Reviewerly/exHarmony huggingface dataset:
On Continuity of Transition Probabilities in Belief MDPs with General State and Action Spaces

Natural conditions sufficient for weak continuity of transition probabilities in belief MDPs (Markov decision processes) were established in our paper published in Mathematics of Operations Research in 2016. In particular, the transition probability in the belief MDP is weakly continuous if in the original MDP the transition probability is weakly continuous and the observation probability is continuous in total variation. These results imply sufficient conditions for the existence of optimal policies in POMDPs (partially observable MDPs) and provide computational methods for finding them. Recently Kara, Saldi, and Yuksel proved weak continuity of the transition probability for the belief MDP if the transition probability for the original MDP is continuous in total variation and the observation probability does not depend on controls. In this paper we show that the following two conditions imply weak continuity of transition probabilities for belief MDPs when observation probabilities depend on controls: (i) transition probabilities for the original MDP are continuous in total variation, and (ii) observation probabilities are measurable, and their dependence on controls is continuous in total variation.
Budgeted Reliability Maximization in Uncertain Graphs.

Network reliability measures the probability that a target node is reachable from a source node in an uncertain graph, i.e., a graph where every edge is associated with a probability of existence. In this paper, we investigate the novel and fundamental problem of adding a small number of edges in the uncertain network for maximizing the reliability between a given pair of nodes. We study the NP-hardness and the approximation hardness of our problem, and design effective, scalable solutions. Furthermore, we consider extended versions of our problem (e.g., multiple source and target nodes can be provided as input) to support and demonstrate a wider family of queries and applications, including sensor network reliability maximization and social influence maximization. Experimental results validate the effectiveness and efficiency of the proposed algorithms.
Study of the ranges of values of a Biomimetic Statistical Learning Tool parameters

We built a Biomimetic Statistical Learning T ool based on a P DE system, embarking the mathematical expression of biological determinants, in order to perform Data Fitting and Data Assimilation (See Flourent et al. (Submitted)). Before starting the tting of the parameters of a model, we have to specify for each parameter a lower and an upper values between which the optimization algorithm will search the value minimizing the error associated to the model. The goal of this working paper is to detail the study of the ranges of values of the parameters of the built Biomimetic Statistical Learning T ool.
Rods and Rings: Soft Subdivision Planner for R^3 x S^2.

We consider path planning for a rigid spatial robot moving amidst polyhedral obstacles. Our robot is either a rod or a ring. Being axially-symmetric, their configuration space is R^3 x S^2 with 5 degrees of freedom (DOF). Correct, complete and practical path planning for such robots is a long standing challenge in robotics. While the rod is one of the most widely studied spatial robots in path planning, the ring seems to be new, and a rare example of a non-simply-connected robot. This work provides rigorous and complete algorithms for these robots with theoretical guarantees. We implemented the algorithms in our open-source Core Library. Experiments show that they are practical, achieving near real-time performance. We compared our planner to state-of-the-art sampling planners in OMPL. Our subdivision path planner is based on the twin foundations of \epsilon-exactness and soft predicates. Correct implementation is relatively easy. The technical innovations include subdivision atlases for S^2, introduction of \Sigma_2 representations for footprints, and extensions of our feature-based technique for opening up the blackbox of collision detection.
iSenseVR: bringing VR exposure therapy outside the laboratory

Purpose The purpose of this paper is to present the preliminary outcomes of a research which takes gradual exposure in virtual reality (VR) outside the laboratory to empower people with “hidden disabilities” breaking down their barriers towards independent living. It explores the use of VR through smartphones to practically apply gradual exposure to environment stressors that are typically found in busy spaces from one’s own safe environment. Design/methodology/approach Aberdeen International Airport has kindly accepted to take part to this research as a case study. Following a participatory design and usability testing, a semi-controlled seven-day study was conducted among seven individuals with hidden disabilities to assess user acceptance. Findings Results showed undeniable participants’ engagement and enthusiasm for the proposed approach, although further research is needed to increase the presence and improve the overall user experience. Research limitations/implications The proposed research has been conducted on small cohort of participants outside of a clinical setting. Further engagement with individuals with hidden disabilities is required in order to determine the effectiveness of the proposed approach. Originality/value This research presents a methodological and technological framework which contributes effectively to the practicality of VR exposure therapy outside of the laboratory setting, from one’s own safe place.
Blockchain Governance: Perspective An Evaluation from COBIT 5

