Reviewerly/exHarmony huggingface dataset:
Random processes with random transitions between stable states

Introduction: Studying random processes with several stable states and random transitions between them is important because it opens a wide range of practical problems. The detailed information structure is not studied well enough, and there is no unified approach to the description and probabilistic analysis of such processes. Purpose: Studying the main probabilistic characteristics of random processes with two stable states, and probabilistic analysis of control over chaotic transitions under various control actions. Results: We show the ways to represent and preliminarily analyze random processes with two stable states on the phase plane and in the pseudophase space. A general probabilistic model for the processes in question is proposed in the form of a two-component probabilistic «mixture» of distributions. A probabilistic analysis was carried out for the principles of control over random transitions between different states. We have defined the basic probabilistic characteristics for the processes in a management action with a variety of spectral-correlation properties and a changeable threshold for random transitions. The Poisson model of a random transition flow is analyzed with an example of «high» threshold levels. Practical relevance: The methods of visual, qualitative and analytical research in studying dynamic systems with several stable states can be combined. The proposed probabilistic models, regardless of the physical nature of the processes under consideration, can be used in problems of probabilistic analysis, control over probabilistic structure of random transitions, and simulation of physical, technical or biological systems with random switching.
Visualization of City model with VWorld (poster)

The VWorld Data Center provides high quality spatial information data with 3D terrains and buildings of major cities in Korea. In this paper, we proposed the visualization method for the city model using VWorld data. Anyone can use through the VWorld 3D map service site. We expect that our platform can be used to visualize 3D spatial information for various applications.
Anti-Social Media and Violence

I was shaken by the mass shooting at a mosque in New Zealand, particularly because the perpetrator was purposely video recording the slaughter on his telephone and was sending out the video in real...
Incremental Alignment of Metaphoric Language Model for Poetry Composition

The ability to automatically generate meaningful text with respect to a topic is an important AI mission. In particular, the automatic generation of content which is deemed creative is a great challenge. In this paper, poetry generation is approached through the lenses of a new architectural design for creativity, that leverages semantics for the creation of variance and the preservation of the content coherency throughout the generation process. The full implemented system is made available on github ( https://github.com/marilenaoita/poetry-composition-resources ).
The Implementation of MiRO, a Media-as-Place Computer Game

In this study, we implemented MiRO, a web-based Media-as-Place storytelling game that resembles an existing OS. Media-as-Place storytelling refers to a story where its medium is identical to its place. Observing from escape room games, we propose this concept by contrasting it with interactive storytelling and tangible interaction. To realize the computer version of Media-as-Place storytelling, we focused on imitating visual representation and interactions of existing OSs. MiRO involves OS elements (e.g., a taskbar), in-game applications (e.g., e-mail, a web browser), and interaction techniques for existing OSs (e.g., drag-and-drop). We also conducted a preliminary user evaluation with 17 participants. MiRO was perceived as an OS visually, although its interaction was limited due to the characteristics of the web environment. From the interviews, we found that the role of guidance is important because Media-as-Place storytelling allows a high variety of available interaction at a time.
Crossing Hands in the Russian Cards Problem

The Russian Cards Problem has been extensively studied as an example of a problem of an unconditionally secure protocol where the sender and receiver are able to transmit secret information safely over a public non-secure channel without the secret being learned by a third party with access to the channel. Epistemic logic in general and public announcement logic in particular have been very useful in this study, as it involves careful analysis of subtle properties of nested knowledge. A long standing open problem is whether or not there exist protocols of length strictly greater than two for the original problem. In this paper we finally settle this problem, answering the question in the affirmative, by presenting a new solution to the original Russian Cards Problem that involves more than two steps. It starts with an initial announcement with so-called crossing hands, after which it is not common knowledge that the protocol can terminate in one more step.
Advances in Speech-to-Speech Translation Technologies

Automated speech translation, long a dream, has come into widespread use, as enterprises, application developers, and government agencies have become aware. Real-world S2ST applications have been tested locally over the past decade in consumer, healthcare, military, and humanitarian missions, and several projects aim to enable automatic cross-language communications at the 2020 Olympic Games to be held in Tokyo. Accordingly, this chapter provides a survey of the field’s technologies, approaches, companies, projects, and target use cases. (It is based on an industry report sponsored by the Translation Automation Users Society, released in 2017.) Sections examine the Past, Present, and Future of speech-to-speech translation. The first provides an orientation concerning issues in speech translation and a capsule history; the second snapshots technical achievements and representative participants in the burgeoning current scene; and the third speculates about future directions, with emphasis on platforms and form factors, big data, knowledge source integration, and the roles of human and automatic translators.
Straightforward Recognition of Daily Objects in Smart Environments from Wearable Vision Sensor

