Reviewerly/exHarmony huggingface dataset:
Apportioned margin approach for cost sensitive large margin classifiers

We consider the problem of cost sensitive multiclass classification, where we would like to increase the sensitivity of an important class at the expense of a less important one. We adopt an apportioned margin framework to address this problem, which enables an efficient margin shift between classes that share the same boundary. The decision boundary between all pairs of classes divides the margin between them in accordance with a given prioritization vector, which yields a tighter error bound for the important classes while also reducing the overall out-of-sample error. In addition to demonstrating an efficient implementation of our framework, we derive generalization bounds, demonstrate Fisher consistency, adapt the framework to Mercer’s kernel and to neural networks, and report promising empirical results on all accounts.
Incorporating Rich Social Interactions Into MDPs

Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions originating from microsociology can be formalized by extending a nested MDP where agents reason about arbitrary functions of each other's rewards. This extended Social MDP allows us to encode the five basic interactions that underlie microsociology: cooperation, conflict, coercion, competition, and exchange. The result is a robotic agent capable of executing social interactions in new environments with no interaction-specific training; like humans it can engage socially in novel ways even without a single example of that social interaction. Moreover, the estimations of these Social MDPs align closely with the judge-ments of humans when considering which social interaction is taking place in an environment. This method both sheds light on the nature of social interactions, by providing concrete mathematical definitions, and brings rich social interactions into a mathematical framework that has proven to be natural for robotics.
Secure Data Management using Distributed Storages for Stronger Cybersecurity in Cloud Service

As data used in IT services increases exponentially, cloud storage-based services that normally store user data on the cloud storage which is controlled by cloud storage service providers and restore data only when necessary are being activated. Accordingly, the importance of technique for securing data stored in cloud storage is increasing. In particular, as the importance of data increases, an attack on cloud storage services is considered not as a simple threat to the safety of individual services, but as an attack from the perspective of cybersecurity that can threaten the well-being of society where the stored data is utilized. In this paper, we propose a technique for providing cloud service safety. Specifically, we consider three major requirements for providing information security of data stored in the cloud storage: confidentiality, integrity, and availability. In the case of the previously proposed technique, only some of the three major requirements are intensively analyzed and countermeasures are proposed. Since there is a corresponding technique for individual requirements, it is possible to respond to each security requirement by parallelly applying the existing technologies. However, in the case of a system designed with such simple approaches, it is difficult to provide all security requirements by actually grafting it due to the difference in the configuration method of each hypothesis, or even if possible, efficiency impairment occurs. In this paper, we designed a technique that can efficiently meet the three major requirements from this point of view.
Exploring the Computational Recovery of the Typographical Logic of Book Indexes as Paratext for Improving Navigation within Digitized Historical Texts with Semantic Model

Abstract The rapid growth in the number of digital texts has posed challenges for information professionals to make them more navigable and usable. These challenges have prompted researchers to develop different approaches to creating semantic descriptions for these resources aiming at more efficient and effective navigation and granular access to specific textual content. In this poster, we report on ongoing work that utilizes cluster‐oriented machine learning algorithms to recover the logical relationships embedded in the typographical layout of book indexes, structures that are lost when standard page‐level OCR scanning techniques are deployed on the index pages of digitized historical books. Recovering typographical logic would aid in the construction of semantic representation of indexes that can provide granular access to digital texts thus improving textual navigation.
Invariant Submerged Material Recognition with Fluorescence Lidar and Sparsity-Based Approaches

This work deals with submerged object recognition methodologies with fluorescence LIDAR that can be applied when no prior information about environmental conditions is available. Previous invariant methods rely upon conventional unconstrained and constrained subspace projection concepts. This paper investigates application of sparsity-based concepts within this framework. A method enforcing both L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> norm penalties is investigated. Both synthetic and real data are employed to evaluate the potential of the method. Experimental results reveal that sparsity-based methods can be useful in this context and deserve further investigation.
Running Scientific Computing Workloads in JointCloud Computing Environment

