Reviewerly/exHarmony huggingface dataset:
Improving Prediction Accuracy by Distinguishing the Importance of Different User-Item Pairs

Nowadays, the network brings a lot of conveniences along with problems. One of the challenges is how to efficiently obtain useful information from mass data. The rating prediction is an essential tool to handle this. However, the lack of rating data makes recommendation algorithms not accurate enough. We find that the existing algorithms treat each rating record's contribution to prediction results as equivalent. In fact, there is an implicit condition that different records have different importance for prediction. In this article, we consider this and propose two methods to distinguish the importance of rating data, one assigns weights during modeling and another during model learning. The experimental result demonstrates our algorithms are effective and outperforms several state-of-the-art methods.
PAREMEOLOGICAL WORLD VIEW OF ITALIAN SPEAKERS

In the proposed research, the analysis of paremiological picture of the world of Italian language is performed; the notions of “picture of the world”, “linguistic picture of the world”, “paremiological picture of the world” are analyzed; the connection between the linguistic and paremiological picture of the world is distinguished; the frequency of using mythological proverbs with the proper name is set. According to the conducted research, it is possible to distinguish the following notions : 1) picture of the world is the way people see this world, how they communicate with each other, etc.; 2) linguistic picture of the world deals with the linguistic approach in seeing the world, how with the help of words people express their feelings towards different things; 3) paremiological picture of the world deals with proverbs and sayings, with the help of which people show their attitude to different things. As a result, it is possible to notice that this research of proper names in Italian proverbs in the paremiological picture of the world promotes reconstruction of one of important components of a national language picture of the world which is a cultural sphere of any language. We have distinguished that proverbs and sayings with the proper name are mythological, religious, historical, literal, toponymic, from various spheres of social and individual activity of native speakers. As a result, in the studied proverbs and sayings the features connected with mythology come to the first place among the rest of them. Never before have the scientists studied proper names in the light of meaning and form. While investigating proverbs with proper names it is possible to notice the link between ancient myths and modern mass media where we have found the majority of our examples during the research. And it is noticed that the majority of analyzed proverbs in mass media have examples in political and economic articles. From the studied proverbs and sayings we can make the conclusion that it is a vital source of national and cultural wisdom that is worth analyzing.
Scene depth reconstruction via the light field in YCbCr color space

Depth reconstruction from the light field is now a vibrant research field in the computational imaging. Based on the 4D structure information of light field data, this paper proposes an algorithm with pixel-level precision on YCbCr color space to obtain accurate disparity and provide accurate depth information for 3D surface reconstruction. In this paper, the public HCI standard light field data are used to verify, test and evaluate the reconstruction accuracy.
The Historian of Philosophy and His Experience: Within and Beyond the Realm of Philosophy

The article analyzes the intellectual experience of a historian of philosophy. This experience is formed by the ideological and the political factors that lays beyond the realm of the philosophy. The author demonstrates how nonphilosophical factors influence both philosophy itself and the activity of the historian of philosophy. If, since the Greek ancient epoch, the basis of the philosophy is the faith in the universal laws of the nature and their intelligible character, then the historian of philosophy relies on the faith into the intelligible laws of the development of the thought, derived from the historicism, which is a part of the ideological corpus of the Western Enlightenment. The history of philosophy in the modern state is a space where the activity of the historian of philosophy, emulating epistemological anarchism, in reality covers its bias by the political paradigm of Western civilization. As a result, the history of philosophy turns out to be a sphere of unfolding specific political and ideological programs, and the experience of the historian of philosophy is not only professional but also political work. This personal experience by its nature is decisive for the formation of the image of philosophy and the picture of its historical development. The author suggests to qualify the history of philosophy as a register of knowledge in which the central concepts and definitions should be constantly renewed. In the conclusion, the author proposes to acknowledge that the historian of philosophy, working with the corpus of philosophical texts and ideas, has far greater degree of creative freedom than it is traditionally assumed.
Intensity Inhomogeneity Image Segmentation Based on Active Contours Driven by Self-Similarity

