Reviewerly/exHarmony huggingface dataset:
The Concept of the Implementation of the Orthobiotic Approach and Well-Being Approach to Working with Staff in the Digital Economy

The fact of the active development in recent years of scientifi c thought in the direction of researching innovations in the digitalization of the economy remains a fact. Of course, much attention is paid to the study of hyperdynamic information fl ows. At the same time, one of the most important tasks is to study the necessary competences of personnel in the digital economy. The article presents a content analysis of the defi nition of “digital economy”, discusses the benefits and risks of the digital economy, and considers the requirements for personnel in the digital economy. The content analysis of the defi nitions of “orthobiotics”, “well-being” and “well-being” is presented. The eff ect of the transition from health to increase productivity is considered. The author proposes the operationalization of soft skills with the included competencies of the orthobiotic approach and well-being. The concept of the implementation of the orthobiotic approach and well-being in working with staff in the digital economy, developed by the author, is presented.
Implementation of Design Thinking Approach In Designing Learning Support Tools In The Classroom For Hearing Impaired Person

The absence of adequate accommodation for the hearing-impaired in the Education field is one of the problems in Indonesia nowadays. Teaching aids or learning support tools as accommodation can help the deaf to accelerate and improve the quality of their education. This research uses design thinking approach in designing the tool so that the result of the design is in accordance with the needs and desires of the users, which are deaf elementary students age 8-10. Started from the empathy phase until the define phase which obtained that the target users have a need and desire to learn the vocabulary with ease and fun then proceed with the ideation phase with stakeholders and prototyping to generate ideas and create the teaching aids in accordance with their needs and desires in the form of an arcade game with card of words and ended with the testing phase which shows that the tool is able to improve visual receptive language comprehension of 8.07% and visual expressive language of 77.74% in a fun way. This research has produced a teaching aids designed with design thinking approach which can improve the quality of their learning in school in accordance with the needs and desires of hearing-impaired elementary school students and has been validated by stakeholders.
Continuous Ranked Probability Score Validation Methods in Mixture Bayesian Model for Microarray Data in Indonesia

Abstract Validation in statistical modeling becomes a very important part to get information on how well the model has been built. Algorithm of Continuous Ranked Probability Score (CRPS) is a validation method of goodness of fit model in statistical modeling. A model that has a small CRPS value and has a small statistical significance, then the model is declared fit for data. Conversely, if a model has a large CRPS value, then the model is declared not fit for data. Several applications of the CRPS Algorithm have been developed for unimodal distribution models. Bayesian mixture is a modeling with Bayesian approach where data has a multimodal distribution. Characteristics of multimodal distribution are owned by microarray data in Indonesia, namely data on gene expression differences for several gene IDs from Chickpea plants in Indonesia. The purpose of this study was to obtain a performance from the Continuous Ranked Probability Score (CRPS) Algorithm as a goodness of fit model method in Bayesian Mixture Model (BMM) modeling for microarray data in Indonesia in a series of activities to find new varieties of Chiekpea plants that are resistant to attack by pathogenic fungal diseases Ascochyta Rabiei. The results of this study have succeeded in establishing the Algorithm of Continuous Ranked Probability Score (CRPS) for the distribution of normal mixture for data on gene expression differences of Chickpea plants in Indonesia as a result of microarray experiments with Bayesian approaches. BMM modeling on microarray data is declared fit because it has a small average value of CRPS, which is 0.0412 to 0.385.
Handling Web Bias 2019

A key aspect of the Web Science conference is exploring the ethical challenges of technologies, data, algorithms, platforms, and people in the Web as well as detecting, preventing and predicting anomalies in web data including algorithmic and data biases. Handling Web Bias (HWB) is a new workshop focusing on best practices for identifying and handling web bias. Awareness of the problems of algorithmic and data bias has been growing but even with careful review of the algorithms and data sets, it may not be possible to delete all unwanted bias, particularly when systems learn from historical data that encodes cultural biases. This workshop will take a rich and cross-domain approach to this complicated problem, providing a venue for researchers to move beyond awareness of the problem of algorithmic and data bias to focus on practical strategies for handling it.
Performance Analysis and Performance Testing

This chapter covers topics that you need to know if you want to control the performance level in a large product automatically. You will learn different kinds of performance tests, performance anomalies that you can observe, and how to protect yourself from these anomalies. At the end of this chapter, you will find a description of performance-driven development (an approach for writing performance tests) and a general discussion about performance culture.
Feature Based Opinion Mining for Hotel Profiling

