 本論文では, あらかじめ学習用の入出力 データを用いて, ベクトル量子化により入力空間の分割とその空間での重み設 定を行い, これらをパラメータの初期値として用いるファジィ推論システムの 学習法を提案し, その有効性を示す.
GAMER: A Genetic Algorithm with Motion Encoding Reuse for Action-Adventure Video Games

Genetic Algorithms (GAs) have been predominantly used in video games for finding the best possible sequence of actions that leads to a win condition. This work sets out to investigate an alternative application of GAs on action-adventure type video games. The main intuition is to encode actions depending on the state of the world of the game instead of the sequence of actions, like most of the other GA approaches do. Additionally, a methodology is being introduced which modifies a part of the agent’s logic and reuses it in another game. The proposed algorithm has been implemented in the GVG-AI competition’s framework and more specifically for the Zelda and Portals games. The obtained results, in terms of average score and win percentage, seem quite satisfactory and highlight the advantages of the suggested technique, especially when compared to a rolling horizon GA implementation of the aforementioned framework; firstly, the agent is efficient at various levels (different world topologies) after being trained in only one of them and secondly, the agent may be generalized to play more games of the same category.
USING MODERN EDUCATIONAL TECHNOLOGY IN THE FORMATION OF COMPETITIVE SPECIALIST

This article presents the experience of innovative use of modern pedagogical technologies in the formation of a competitive specialist, which is used in studying the discipline Foreign Language in high school.Use of this method facilitates business communications, sustains mental activity of the student's, provides team building and prepares conditions for creative thinking.
Development of Robotic Cane for Assisting Elderly People's Walk on Uneven Terrain

Recently, the number of elderly people is increasing very much especially in Japan. It is one of the most important theme to maintain and improve their QOL(Quality of Life) and it is necessary that elderly people can do their activities in daily lives according to their intention. Because the walking ability is indispensable in their social activities, gait assist canes are often used to support them and the demand for canes is increasing as the growing number of elderly. However, generally used canes (single or multi-legged cane) have the instability and the danger of falling on the uneven terrain. So, we will propose ``Gait assist robotic cane'' which can absorb irregularities on the uneven terrain and can keep the balance automatically. This cane can change the length of each leg according to the contact condition and control compliance of supporting legs using MR fluid. In this paper, we developed a prototype robotic cane which could be actually used outdoors and the control algorithm for the step absorption and the balancing functions. In the experiment, it was confirmed that the step absorption and the balancing function were effective.
Cross-Client SLA Management with the ysla Language and Engine

Due to lack of standardization and automation, large-scale Service Level Agreement (SLA) management remains challenging for IT service providers. For instance, flexible re-use of SLA definitions across different client engagements is often poorly supported by current SLA management frameworks. In this demonstration we present the ysla Engine, a new SLA management framework implementing the YAML-based ysla language for modeling SLAs. ysla provides novel semantic constructs for adaptable SLA templates that formally separate metrics definitions from associated customer-specific classification/categorization taxonomies for monitored subjects. In our demonstration we model the common, but intricate industry use case of SLAs for incident management and demonstrate how ysla-based SLA templates and SLAs can foster cross-client SLA re-usability.
Increasing Safety by Combining Multiple Declarative Rules in Robotic Perception Systems

Advanced cyber-physical systems such as mobile, networked robots are increasingly finding use in everyday society. A critical aspect of mobile robotics is the ability to react to a dynamically changing environment, which imposes significant requirements on the robot perception system. The perception system is key to maintaining safe navigation and operation for the robot and is often considered a safety-critical aspect of the system as a whole. To allow the system to operate in a public area the perception system thus has to be certified. The key issue that we address is how to have safety-compliant systems while keeping implementation transparency high and complexity low. In this paper we present an evaluation of different methods for modelling combinations of simple explicit computer vision rules designed to increase the trustworthiness of the perception system. We utilise the best-performing method, focusing on keeping the models of the perception pipeline transparent and understandable. We find that it is possible to improve the safety of the system with some performance cost, depending on the acceptable risk level.
Partnership between the Education and the Business in Russia: Shifting of the Framework

The process of adaptation of the Russian education system to the market principles was rather chaotic and resulted in a number of system problems. For instance, business organizations have distanced themselves from the education of qualified personnel on the basis of state educational institutions. The article features some results of a theoretical research of interaction between the state structures and firms, its specifics and prospects in the sphere of Russian education. The analysis showed that the term "social partnership" was replaced with the term "public-private partnership" as the main concept for interactions between education organizations and firms in Russia in the last 20 years. It reveals a transition from the ethical to the rational-economical argumentation of the participation of firms in the social sphere. In the absence of institutional mechanisms, the most effective model of interaction between educational institutions and firms seems to be the one based on individual economic profits for both sides.
0168 Sleep and Academic Performance in Korean High School Students

The objective of this study was to investigate Korean high school students’ sleep and its relationship with academic performance. The sleep surveys were completed by high school students in Daegu, South Korea. The questionnaires provided details for factors such as sleep quality, sleep and wake schedules, nighttime smartphone use, and a five-level grading scale pertaining to academic performance (A: 1-20%, B: 21-40%, C: 41-60%, D: 61-80%, E: 81-100%). Chi-squared test, independent t-test, analysis of variance and regression analysis were used to analyze the results. A total of 691 high school students consisting of 279 (40.4%) boys and 412 (59.6%) girls completed the questionnaires. Those with poor sleep quality (Pittsburgh sleep quality index, PSQI ≥8.5) resulted in 111 (16.1%) and depressed mood (Hospital depression scale, HDS ≥8) resulted in 254 (36.8%). All students with high levels of academic performance tended to have lower PSQI scores (A: 5.29±2.58, B: 5.41±2.98, C: 6.00±2.65, D & E: 6.15±2.97), and the A-level students had significantly higher Morningness-eveningness questionnaire scores than those at D & E-levels students. Regression analysis shows that earlier departure time from school and higher nighttime smartphone use was significantly associated with worse academic performance in high school students. This study showed a close relationship between students’ sleep quality and academic performance. The students with better sleep quality, morningness, and less nighttime smartphone use had a tendency to perform better academically. To improve academic performance, lifestyle modification should be considered as an important factor. NA
Shaping Student’s Scientific Mindset During Laboratory Classes in Physics at the University

The article deals with the problems faced when shaping student’s scientific mindset. It should be pointed out that such difficulties lie deep in the pedagogical science. Look-back analysis of undertaken research on the problems under consideration is carried out. The article proves the necessity to make use of all the possibilities of a learning process at the university to shape student’s scientific mindset in accordance with society requirements. It is shown that students are able to transfer knowledge into scientific mindset when studying physics at the university. Curriculum of lab classes in mechanics, molecular physics, and thermodynamics is developed considering its functional feature — an accomplishment of objectives in development physics. In accordance with the aim of shaping student’s mindset criteria on evaluating lab classes in physics are developed.
A Survey on Load Balancing Algorithms

Cloud computing is referred to as biggest technology of today’s environment that provide access to distributed resources on the basis of pay-per-use. Everyone try to use cloud to reduce the cost and maintenance of infrastructure due to which lots of load is increasing day by day. Therefore, there is need to balance that load since resources of cloud are limited but usage is increasing at every moment. This paper discuss how the resources are allocated and how the tasks are scheduled among those resources. Task scheduling mainly focuses on enhancing the utilization of resources and hence reduction in response time. There are various static and dynamic load balancing algorithms to balance the load, this paper discusses comparative study of these algorithms.