The segmentation of images with intensity inhomogeneity is always a challenging problem. For the segmentation of these kinds of images, traditional active contour models tend to reduce or correct the intensity inhomogeneity. In this chapter, we present a framework to make use of the intensity inhomogeneity in images to help to improve the segmentation performance. We use self-similarity measure to quantify the degree of the intensity inhomogeneity in images and incorporate it into a variational level set framework. The total energy functional of the proposed algorithm consists of three terms: a local region fitting term, an intensity inhomogeneity energy term, and a regularization term. The proposed model treats the intensity inhomogeneity in images as useful information rather than alleviates the effect of it. The proposed method is applied to segment various intensity inhomogeneous images with promising segmentation results. Comparison results also prove that the proposed method outperforms four state-of-the-art methods.
On the Design of a Privacy Preserving Collaborative Platform for Cybersecurity

Nowadays, cyber-attacks are targeting mobile devices, bank accounts, connected vehicles and cyber-physical systems. These attacks are becoming more complex and are raising safety problems when targeting physical environment. An efficient way to protect against these attacks is making several security actors collaborate in defining appropriate countermeasures. However, in practice, security actors refrain from collaborating to avoid sharing their proprietary security processes. These processes represent a critical knowledge as they reflect these actors brand images. In this work, we investigate the use of homomorphic encryption to define a privacy preserving framework for sharing processes between different cybersecurity actors and for providing confidential data analysis. We describe a high level design for a secure cloud platform managing encrypted data. The data analysis algorithms provided by the cloud platform are designed with our open source tool Cingulata, which enables designers to implement any data analysis function, compile it and run it on homomorphically encrypted data.
Modeling stock prices using mixture autoregressive model

Telecommunication has been being a need for a wide community that cannot be avoided. The development of communication technology users in Indonesia causes the movement of the development of information technology from a secondary or tertiary need to be a primary need. The increasing of the needs of communication in the community makes these stocks being the largest capital stocks. So that it makes community interest to invest in the telecommunication factory. The closing price of this stocks somehow changing form the high prices switch to the low prices or vice versa. The closing price fluctuation could cause the behavior of stock prices to emerge to a multi-modal pattern. Frequently it would hard to perform a time series model because of its multi-modal characteristics in its serial data. This paper demonstrates the success of the work of the Mixture Autoregressive (MAR) modeling to overcome the multi-modality of some of the serial telecommunication stock price data and compare its performance with the Autoregressive Integrated Moving Average (ARIMA) modeling based on the smaller Mean Square Error (MSE), Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC).
The Interplay Between Loss Functions and Structural Constraints in Dependency Parsing

Dependency parsing can be cast as a combinatorial optimization problem with the objective to find the highest-scoring graph, where edge scores are learnt from data. Several of the decoding algorithms that have been applied to this task employ structural restrictions on candidate solutions, such as the restriction to projective dependency trees in syntactic parsing, or the restriction to noncrossing graphs in semantic parsing. In this paper we study the interplay between structural restrictions and a common loss function in neural dependency parsing, the structural hingeloss. We show how structural constraints can make networks trained under this loss function diverge and propose a modified loss function that solves this problem. Our experimental evaluation shows that the modified loss function can yield improved parsing accuracy, compared to the unmodified baseline.
Ways to Collect Disparate Information in a Single Data Warehouse at a Machine-Building Enterprise

In this article, we discuss existing methods of collecting information from various sources of a heterogeneous information structure into a single data warehouse. Based on the collected information, a single information space of a machine-building enterprise is formed.
Matrix factorization recommendation algorithms based on knowledge map representation learning<sup>1</sup>

Abstract The matrix factorization recommendation algorithm does not consider characteristics of the recommendation object itself, resulting in poor recommendation results. Therefore, a matrix factorization recommendation algorithm based on knowledge map representation learning is proposed. Firstly, the recommendation object is represented as a low dimensional semantic vector by using the knowledge map distributed representation learning algorithm. Then the semantic similarity between objects is calculated, and the semantic similarity is incorporated into the objective optimization function of matrix factorization, so that the feature vectors obtained by matrix factorization can also contain semantic knowledge, which makes up for the shortcoming of matrix factorization recommendation algorithm that does not consider characteristics of the recommendation object itself from the semantic perspective. The experimental results show that the improved algorithm has higher accuracy, recall and coverage than the traditional matrix factorization recommendation algorithm.
Two-Level Systems

In this book we have stayed so far with a linear material response. This chapter marks a change. We consider quantum emitters with a nonlinear dynamics. As a starting point, we introduce the simplest model of a two-level system, which will allow us to develop a pictorial representation of the system state in terms of a Bloch vector. The generalization to few-level systems will be discussed in the next chapter, where we will also present examples representative for nano optics and plasmonics.
A Quadtree Density Clustering Algorithm Under Differential Privacy