In this work, we propose a method to create and synthesize a new set of virtual images of daily objects within a smart environment partially automating the labeling process. Proposed methods enable the generation of a large dataset from a set of few images using an ad hoc data augmentation, which increases the original dataset size, generating new items through partial modification of available images. The proposed method for data augmentation is accomplished through the following steps: (i) object tracking is proposed to identify and label static objects; and (ii) background subtraction is used to select the masked foreground object of moving objects, which are virtually projected with geometry transformation over room images used as background. Furthermore, a case study is carried out, where an inhabitant wears a wearable vision sensor in a daily scene. Eight objects are learned using the proposed methodology. Finally, obtained results and successful recognition rates are discussed.
A Practical Obstacle Avoidance Method Using Q-Learning with Local Information

Various methods have been proposed for solving the obstacle avoidance problem. However, many of them are based on information that might not be available for robots in real-world settings. We focus on the generalizability and the practical aspects of the problem instead of studying yet another obstacle avoidance method. We propose a simple but robust method based on reinforcement learning for obstacle avoidance using only local information that could be gathered by the sensors on the robot. We train the model with simple and random cases having only static obstacles in a simulated environment and deploy the trained model to an actual robot car. The robot successfully avoided the static and, surprisingly, dynamic obstacles and eventually reached the target.
Integrated Data Engineering for Automated Labeling (IDEAL) and Future Design of Aircraft

Data analysis and machine learning methods provide information that can be used to (1) reduce costs by enabling a condition-based maintenance program, (2) optimize maintenance scheduling, and (3) influence the designs of future platforms. The work presented here describes the integration of repurposed data and autonomous labeling of such engineered data compilations. This effort is called Integrated Data Engineering for Automated Labeling (IDEAL) and is based on an innovative data engineering approach called Data Programming. IDEAL aims to exploit the wealth of heterogeneous, historical data sets describing different aspects of aircraft performance from sources such as the aircraft onboard data collection system (Health and Usage Monitoring System/Flight Data Recorder) and the off-board maintenance logbook. By generating an integrated data landscape, IDEAL enables the exploration, exploitation, testing, and evaluation of innovative condition indicator (CI) algorithms for better representing the performance state of a platform and its components. In particular, IDEAL supports the Subject Matter Expert-guided characterization of ground-truth labels informing both the integration of disparate data sets and the classification of events within the resulting integrated data set. This mechanism results in massive labeled data sets that allow for the application of machine learning techniques to support the exposure of hidden phenomena embedded within platform performance data.
A Website Analytics System Considering Users’ Category

Website analysis is one of the most important fields of e-commerce and is always one of the main concerns of website administrators. Exploring the way users interact with web contents and where their attention focuses onto during navigation, is one of the key issues in this field. There are many studies in which the analysis is done for all categories of users. However, different categories of users have different needs from a website. An element may be favorable of a category while it is not of interest of another category. That is why in this paper, we propose a website analysis method based on users' categories that categorized users using two methods of data mining, classification and clustering, The first approach relies on the organizational categories and the second one on users' behaviors on the website. The proposed approaches have been implemented on a section of the University of Tehran's website. Then, using heat map, we showed the important items in a page for each category of users which can be used to restructure the pages appropriate to each category.
Quality Management of the Polytechnic Education by the Methods of Knowledge Value Refinement Technology

Abstract The author states the problems in the organization of polytechnic education at the world and national levels and offers involvement of innovative approaches to the organization of students training. The author recalls the author’s approach to the assessment of the essence of knowledge. The concept of «knowledge» is systematically used in problematic scientific publications, but the task of its semantic specification remains. This is predetermined by different assessments of the essence of knowledge not only in different scientific areas, but even within them. The author recalls the results of her previous studies where the activity essence of knowledge, principles of its realization, ontological structure of knowledge was specified. On this basis, the author describes the technology of refining the value of knowledge and provides data on the introduction of this technology. The effect of the introduction of technology to clarify the value of knowledge is presented and an invitation to discussion on the humanization of technological education on the basis of innovative assessment of the essence of knowledge and its value is given.
Rethinking Identification Protocols from the Point of View of the GDPR