Currently blockchain has been recognized and implemented in cryptocurrency sector, but notfor other industry, especially enterprise. Through this research it is expected to help the adoption process of blockchain. The evaluation was conducted on blockchain through testing using COBIT 5 cascaded from Stakeholder Question to IT Process. 35% of organization goal is applicable with selected stakeholder question, while 65% is applicable with IT related goal. The results of the analysis stated that finding of this study was more applicable for enterprise organizations such as fintech company with some parameters need to be added as the future work due to the enterprise organization had a lot of business and governance processes that were already running. Meanwhile the start-up organization can be audited as well based on this process.
Deep Learning for the Degraded Broadcast Channel

Machine learning has shown promising results for communications system problems. We present results on the use of deep auto-encoders in order to learn a transceiver for the multiuser degraded broadcast channel, and see that the auto encoder is able to learn to communicate on this channel using superposition coding. Additionally, the deep neural net is able to determine a bit labeling and optimize the per user power allocation that depends on the per user SNR.
Research on the Association of Mobile Social Network Users Privacy Information Based on Big Data Analysis

The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications. The existing researches on user privacy protection in mobile social network mainly focus on privacy p... | Find, read and cite all the research you need on Tech Science Press
The concept of a Convention on Electronic Evidence

Stephen Mason explains the idea of a Convention dealing with the status of electronic evidence, covering civil and criminal proceedings; the investigation and examination of electronic evidence, and general provisions regarding the recognition and admissibility of electronic evidence from foreign jurisdictions. His paper describes his collaborative work with other experts in the field to draft an important and much needed Convention providing the first treaty in this sphere.
Experimental Study of Totally Optimal Decision Trees

In this paper, we present results of experimental studies related to the existence of totally optimal decision trees (which are optimal relative to two or more cost functions simultaneously) for nine decision tables from the UCI Machine Learning Repository. Such trees can be useful when we consider decision trees as algorithms for problem solving or as a way for knowledge representation. For cost functions, we use depth, average depth, and number of nodes. We study not only exact but also approximate decision trees based on five uncertainty measures: entropy, Gini index, misclassification error, relative misclassification error, and number of unordered pairs of rows with different decisions. To investigate the existence of totally optimal trees, we use an extension of dynamic programming that allows us to make multi-stage optimization of decision trees relative to a sequence of cost functions. Experimental results show that totally optimal decision trees exist in many cases. The behavior of graphs that describe how the number of decision tables with totally optimal decision trees depends on their accuracy is mainly irregular. However, one can observe some trends, in particular, an upward trend when accuracy is decreasing.
Research Of Conditions Application Of Distance Learning Technologies In The Master's Programs

The article presents the main results of a study conducted in 2018 at the Institute of Economics and Management of Kemerovo State University (Kemerovo, Russia) in order to determine the conditions for the effective realization of the potential of distance learning technologies in teaching courses in the Master’s program of Management. The study included the study of the master students’ opinion on the possibilities, limitations and prospects for the use of distance learning technologies. The results of the study show that students have the necessary competencies and motivation to learn using distance learning technologies, have a positive attitude to distance learning, they know and understand its main capabilities and limitations. However, undergraduates underestimated the real scale and nature of changes in the conditions necessary and sufficient for the use of distance technology. The presence of a well-functioning electronic information-educational environment and technical support for distance learning are not sufficient to ensure the quality of education. In the transition from traditional educational technology to distance technologies, the structure of situational factors and their influence on the quality of training, the role of individual characteristics of students, their working and living conditions, image life and other factors increase. The study showed that the preliminary special training of master students for distance learning, focusing their attention on autonomy, self-organization, self-government, as well as the obligatory consideration of situational factors, the influence of which is less noticeable during the traditional educational process, becomes an indispensable condition for the effective use of distance learning technologies.
Cryptocurrencies in the Global Economy

Among the most important fintech innovations are Bitcoins, other cryptocurrencies, and initial coin offerings (ICOs). Other innovations include smart contracts, blockchain, Internet applications, mobile payments, and so forth. There are almost 2,000 cryptocurrencies in existence as of 2018. The advent of cryptocurrency is a major reason for many to believe that blockchains would become a potential mainstream financial technology for the future. Cryptocurrencies have exhibited tremendous volatility—thus, cryptoassets are risky investments. Regulators are exploring the type of regulatory changes that would be needed to close the current loopholes and whether to issue their own central bank digital currency (CBDC). In addition to implications related to the illicit financial activities (such as money laundering), there have also been concerns around the impact of cryptocurrencies on the effectiveness of monetary policy and the overall financial stability. <b>TOPICS:</b>Currency, portfolio construction, wealth management
3D printing – a new disruption to law?