Hotel profiling plays an important role in hotel recommendation. With the proliferation of huge amount of user-generated-reviews on web-sites, hotel profiling has become more challenging as these reviews and embedded opinions could indirectly drive hotels. Comprehensive hotel profiling based on review analysis could help people to get an overall opinion on hotels and hence to facilitate mindful tourism. To avoid deficiencies of many other recent researches, this research focuses more on the feature-based opining mining rather analysing only sentiments of the reviews. Thus, a semantic profiling approach which integrates a machine learning technique, part-of-speech (PoS) tagging and Ontology is proposed for feature-based hotel profiling. PoS tagging is used for recognising patterns of opinions and SentiWordNet is used to resolve semantic heterogeneity of the opinion phrases and to classify them. Feature-based analysis could generate the feature level opinion about a hotel in several aspects including food, hospitality and environment.
Analysis of Usability for the Dice CAPTCHA

This paper explores the usability of the Dice CAPTCHA via analysis of the time spent to solve the CAPTCHA, and number of tries for solving the CAPTCHA. The experiment was conducted on a set of 197 subjects who use the Internet, and are discriminated by age, daily Internet usage in hours, Internet experience in years, and type of device where a solution to the CAPTCHA is found. Each user was asked to find a solution to the Dice CAPTCHA on a tablet or laptop, and the time to successfully find a solution to the CAPTCHA for a given number of attempts was registered. Analysis was performed on the collected data via association rule mining and artificial neural network. It revealed that the time to find a solution in a given number of attempts of the CAPTCHA depended on different combinations of values of user’s features, as well as the most meaningful features influencing the solution time. In addition, this dependence was explored through prediction of the CAPTCHA solution time from the user’s features via artificial neural network. The obtained results are very helpful to analyze the combination of features having an influence on the CAPTCHA solution, and consequently, to find the CAPTCHA mostly complying to the postulate of “ideal” test.
Requirements Engineering as Science in the Small

This paper identifies similarities between requirements engineering (RE) and the scientific method. It argues that RE is concerned with the development of models of small universes and thus relates to natural science despite RE having a prescriptive rather than a descriptive nature. An explicitly philosophical stance sheds light on RE processes and brings up criteria for judging RE processes.
Data Processing Model in Hierarchical Multi-agent System Based on Decentralized Attribute-Based Encryption

Multi-Agent and distributed systems are widely used in military defense systems, logistics, navigation and motion control systems of unmanned aerial vehicles and other systems. Such systems are complex and include many different types of agents, the number of which can vary in real time. Elements of such a system require proper management of access roles and security keys during the process of sharing sensitive information. In this paper, we tested the applicability of decentralized attribute-based access control for communication in multi-agent systems. By implementing one of the algorithms, the operation speed and practical benefits of attribute-based encryption were evaluated, and also the encryption rate on the synchronization frequency of the multi-agent system was estimated.
Graph Convolution and Self Attention Based Non-maximum Suppression

Non-maximum suppression is an integral and last part of object detection. Traditional NMS algorithm sorts the detection boxes according to their class scores. The detection boxes with maximum score are always selected while all other boxes with a sufficient overlap with the preserved boxes are discarded. This strategy is simple and effective. However, there still need some improvements in this process because the algorithm makes a ‘hard’ decision (accept or reject) for each box. In this paper, we formulate the non-maximum suppression as a rescoring process and construct a network called NmsNet which utilizes graph convolution and self attention mechanism to predict each box as an object or redundant one. We evaluate our method on the VOC2007 dataset. The experimental results show that our method achieves a higher MAP compared with the traditional greedy NMS and the Soft NMS.
The BRAVO: A Framework of Building Reputation Analytics from Voice Online

This paper provides a framework to efficiently discover production performance and its application plan by analyzing massive amounts of comments from online review data, especially in the field of hospitality. In order to achieve the goal, two stages of text analytics of sentiment analysis and structural topic model estimating are integrated to classify sentimental polarity of each reviews and elicit hidden dimensions of products or services. Based on these dimensions and polarities, this paper verifies key attributes which impact customer satisfaction by adapting logistic regression. This study extends prior research limitation which focused on discovering the product defect by (1) strength detection, (2) time series analysis, and (3) explanation of the relationship between crucial factors and polarity of the review as a proxy of customer satisfaction. By integrating text analytics from computational linguistic and a traditional statistical method, this paper is expected to contribute on both academical and practical implications.
Conditionals and Conditional Probabilities without Triviality