Focusing on quadtree tree density clustering algorithm and studying how to keep the accuracy of clustering results while preserving users' privacy for social applications, an improved DBSCAN algorithm under differential privacy (DP-QuadtreeDBScan) is proposed in the paper. On the basis of analyzing the similarity between user space data and social relations, quadtree distance is used to measure the distance between two users, and a differentially private quadtree indexing method is adopted to effectively find the most similar users in an optimized way. Besides, Fibonacci's budget allocation technique is applied for privacy budget assignment. Experimental results show that the proposed algorithm achieves good clustering accuracy in condition of preserving social users' privacy.
Agile Explorations in AR

Although the importance of evaluating Augmented Reality solutions with users is being increasingly acknowledged which is reflected on the rise of AR user studies in the last years, a small number of works describe the application of a structured, user-centered framework to be iteratively implemented in explorations with AR. This paper presents the main findings from an exploratory study carried out to probe several experiences among different type of users with an AR-based wearable device. The aim of the study was to set out the first steps towards the design of an ideal AR solution, through the understanding of potential end users desires and expectations. To do so, several demo scenarios were created along four opposing conceptual lines. These were: an unconventional experience vs. a conventional one and a light solution vs. a powerful one. Several issues were then validated through interactive simulation-based user tests with two demographically different groups of participants. This work is part of an ongoing study aimed at acquiring good practices in Augmented Reality user interface and controls, under a user-centered approach. The primary goal is to develop an effective evaluation framework to explore and validate different solutions in an agile and effective way, that can be easily framed into the pace of the technological industry.
The semantic tagging model of chinese question sentence chunk based on description logics

QA (Question Answering) is one of the hot spots in artificial intelligence field. At present, English QA research has made great progress in large-scale text. There are still many difficulties in Chinese QA and it is impossible to understand the true meaning of questions. To solve the problem of Chinese question sentence can't provide deep semantic information for syntactic analysis, the paper proposes a semantic tagging model of Chinese question sentence chunk based on description logics. With the knowledge of HNC's concept symbol to get semantic information about the word, and by starting with the connotation of the concept categories, it preliminarily analyzes the logical structure of specific question sentences. Finally, with description logic reasoning mechanism, we get semantic view of question sentence and proving it's verify in practical.
A Sequent Game Computing Logic: Game Descriptions and Models

Game tree modeling on VMK encodings with a sequent description logic is presented based on computable partition functions on generic game model diagrams. The encoding allows us to reach newer computability areas on game trees based on agent game trees on VMK game models. Novel payoff criteria on game trees and game topologies are obtained. Epistemic accessibility is addressed on game model diagrams payoff computations.
Log-Based Reading Speed Prediction: A Case Study on War and Peace

In this exploratory study, we analyze reading behavior using logs from an ebook reading app. The logs contain users’ page turns along with time stamps and page sizes in characters. We focus on 17 readers of War and Peace by Leo Tolstoy, who read at least 80% of the novel. We aim at learning a regression model for reading speed based on shallow textual (e.g. word and sentence lengths) and contextual (e.g. time of the day and position in the book) features. Contextual features outperform textual ones and allow to predict reading speed with moderate quality. We share insights about the results and outline directions for future research. The analysis of reading behavior can be beneficial for school education, reading promotion, book recommendation, as well as for traditional creative writing and interactive fiction design.
Learning Media of Electric Lighting Installation Based on <i>Mobile Augmented Reality</i>

Learning Media of Electric Lighting Installation Based on Mobile Augmented Reality is a technology that can be used as a substitute for conventional learning media to modern virtual learning media. This can be accessed anywhere and anytime by using an Android smartphone. Augmented reality is a technology that helps users to be able to interact directly with virtual components that have been designed to resemble the original. This study applies augmented reality technology to learning aids called learning media at the concentration of household electrical lighting installations. In the operation of learning media, several procedures must be carried out before becoming a product that is ready for use. The results of this study is a media for studying household electric lighting installations assisted by mobile augmented reality.
Relative Equivalence of Defects