In matters of technology law and policy, especially for emerging technologies such as 3D printing, law and policymakers would do well to monitor the state of the technology and market developments as well as the more theoretical literature from law and other disciplines about conceptual obstacles that the affordances of technologies pose for existing legal and policy regimes. The example of 3D printing so far fits squarely into this description, as a potentially highly disruptive technology for many areas of law, but whose progress in practice is deviating from some of these predictions. Empirical monitoring of 3D printing’s development will assist academics, lawyers, law-makers and others in determining the extent to which any “disruption” needs managed through reform.
BUILDING TEACHERS’ INTERCULTURAL COMMUNICATION COMPETENCE IN THE DIGITAL ERA

This paper discusses the concept of intercultural communication competence and intercultural communication in education settings. The digital era which is indicated by the explosion of communication technologies has tremendous implication for intercultural communication. Intercultural communication is a transactional and symbolic process involving the attribution of meaning between people from different culture. It may occur not only in virtual environment in which people interact through communication technology media, but also in non-virtual settings. The ultimate terminal of intercultural communication skill is the ability to interact appropriately and effectively with those from other cultural background. Thus, teachers should master the intercultural communication competence, as learners and teachers come from a variety of cultural backgrounds and bring a variety of expectations to the interaction.
On the Idiosyncrasies of the

Shijia Liu, Hongyuan Mei, Adina Williams, Ryan Cotterell. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Document Clustering – A Feasible Demonstration with K-means Algorithm

The manual structural organization of documents is expensive in terms of time and efforts. Traversing large number of documents to interpret manually is also challenging issue. Therefore, sophisticated means are needed to cope with this challenge. Clustering is one of the automated solutions. It is a major tool in many applications of business and data sciences. Document clustering sorts out records into various gatherings called as groups, where the documents in each group share some regular properties as indicated in closeness or similarity measure. Robust document clustering assumes an essential role in helping its users to successfully explore, condense, and sort out the data. This paper aims at clustering textual documents using TF-IDF (Term Frequency – Inverse Document Frequency) scheme. This research proposed methods for the selection of initial centroids in k-means clustering algorithm, which reduces efforts to great extent by minimizing the number of iterations usually one and efficiently ensures the accuracy of obtained clusters. Besides, the proposed methods expressed potentially promising results for small document sets.
PENGEMBANGAN SISTEM PENDETEKSI BANJIR MENGGUNAKAN FUZZY DENGAN RASPBERRY PI (STUDI KASUS: KABUPATEN SAMPANG)

Flooding is a natural disaster that is troubling for the community. Floods occur due to overflowing water from rivers, lakes, or gutters. The flood also caused material losses and even caused casualties. Sampang Regency is one of the districts located on Madura Island which is often flooded.The Raspberry Pi is a mini computer that is capable of performing the same tasks as a computer. The use of the Raspberry Pi as a server is more efficient than using a PC server which in terms of price is very expensive. The Raspberry Pi can also be combined with various sensors because it has 40 pins as a connector. The Parallax Ping sensor is an ultrasonic sensor that can detect the distance of an object by emitting ultrasonic waves with a frequency of 40 KHz and then detecting the reflection. By using an ultrasonic sensor as a water level counter, the height value of the river will be obtained. From this height value, it is then processed using the fuzzy mamdani method to produce a river water level status. This elevation status will then be informed to the public.Based on the test results, this flood detection system uses an ultrasonic sensor as a water level meter that is controlled by the Raspberry Pi. Data from ultrasonic sensors then calculated by fuzzy mamdani method can produce water level status. The water level status is obtained from the height data of the water surface calculated by using an ultrasonic sensor. The water level status is then sent to a cellphone number using a wavecom modem.
The Use Of Representative Systems As A Means Of Teaching Students