The Adams Thesis holds for a conditional → and a probability assignment P if and only if P(A→B)=P(B∣A) whenever P(A)>0. The restriction ensures that P(B∣A) is well defined by the classical formula P(B∣A)=P(B∩A)/P(A). Drawing on deep results of Maharam on measure algebras, it is shown that, notwithstanding well-known triviality results (Lewis, etc.), any probability space can be extended to a probability space with a new conditional satisfying the Adams Thesis and satisfying a number of axioms for conditionals. This puts significant limits on how far triviality results can go.
A Constant-Factor Approximation Algorithm for Online Coverage Path Planning with Energy Constraint

In this paper, we study the problem of coverage planning by a mobile robot with a limited energy budget. The objective of the robot is to cover every point in the environment while minimizing the traveled path length. The environment is initially unknown to the robot. Therefore, it needs to avoid the obstacles in the environment on-the-fly during the exploration. As the robot has a specific energy budget, it might not be able to cover the complete environment in one traversal. Instead, it will need to visit a static charging station periodically in order to recharge its energy. To solve the stated problem, we propose a budgeted depth-first search (DFS)-based exploration strategy that helps the robot to cover any unknown planar environment while bounding the maximum path length to a constant-factor of the shortest-possible path length. Our $O(1)$-approximation guarantee advances the state-of-the-art of log-approximation for this problem. Simulation results show that our proposed algorithm outperforms the current state-of-the-art algorithm both in terms of the traveled path length and run time in all the tested environments with concave and convex obstacles.
Ranking Robustness In Adversarial Retrieval Settings

The adversarial retrieval setting over the Web entails many challenges. Some of these are due to, essentially, ranking competitions between document authors who modify them in response to rankings induced for queries of interest so as to rank-promote the documents. One unwarranted consequence is that rankings can rapidly change due to small, almost indiscernible changes, of documents. In recent work, we addressed the issue of ranking robustness under (adversarial) document manipulations for feature-based learning-to-rank approaches. We presented a formalism of different notions of ranking robustness that gave rise to a few theoretical findings. Empirical evaluation provided support to these findings. Our next goals are to devise learning-to-rank techniques for optimizing relevance and robustness simultaneously, study the connections between ranking robustness and fairness, and to devise additional testbeds for evaluating ranking robustness.
Secret Voting: Knowledge vs Trust

In this paper we discuss on the general level the issue of trust the voters put in the secret voting system used in political elections. We promote the point of view that the voters’ recognition and acceptance of a particular voting system should be based not on trust, but on knowledge—on the knowledge of what is really happening during the voting, not presumed. The starting point of knowledge is mistrust. We formulate a postulate of mistrust (of an election commission) and keeping it in mind look closely at several deployed voting systems including the Estonian one. We find all of them unacceptable. Then we formulate three criteria for an Internet voting system to be acceptable to the voters (civil society). These are (1) openness, (2) acceptance of the system by an uncertain and broad set of qualified examiners and (3) a verifiable access of unsophisticated voters to the voter’s software approved by the aforementioned set of examiners. Finally, we explain only the modest role of the official (government) certification of the voting system in its recognition by the voters (civil society).
Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis

In the academic world, a large amount of data is handled each day, ranging from student's assessments to their socio-economic data. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. However, Data Warehouses are not able to perform predictive analysis by themselves, so machine intelligence techniques can be used for sorting, grouping, and predicting based on historical information to improve the analysis quality. This work describes a Data Warehouse architecture to carry out an academic performance analysis of students.
Efficient colour filter array demosaicking with prior error reduction

A single sensor camera captures only one intensity value in a pixel known as the raw/unprocessed image. The raw or incomplete image is reconstructed to a full colour image by the process called demosaicking. The proposed work introduces an error efficient demosaicking algorithm. The efficient prior error reduction technique helps to obtain better results. The demosaicked green image is a guide to the residual demosaicking process. The conventional demosaicking is replaced by the residual demosaicking. The standard Kodak and McMaster datasets are employed in the experimental analysis. The proposed methodology has produced optimal performance with reduced error compared to the conventional demosaicking algorithms.
Privacy-Protecting Algorithms for Digraph Shortest Path Queries