An identification protocol has to deliver a proof that the protocol participants are who they claim to be. Related to the circumstances, the proof must be sufficiently convincing for the addressee. On the other hand, as long as the data minimality principle is concerned, the proof should be useless for any party that is not the intended addressee. While the first goal has attracted a lot of attention, the second one has been rather neglected. In this paper we discuss requirements for identification protocols from the point of view of privacy protection requirements of the GDPR regulation introduced in Europe. We concern the problem of cryptographic data created by identification protocols and misusing them as an evidence presented to third parties. We concern in particular the case when it appears that a malicious participant follows the protocol, however the privacy protection guarantees supposedly provided by the scheme are effectively broken. We show that from the point of view of GDPR the classical schemes like static Diffie-Hellman, Schnorr, Wu, Stinson-Wu, and Di Raimondo-Gennaro fail to comply with the EU Regulation even if they are deniable.
Transferring Informal Text in Arabic as Low Resource Languages: State-of-the-Art and Future Research Directions

Rapid growth in internet technology lead to increase the usage of social media platforms which make communication between users easier. Through the communication users used their daily languages which considered as non-standard language. The non-slandered text contains lots of noise, such as abbreviations, slang which used more in English languages and dialect words which are widely used in Arabic language. These texts face challenging using any natural language processing tools. Therefore, these texts need to be treated and transferred to be similar to their standard form. According to that the normalization and translation approach have been used to transfer the informal text. However, using these approach need large label or parallel datasets. While high resource languages such as English have enough parallel datasets, low resource languages such as Arabic is lack of enough parallel dataset. Therefore, in this paper we focus on the Arabic and Arabic dialects as a low resource language in the era of transferring non-stander text using normalization and translation approach.
Application of Fuzzy Quality on Dempster-shafer for Pest and Disease Diagnosing of Chili (Case Study on Tidal Swamp Land)

Abstract Expert systems are usually used only to help get the results of a diagnosis faster. In the expert system, a method usually used to support a diagnosis process. In this research using the method of Fuzzy and Dempster-Shafer. Fuzzy methods used to find the scale of values belief a fact with fuzzyfication process, while the Dempster-Shafer method used to combine pieces of the facts to calculate the likelihood of an event. The purpose of this study was to determine whether the method of Fuzzy and Dempster-Shafer can be applied to the expert system to provide disease diagnosis chili. Where the results of the expert system will produce a presentation about the likelihood of diagnosis of plant diseases chili. Fuzzy weights obtained are Low 0.15, Medium 0.4 and High 0.65.
NONCADO: A System to Prevent Falls by Encouraging Healthy Habits in Elderly People

Falls in the elderly are a known problem, leading to hospitalization, impaired life quality, and social costs. Falls are associated to multiple risk factors, related to the subject’s health, lifestyle, and living environment. Living alone makes it difficult to detect a patient’s decline, which increases the fall risk. In this paper, we present NONCADO, a project funded by the Lombardy Region (Italy), aimed at developing a system for preventing falls in the elderly living alone, by integrating data from a network of sensors (both wearable and environmental). The collected data are analyzed by a decision support system (DSS) that exploits advanced temporal data analysis techniques to detect behaviors known to increase the individual risk (e.g. moving within the house with inadequate lighting, or performing not enough physical activity). A daily report listing the detected risky behaviors is produced and delivered through a mobile app. Since we address long-term monitoring, it’s important to detect as well the changes in a subject’s habits that may increase fall risk. Such changes are summarized in a weekly report. A preliminary feasibility evaluation of the system was performed during a 2-weeks pilot study involving 16 patients treated at the Casa di Cura Privata del Policlinico hospital, in Milan, Italy. Patients were asked to perform 5 activities, and the system’s ability to correctly detect them was assessed. The study results were encouraging, as the system reached an overall accuracy of 90%.
Mention Recommendation with Context-Aware Probabilistic Matrix Factorization