The progressive development of Russian society suggests the need for an innovative system of higher education. On the one hand, there are new professions and competencies that require a qualitatively new approach to teaching students from modern universities, and, on the other hand, classical lectures have long lost their relevance and effectiveness due to the general availability of information. The problem is that lectures refer to passive forms of conducting classes, and in order for modern education to be effective, it is necessary to maximize the percentage of active and interactive forms of conducting classes. Unfortunately, quite often there are situations in which it is difficult for the teacher to find an effective way to transfer knowledge, since each student has his/her own peculiarities in the processing of new information. Channels of perception of new information are called the representative system of a person, which allows you to capture and process information, and contributes to the effective perception of new knowledge. The use of various types of representative systems in the educational process increases the students ability to perceive new information, enhances the motivation to learn, which contributes to the quality of higher education in general. The article analyzes the effectiveness of various teaching methods: passive, active and interactive. Information on the types of representative systems prevalent among Russian students is presented, and the influence of various types of representative systems on the process of effective information perception by university students is demonstrated.
Integrating multicore awareness functions into distribution middleware for improving performance of distributed audio surveillance

This paper describes an approach to improve the performance of the distributed audio-processing functions for audio surveillance systems. In order to increase portability, current distributed audio-processing uses the default capacities offered by the underlying scheduling facilities of the operating system. In this approach, a set of capacities are added to the distribution software that enable the reduction of the distributed processing time of audio frames at the server side by adding functions that utilize the underlying hardware resources including exclusive core reservation. By loosing some generality in the design of the distribution software, it is possible to increase performance and provide better isolation to selected audio tasks in the presence of other competing software tasks. The approach is designed and implemented as well as analyzed on general purpose computers with a server-client architecture using serial scheduling of the audio tasks and parallelizing the digital signal processing computations. The proposed solution is implemented and analyzed showing benefits in performance and robustness over single threaded audio processing. The resulting system is significantly more robust in the presence of other competing software tasks (noise). These results directly yield the possibility to manage more concurrent audio streams at the server side.
Data and Information Quality in Remote Sensing

Remote sensing datasets are characterized by multiple types of imperfections that alter extracted information and taken decisions to a variable degree depending on data acquisition conditions, processing, and final product requirements. Therefore, regardless of the sensors, type of data, extracted information, and complementary algorithms, the quality assessment question is a pervading and particularly complex one. This chapter summarizes relevant quality assessment approaches that have been proposed for data acquisition, information extraction, and data and information fusion, of the remote sensing acquisition-decision process. The case of quality evaluation for geographic information systems, which make use of remote sensing products, is also described. Aspects of a comprehensive quality model for remote sensing and problems that remain to be addressed offer a perspective of possible evolutions in the field.
Pemanfaatan Buku Bergambar Untuk Menumbuhkan Kemampuan Berbicara Bahasa Inggris Siswa Pra Sekolah

Young learner is very happy if he/she has a chance to see a book full of colorful pictures. Usually, he/she will ask many questions about the pictures and other things related with the pictures. Therefore, in this community service program, the team tried to use picture book to cultivate speaking competence of the students. The target audiences of the program were the students of SPS Sa’adatur Rahman. They were pre school students. Before the treatment was given to the students, they were asked to respond the greetings and to answer some questions about name, age, and things around them. From the test, it can be seen that they have enough vocabularies to speak simple English like good morning or this is an orange. Unfortunately, eventhough they know the vocabularies, they cannot arrange them into even very simple sentence like this is an orange. After the treatment, they were given the same test. The results showed that there were some improvements. They were able to greet their teachers and friends. They were able to say some sentences about themselves. They were also able to say some sentences about the things around them.
Code-Based Preservation Mechanism of Electronic Record in Electronic Record Center of Cloud Storage

With the rapid development of E-commerce and E-government, there are so many electronic records have been produced. The increasing number of electronic records brings about storage difficulties, the traditional electronic rec... | Find, read and cite all the research you need on Tech Science Press
Comparison of Feature Weighting in SVM Performance for Sentiment Analysis of Jakarta BRT

Twitter has been utilized to distribute opinions from netizens directly to public service providers, such as Jakarta Bus Rapid Transit (BRT), in an efficient and effective way. In this context, the opinions formatted in textual data can be analyzed to help BRT operators improve their facilities and services via sentiment analysis, which consists of multiple steps: preprocessing, feature weighting, classification, and evaluation. The preprocessing and feature weighting are the key processes that may significantly affect the classification algorithm performance. Several researches have investigated these key preocesses, specifically to observe its effect in classification performance. However, none of those researches compare n-gram feature tokenization with feature weighting in Bahasa Indonesia. The present study compares the combination of n-gram feature tokenization with feature weighting to the performance of Support Vector Machine algorithm. The present study utlilizes TF-IDF, TF-CHI, TF-RF, and TF-OR as the feature weighting scheme. The results show that TF-IDF has the highest performance of 79.3% (accuracy), 83.2% (precision), and 83.6% (recall), and 82.2% (F1 score).