Trust relation in this work refers to permission that is given to a user at a source-host to access another user at a target-host through an authentication key with a unique fingerprint. The database owner can form a directed graph out of these trust relations, such that user-host pairs are considered nodes and fingerprints as arrows. The authors of this article present a novel protocol to query the shortest path from node A to node B, in a privacy preserving manner. The authors would like to use a cloud to perform such queries, but they do not allow the cloud to learn any information about the graph, nor the query. Also, the database owner is prevented from learning any information about the query, except that it happened.
Developing Physics Learning Multimedia to Improve Graphic and Verbal Representation of High School Students

Abstract The purpose of this research are; (1) to develop the appropriate physics learning tool used to assist the learning process of learners, and (2) to know the ability of graphical and verbal representation of the learners. This development study refers to the 4D method, which includes: (1) defining, (2) development, (3) planning, and (4) deployment of devices. Learning tools developed include Teaching and learning plan (RPP), test of graphical and verbal representation and multimedia learning. This development product is validated by expert lecturers, physics teachers, and peer reviewer. The subject of empiric test consisted of 100 students. The limited test subjected consisted of 21 students. Field test subjects in the experimental class were as many as 32 students and the control class as many as 31 students. Data collection used interview guides, learning device assessment sheets and learning result test. Data analysis technique using MANOVA with 5% significance rate.
MaxEntropy Pursuit Variational Inference

One of the core problems in variational inference is a choice of approximate posterior distribution. It is crucial to trade-off between efficient inference with simple families as mean-field models and accuracy of inference. We propose a variant of a greedy approximation of the posterior distribution with tractable base learners. Using Max-Entropy approach, we obtain a well-defined optimization problem. We demonstrate the ability of the method to capture complex multimodal posterior via continual learning setting for neural networks.
Profit Maximization of Big Data Jobs in Cloud Using Stochastic Optimization

Reserved instances offered by cloud providers make it possible to reserve resources and computing capacity for a specific period of time. One should pay for all the hours of that time interval; in exchange, the hourly rate is significantly lower than on-demand instances. Reserved Instances can significantly reduce the monetary cost of resources needed to process big data applications in cloud. However, purchases of these instances are non-refundable, and hence, one should be able to estimate the required resources prior to purchase to avoid over-payment. It becomes important especially when the results obtained by big data job has monetary value, such as business intelligence applications. But, estimating the resource demand of big data processing jobs is hard because of numerous factors that affect them such as data locality, data skew, stragglers, internal settings of big data processing framework, interference among instances, instances availability, etc. To maximize the profit of processing such big data jobs in cloud considering fluctuating nature of their resource demand, as well as reserved instances limitations, we propose Reserved Instances Stochastic Allocation (RISA) approach. Using historical traces of resource demand of big data jobs submitted by user, RISA leverages stochastic optimization to determine the amount of resources needed to be reserved for that user to maximize the profit. Our evaluation using real-world traces shows that RISA can increase the net profit by up to 10x, compared to previous approaches. RISA can also find solutions as close as 2 percent to the best possible solution.
Collaborative Learning Agents (CLA) for Swarm Intelligence and Applications to Health Monitoring of System of Systems

The system of systems is the perspective of multiple systems as part of a larger, more complex system. A system of systems usually includes highly interacting, interrelated and interdependent sub-systems that form a complex and unified system. Maintaining the health of such a system of systems requires constant collection and analysis of the big data from sensors installed in the sub-systems. The statistical significance for machine learning (ML) and artificial intelligence (AI) applications improves purely due to the increasing big data size. This positive impact can be a great advantage. However, other challenges arise for processing and learning from big data. Traditional data sciences, ML and AI used in small- or moderate-sized analysis typically require tight coupling of the computations, where such an algorithm often executes in a single machine or job and reads all the data at once. Making a generic case of parallel and distributed computing for a ML/AI algorithm using big data proves a difficult task. In this paper, we described a novel infrastructure, namely collaborative learning agents (CLA) and the application in an operational environment, namely swarm intelligence, where a swarm agent is implemented using a CLA. This infrastructure enables a collection of swarms working together for fusing heterogeneous big data sources in a parallel and distributed fashion as if they are as in a single agent. The infrastructure is especially feasible for analyzing data from internet of things (IoT) or broadly defined system of systems to maintain its well-being or health. As a use case, we described a data set from the Hack the Machine event, where data sciences and ML/AI work together to better understand Navy’s engines, ships and system of systems. The sensors installed in a distributed environment collect heterogeneous big data. We show how CLA and swarm intelligence used to analyze data from system of systems and quickly examine the health and maintenance issues across multiple sensors. The methodology can be applied to a wide range of system of systems that leverage collaborative, distributed learning agents and AI for automation.