Cloud computing has been widely adopted by personal developers and enterprises because of its on-demand and elastic resource usage paradigm. Currently most cloud applications are running on one single cloud. Cloud vendors provide users a bewildering choice of VMs and the choice can have a big impact on performance and cost. Therefore, it is difficult to choose the appropriate VMs to process jobs efficiently and economically. Besides, more and more applications are demanding to run across several clouds with heterogeneous resources to satisfy the requirements like best cost efficiency, avoidance of vender lock-in, and geolocation sensitive service. Existing systems lack a mechanism to unify the management of heterogeneous computing resources across clouds. In this paper, we present Docklet to build a virtual private cloud in JointCloud environments, managing heterogeneous and distributed cloud resources and selecting suitable resources according to the needs of users. At the same time, it provides computing framework support for scientific computing workloads, having the customized software stacks, configurations readily available. With Docklet, users only need one browser to do their jobs online. In the end, we evaluate the performance of running scientific workloads in Docklet.
Fighting Hunger in Indonesia with Effective Food Banking System

This paper demonstrates the feasibility of developing an effective food banking system in response to the food insecurities encountered by millions of Indonesians as the result of Covid-19 pandemic. Food banks are charitable organization that gives food aid to food insecure families and today there are thousands of food banks all over the world. However, in Indonesia food banks are still largely unheard of with the exception of a handful of organizations. Therefore this paper focuses on how charitable organizations can set up food banks that are powered with an information system that allows effective management of its daily operations and sharing of resources with other food banks. The outcome of this study is a food banking system that performs successfully during testing process where the output is measured against the provided requirements. With an effective system, charitable organizations will be able to demonstrate accountability to their donors and show the positive impacts they have on the community, hence inviting more donors to contribute to their cause..
The K and Th concentration in the Martian crust: insights from multi-scale analyses

The univariate statistics of potassium (K) and thorium (Th) concentrations in the oceanic and continental crust of the Earth has been recently investigated from geochemical databases and airborne radiometric surveys [1].This study demonstrates that the frequency distributions of these elements are scale-dependent.There are right-skewed for small-scale samples (typical volume of rocks analyzed for an individual sample in a dataset) but tend to be more symmetric for large-scale samples.The right-skewed behavior of K and Th is attributed to their incompatible behavior during partial melting or fractional crystallization.The scale-dependence and evolution toward normal distributions are a direct consequence of the central limit theorem applied to K and Th concentrations.The results of the results of this study may be applied to Mars, using the Mars Odyssey global
Introduction: Aims and Outline

Parametric families of probability distributions and their properties are extensively studied in the literature on statistical modeling and inference. Exponential families of distributions comprise density functions of a particular form, which enables general assertions and leads to nice features. With a focus on statistical inference, a motivation is given to examine exponential families in a general and unified approach. Moreover, aims and outline are addressed along with a literature overview on books in the field of Mathematical Statistics, which discuss exponential families and their properties.
An Outdoor Navigation System for Mobile Robots Based on Human Detection

The reliable navigation of mobile robots outdoors depends on high-precision maps, accurate localization and efficient path planning. The complexity and variability of outdoor scenes bring great challenges to the navigation of mobile robots. The safety of mobile robot operation is the premise of application. In this paper, a navigation system including localization, human detection and path planning is proposed for the safety of mobile robot in outdoor tasks. We use Support Vector Machine (SVM) to carry out human detection for 3D point cloud before path planning. And special avoidance processing is carried out for clusters labeled as human. The system can enhance the safety of robot and human in the operation of mobile robot.
Statistical analysis of cytogenetic data

Using the data obtained in a cytogenetic study as an example, we consider the typical errors that are made when performing statistical analysis. Widespread but flawed statistical analysis inevitably produces biased results and increases the likelihood of incorrect scientific conclusions. Errors occur due to not taking into account the study design and the structure of the analyzed data. The article shows how the numerical imbalance of the data set leads to a bias in the result. Using a dataset as an example, it explains how to balance the complex. It shows the advantage of presenting sample indicators with confidence intervals instead of statistical errors. Attention is drawn to the need to take into account the size of the analyzed shares when choosing a statistical method. It shows how the same data set can be analyzed in different ways depending on the purpose of the study. The algorithm of correct statistical analysis and the form of the tabular presentation of the results are described.&#x0D; Keywords: data structure, numerically unbalanced complex, confidence interval.
STEM Education in Natural Sciences Through Online Classes with Experiments