Mention as a key feature on social networks can break through the effect of structural trapping and expand the visibility of a message. Although existing works usually use rank learning as implementation strategy before performing mention recommendation, these approaches may interfere with the influening factor exploration and cause some biases. In this paper, we propose a novel Context-aware Mention recommendation model based on Probabilistic Matrix Factorization (CMPMF). This model considers four important mention contextual factors including topic relevance, mention affinity, user profile similarity and message semantic similarity to measure the relevance score from users and messages dimensions. We fuse these mention contextual factors in latent spaces into the framework of probabilistic matrix factorization to improve the performance of mention recommendation. Through evaluation on a real-world dataset from Weibo, the empirically study demonstrates the effectiveness of discovered mention contextual factors. We also observe that topic relevance and mention affinity play a much significant role in the mention recommendation task. The results demonstrate our proposed method outperforms the state-of-the-art algorithms.
Development of a measuring system based on the principles of stereo vision

The paper discusses the main stages of creating and implementing a model of a universal, inexpensive measuring system based on the principles of stereo vision. Particularly special attention had been paid to the development, analysis, study of the features and accuracy of measurements of a passive stereo vision system, as a measuring device. In the course of the presented work, the following procedures were performed: calibration of a separate camera, calibration of a stereo camera, straightening and comparison of stereo images. The software part of the work was done in the C ++ programming language in the QtCreator editor using the computer vision library OpenCV 3.2.
Reordering Techniques in Japanese and English Machine Translation

English and Japanese have very different word order and form one of the most difficult language pairs for translating each other. In statistical machine translation, it is well known that the translation accuracy between distant languages such as English and Japanese, is significantly lower than that between similar languages such as English and French. In this article, we describe a series of works done at our research groups in NTT to solve the problems in statistical machine translation between language pairs with very different word order such as Japanese and English.
Multi-Dimensional Genomic Data Management for Region-Preserving Operations

In previous work, we presented GenoMetric Query Language (GMQL), an algebraic language for querying genomic datasets, supported by Genomic Data Management System (GDMS), an open-source big data engine implemented on top of Apache Spark. GMQL datasets are represented as genomic regions (i.e. intervals of the genome, included within a start and stop position) with an associated value, representing the signal associated to that region (the most typical signals represent gene expressions, peaks of expressions, and variants relative to a reference genome.) GMQL can process queries over billions of regions, organized within distinct datasets. In this paper, we focus on the efficient execution of region-preserving GMQL operations, in which the regions of the result are a subset of the regions of one of the operands; most GMQL operations are region-preserving. Chains of region-preserving operations can be efficiently executed by taking advantage of an array-based data organization, where region management can be separated from value management. We discuss this optimization in the context of the current GDMS system which has a row-based (relational) organization, and therefore requires dynamic data transformations. A similar approach applies to other application domains with interval-based data organization.
A Space-Efficient Technique of Policy Trees for an Intelligent Tutoring System on POMDP

Adaptive tutoring is essentially a decision process, in each step of which an optimal tutoring action is chosen and taken. In recent years, researchers have been increasingly interested in applying the partially observable Markov decision process (POMDP) model to build intelligent tutoring systems (ITSs). The POMDP model may enable an ITS to optimize tutoring when uncertainties exist. Computing in a POMDP is characterized by exponential costs, in both time and space, which obstruct the POMDP model for practical applications. In our research, we develop techniques to improve computing efficiency in a POMDP based ITS. In this paper, we report a space-efficient technique of policy trees. The technique may help achieve high space efficiency by grouping policy trees and dynamically creating the policy trees to be evaluated in making a decision.
A Machine Learning-based Software Code Weakness Detect Approach

Abstract In recent years, machine learning and deep learning have yielded immense success on computer vision, speech recognition and natural language processing. However, exploration of machine learning on software code weakness detect haven’t been fully investigated. Software source code could be thought of a kind of natural language but has the intrinsic difference from common natural language. In this work we propose a novel machine learning based approach to tackle the problem of software code weakness detection. Differing from traditional algorithm based tools, flawfinder, for example, we do not design the code weakness detection processing logics directly, instead we propose to use a variant version of Attribute-Relation File Format (ARFF) to preprocess the source code and utilize a state of the art classification algorithm in machine learning to build a unite code weakness detection model to learn the processing logic indirectly as a general framework. In order to exam the detection performance of our model, we collected a couple of datasets for each common weakness of Common Weakness Enumeration (CWE) List. Extensive results on these collected datasets show that our model achieves comparative results from traditional algorithm based tools in both false positive rate and false negative rate.