In diagnostic application works, possible defects set elements features and their classifications are not sufficiently detailed, moreover, an objective relationship on this set has been neglected. The main task of this paper is to describe features of the possible defects set elements, and also to investigate equivalence relations on a given set, taking into account their relative nature, namely: equivalence to a fixed set of diagnostic indicators. The problem of possible defects set equivalence in relation to the diagnostic indicators set is considered. Various defects classes are described. Diagnostic indicators sensitivity to the possible set of defects is analyzed. Various classes of relative equivalence defects are recorded in order to assess the attainability of given level equivalence.
Deep Learning for Combo Object Detection

Convolutional neural networks (CNNs) have become the most vigorous technique for a variety of different tasks in computer vision, due to their proficiency in automatically learning high-level visual representations for images. In this paper, we investigate the effect of deep neural networks on the accuracy in combo object detection setting. The insufficiency of labeled data, coupled with the uncertainty of spacial distribution and dynamic changes in luminance, creates situations where combo object detection is far more challenging. Using transfer learning, we present a system for combo object detection based on a deep CNN called ComboNN. The proposed ComboNN is pre-trained on a huge auxiliary dataset ImageNet and fine-tuned on our small dataset. The use of data augmentation and regularization technique significantly reduces overfitting and improves the robustness of the ComboNN. Experimental results demonstrate that our system is capable of making reliable prediction on combo object detection in the real-world images, and achieves much better accuracy than the state-of-the-art CNNs.
Dining Philosophers, Byzantine Generals, and the Various Nodes, Users, and Citizens under Blockchain Rule

Agreements, consensuses, protocols, resource-sharing, and fairness are all examples of social and political metaphors that define and shape new computational algorithms. The thought experiments and allegories about resource-sharing or agreement between nodes played a vital role in the development of "concurrent programming" (enabling processor power-sharing and process synchronization) and still later in the development of distributed computing (facilitating data access and synchronization). These paved the way for current concepts of consensus mechanisms, smart contracts, and other descriptions of cryptocurrencies, blockchain, distributed ledger, and hashgraph technologies, paradoxically reversing the relations between metaphor and artifact. New computing concepts and algorithmic processes, such as consensus mechanisms, trustless networks, and automated smart contracts or DAOs (Distributed Autonomous Organizations), aim to disrupt social contracts and political decision-making and replace economic, social, and political institutions (e.g., law, money, voting). Rather than something that needs a metaphor, algorithms are becoming the metaphor of good governance. Current fantasies of algorithmic governance exemplify this reversal of the role played by metaphors: they reduce all concepts of governance to automation and curtail opportunities for defining new computing challenges inspired by the original allegories, thought experiments, and metaphors. Especially now, when we are still learning how best to govern the transgressions and excesses of emerging distributed ledger technologies, productive relations between software and allegory, algorithms and metaphors, code and law are possible so long as they remain transitive. Against this tyranny of algorithms and technologies as metaphors and aspirational models of governance, we propose sandboxes and environments that allow stakeholders to combine prototyping with deliberation, algorithms with metaphors, codes with regulations.
Blockchain technology impact on the maritime supply chain

Blockchain technology is an emergent technology that has been implemented in different areas: finance, maritime industry, trade, education, land registration and supply chain management.
Analisis Cluster K-Means Kabupaten/Kota di Jawa Timur Berdasarkan Level Epidemik HIV

Human Immunodeficiency Virus (HIV) in East Java was not only occurred in urban areas. This is due to the population mobility and sexual lifestyles. This study aimed to classify districts/cities in East Java used Cluster K-Means analysis based on the number of high risk population that living with HIV. This study was an observational study with an analytical method. The secondary data was taken from HIV and PIMS Situation Report of the East Java Provincial Health in 2017. This study obtained 3 optimal clusters, which were 4 districts/cities in cluster 1 named alert category, 24 districts/cities in cluster 2 named standby category and 10 districts/cities in cluster 3 named alert category. Proposed suggestions based on this study were prioritized early HIV test and counseling, provided adequate care, support and treatment for people living with HIV.
Analysis and Development of Control Algorithms to Increase a Trackless Vehicle Driving Range

The development of new control algorithms and the study of their use in traction drive system are urgent tasks for specialists to solve. The paper considers development of innovative control algorithms for trackless vehicle. The main goal of the control system design is to provide increase of the driving range of the vehicle by using neural fuzzy control approach. Also the article describes the algorithms development for localization and large coefficients control. The transient processes results obtained during the simulation justify the appropriateness of the proposed techniques for trackless autonomous vehicles control. In addition, the paper offers other possible approaches to driving range optimization.