The teaching of sciences by the modern teacher in school today is proving to be a challenge. The general attitude of students in general is that science is boring, difficult and not necessary in life. The recently popular STEM approach to education comes to the rescue. But what it is and how teachers can apply it successfully in practice are the questions to which we continue to look for the best answers for the specific circumstances. The article describes a study of the impact of STEM science education on the knowledge and skills of 5th grade students of Profiled Natural and Mathematical High School "Geo Milev" in Stara Zagora during isolation and Covid-19 pandemic in the school year 2020 - 2021. The author shares good pedagogical practice in science education through online experiments.
Cognitive Discovery Pipeline Applied to Informal Knowledge

This paper introduces an information extraction (IE) architecture to process unstructured data that are typically generated within non-formal contexts such as the ones that are exchanged during multiple forms of technical meetings. Information extraction and knowledge management are providing practical value in all kinds of industries and boosting R&D activities specifically. Our project sees a clear opportunity to leverage and expand this capability to process also “informal sources” of business/technical content such as the one that is exchanged along informal channels during, for instance, physical or web meetings. Given these observations, we have defined a software architecture designed on the concept of expandability; a prototype has also been developed capable of showing the peculiarities and the critical points.
Social Distance Monitoring using AI techniques in AAL environments

The growing global epidemic of coronavirus, as well as the shortage in health care professionals, impose protection measures to be taken by citizens themselves. One of these measures is social distancing. In an autonomous and assisted living environment a set of technologies can offer protection to the tenants by monitoring the individuals’ distance between them. This paper proposes to leverage the social distancing application in Ambient assisted living environments, following the precautions proposed by the experts against Covid-19. The evaluation results were quite impressive and a high accuracy was achieved as the difference between application’s and real world measurements was up to 5%.
Wind Turbine Detection with Synthetic Overhead Imagery

Automatic object detection in overhead imagery is greatly increasing the pace at which we learn about anthropic activity across diverse fields such as economics, environmental management, and engineering. Properly-trained object detection models save significant amounts of human labor when it comes to finding objects, especially rare objects, in overhead imagery. However, applying such techniques to find rare objects typically requires a large amount of labeled imagery data (typically requiring expensive manual labeling). We generate synthetic imagery to reduce the amount of manually labeled imagery required to train models, particularly for data-constrained applications. This approach takes real, unlabeled overhead imagery and inserts artificial 3D models of objects onto the imagery. To evaluate this technique, we collected a baseline dataset of overhead imagery with wind turbines that have been manually labeled and overhead imagery that does not contain wind turbines. We then add synthetic imagery to some of the unlabeled data to create a synthetic dataset. Our results indicate that adding synthetic imagery in training achieving higher levels of recall for similar levels of precision, outperforming the baseline of only real imagery.
SURF: Optimized Data Distribution Technology

In a hybrid cloud environment, an enterprise employs a number of local sites (or data centers) and cloud data center(s) of possibly multiple cloud service providers. SURF is a technology for controlling the distribution of data and applications in such an environment. Distribution decisions are based on (a) enterprise policy, (b) performance characteristics, and (c) pricing tables. The technology may be used as the core of the multiple system types. SURF’s distributed algorithms, and its data distribution decision component, were examined via extensive simulations on a hypothetical application and are vastly superior in a changing environment to naive placement methods.
Prosperity, Gender Equality and Economic Development in the MENA Region

In this study, the effects of macroeconomic fundamentals, prosperity, and gender equality factors on economic development are examined in 18 Middle East and North Africa (MENA) countries. Even though the MENA region countries have gone through advancements in the average wealth and prosperity levels in recent years, these positive changes have not been sufficient to prevent the region to fall behind other world regions in the rankings of development and economic growth. A panel data analysis is carried out for the time duration 2009 and 2019. This period coincides with both before and after the Arab Spring period. A wide spectrum of indicators is used to understand how different aspects of prosperity and gender equality can affect growth in the MENA region. In this way, a collective and comprehensive approach is adopted in the empirical analysis. One of the dependent variables, the productive capacity is calculated as a compound indicator which accounts for resource rents, GDP and working-age population. Two composite indicators, economic environment and institutions are used in the main estimations. Market access and infrastructure, investment environment, enterprise conditions, and economic quality are integrated to generate the variable economic environment. Governance, safety and security, and personal freedom are compounded to create the variable institutions. Later, these two composite indicators’ components are controlled individually in the estimations in order to carry out a robustness check analysis. It is found that female labor force participation, foreign direct investment, social capital, natural resource rents, education, a better economic environment, and improvements in safety-security can positively affect economic growth, total factor productivity and productive capacity. On the other hand, the overall quality of institutions and proportion of women representatives in national parliaments do not seem to significantly impact economic development in these MENA countries.
Stereo Matching Algorithm for High-Resolution Remote Sensing Images Based on Sparse Coding and Dictionary Learning

With the development of remote sensing imaging technology, more and more high-resolution remote sensing images can be easily obtained. As a significant processing step in 3D reconstruction of remote sensing images, stereo matching has always been a hot spot in the field of stereo vision. However, the efficiency and accuracy of stereo matching algorithms remain to be improved, particularly the efficiency. To this end, we propose a remote sensing image stereo matching algorithm based on sparse coding and dictionary learning (SCDL), which can greatly improve the matching efficiency and achieve higher matching accuracy. In SCDL, positive and negative samples are introduced into dictionary learning phase. A constraint term is designed in the objective function to discriminate correct and incorrect matching points. Experimental results show that the proposed algorithm performed well both in efficiency and accuracy.
A Physical-Cyber Dual Space Fusion Learning Assistant Method Based on Mixed Reality Implementation

The learning assistant method of the Physical-Cyber dual space is an effective method to improve learning interaction and learning effect. In this paper, we proposed a learning assistant method based on mixed reality technology and the fusion of physical space and cyber space, including two classical learning scenes. Firstly, we integrated physical real classroom scene with online three-dimensional resource scene, which effectively expanded the imagination of students. It allows students to learn abstract knowledge of macro or micro scenes more intuitively. Secondly, we explored the online search functions to extend resources related to offline teaching knowledge. Finally, we constructed the instructing scene and application example of the dual space fusion learning companion system using HoloLens. We have come to the necessity, rationality and technical feasibility of the method, and carry out learning activities on the basis of HoloLens, which provides new ideas for promoting the innovation of teaching and learning forms in the future.
Psychological portrait of a leader on the example of Muslim women

The article is devoted to the study of the psychological portrait of a leader on the example of Muslim women. An empirical study aimed at studying value orientations, identity, motivation for achieving leadership was organized with the help of 100 respondents – all women leaders, where 50 of them are Muslim women and 50 non-Muslim between the ages of 20 to 55. The respondents have professional education (Professional Education – 0.9%, Secondary Vocational Education – 10.4%, Higher Vocational Education – 82.1%, incomplete Higher Vocational Education – 2.8%, Candidate of Science – 3.8%.); work experience in a managerial position from 1 year to over 15 years; 50.9% profess Islam religion, 43.5% are representatives of Christianity, Buddhism, Judaism, 2.8% are atheists, 2.8% are the other. Comparative analysis of the empirical study results was carried out using the U-Mann-Whitney test for diff erences, Student’s t-test, and the Spearman’s r-rank correlation coeffi cient in the statistical package of the SPSS 18.0 program (PASW Statistics 18.0). Analysis of the research results confi rmed the hypothesis that the portrait of a Muslim female leader contains distinctive features due to value orientations and the identifi cation of one’s own “self-image”.
A study on the algorithm of ultrasonic detection and recognition based on DAG‐SVMs mixed HMM of teleoperation gestures for intelligent manufacturing devices

Remote control for the position and status of a machine or an equipment can often be teleoperated by gestures in an intelligent manufacturing environment. In order to solve the problems that gestures with two directions such as left and right cannot be detected by single ultrasonic frequency, double different ultrasonic frequencies are used to detect gestures by the Doppler shift, and an algorithm of the recognition gesture based on the DAG-SVMs mixed Hidden Markov Model (HMM) is proposed to identify and classify the extracted feature sequences. Thus, four more types of gestures are expanded other than that of reading display screen information, and the comparative experiments to classify and recognise gestures of teleoperation are made with DAG-SVMs, the HMM, the DAG-SVMs mixed HMM, and other improved HMM algorithms. The test results have shown that the mean rate of gesture recognition for the algorithm based on the DAG-SVMs mixed HMM is 94.917%, which is 9.497% higher than that of the unimproved HMM, and its recognition accuracy of complex teleoperation gestures is improved by 2.3% compared with other improved HMM algorithms. The experimental results show that the DAG-SVMs mixed HMM algorithm has a good effect on recognition for the gestures of teleoperation and it can perform gesture recognition accurately.