Reviewerly/exHarmony huggingface dataset:
Segmentation with Active Contours

Active contours (also known as snakes) have shown their ability to introduce regularity on image segmentation.In contrast with level-set approaches, the active contours techniques based on a contour parameterization are able to maintain the initial topology of the area of interest.For this reason, it has been used in recent medical research for diaphragm segmentation.Most of the on-line codes for 2D/3D segmentation, as well as built-in Matlab toolboxes are based on level-set methods.Moreover, in the literature, the implementation details of active contours methods with meshes in three dimensions are tight, making tedious any reproduction of these techniques.In this paper, we give some details of the implementation of active contours in 2D/3D with meshes, especially about the choice of the use of a 2D/3D mesh and its refinement.We also explore the choice of the parameters with a quantitative study of their influence on the segmentation results.The 3D segmentation method has been applied to CT scan images of the lungs.
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion

We develop theory and algorithms for average-reward on-policy Reinforcement Learning (RL). We first consider bounding the difference of the long-term average reward for two policies. We show that previous work based on the discounted return (Schulman et al., 2015; Achiam et al., 2017) results in a non-meaningful bound in the average-reward setting. By addressing the average-reward criterion directly, we then derive a novel bound which depends on the average divergence between the two policies and Kemeny's constant. Based on this bound, we develop an iterative procedure which produces a sequence of monotonically improved policies for the average reward criterion. This iterative procedure can then be combined with classic DRL (Deep Reinforcement Learning) methods, resulting in practical DRL algorithms that target the long-run average reward criterion. In particular, we demonstrate that Average-Reward TRPO (ATRPO), which adapts the on-policy TRPO algorithm to the average-reward criterion, significantly outperforms TRPO in the most challenging MuJuCo environments.
Association Analysis of Private Information in Distributed Social Networks Based on Big Data

As people’s awareness of the issue of privacy leakage continues to increase, and the demand for privacy protection continues to increase, there is an urgent need for some effective methods or means to achieve the purpose of protecting privacy. So far, there have been many achievements in the research of location-based privacy services, and it can effectively protect the location privacy of users. However, there are few research results that require privacy protection, and the privacy protection system needs to be improved. Aiming at the shortcomings of traditional differential privacy protection, this paper designs a differential privacy protection mechanism based on interactive social networks. Under this mechanism, we have proved that it meets the protection conditions of differential privacy and prevents the leakage of private information with the greatest possibility. In this paper, we establish a network evolution game model, in which users only play games with connected users. Then, based on the game model, a dynamic equation is derived to express the trend of the proportion of users adopting privacy protection settings in the network over time, and the impact of the benefit-cost ratio on the evolutionarily stable state is analyzed. A real data set is used to verify the feasibility of the model. Experimental results show that the model can effectively describe the dynamic evolution of social network users’ privacy protection behaviors. This model can help social platforms design effective security services and incentive mechanisms, encourage users to adopt privacy protection settings, and promote the deployment of privacy protection mechanisms in the network.
Generating 3D Facial Expressions with Recurrent Neural Networks

Learning-based methods have proved effective at high-quality image synthesis tasks, such as content-preserving image rendering with different style, and the generation of new images depicting learned objects. Some of the properties that make neural networks suitable for such tasks, for example, robustness to the input’s low-level feature, and the ability to retrieve contextual information, are also desirable in 3D shape domain. During last decades, data-driven methods have shown successful results in 3D shape modeling tasks, such as human face and body shape synthesis. Subtle, abstract properties on the geometry that are instantly detected by our eyes but are nontrivial to synthesize have successfully been achieved by tuning a shape model built from example shapes. Recent successful learning techniques, e.g., deep neural networks, also exploit this shape model, since the regular grid assumption with 2D images does not have a straightforward equivalent in the common shape representation in 3D, thus do not easily generalize to 3D shapes. Here, we concentrate on the 3D facial expression generation task, an important problem in computer graphics and other application domains, where existing data-driven approaches mostly rely on direct shape capture or shape transfer. At the core of our approach is a recurrent neural network with a marker-based shape representation. The network is trained to estimate a sequence of pose change, thus generate a specific facial expression, by using a set of motion-captured facial expression sequences. Our technique promises to significantly improve the quality of generated expressions while extending the potential applicability of neural networks to sequence of 3D shapes.
Social Networks as a Platform for Academic Interaction: Possibilities and Challenges for Indian Academic Libraries

Social media encompasses many forms such as social networking sites like blogs, vlogs, instant messaging and virtual communities. Among the social media, Facebook appeared as a major platform and emerged as a winner because of its large user base. The rapid rise of social media provides numerous possibilities for academic institutions to exploit this media to extend its learning and teaching endeavors. Nonetheless, this has no boundary; it is suitable for administrators, managers, learners and teachers in academic institutions too. The Social networking is a medium that lets the users to interact and work collaboratively with others, including the ability to browse, search, invite friends to connect and interact across the globe. Several opportunities have been emerged for academic libraries in augmenting their basic objectives like identifying right users communities, their need and proactive teaching learning services to its users. Social software in the web 2.0 world not only enhances the practical usability in the library but also helps the diminishing librarian’s role through value addition to profession itself. In this paper, select cases of social academic networks are described and different perspectives have been given on how academic libraries are participating in this massive social networking drive. Also, an attempt has been made to analyze social media presence of select Indian academic libraries.
PMAB: A Public Mutual Audit Blockchain for Outsourced Data in Cloud Storage

With the rapid growth of data, limited by the storage capacity, more and more IoT applications choose to outsource data to Cloud Service Providers (CSPs). But, in such scenarios, outsourced data in cloud storage can be easily corrupted and difficult to be found in time, which brings about potential security issues. Thus, Provable Data Possession (PDP) protocol has been extensively researched due to its capability of supporting efficient audit for outsourced data in cloud. However, most PDP schemes require the Third-Party Auditor (TPA) to audit data for Data Owners (DOs), which requires the TPA to be trustworthy and fair. To eliminate the TPA, we present a Public Mutual Audit Blockchain (PMAB) for outsourced data in cloud storage. We first propose an audit chain architecture based on Ouroboros and an incentive mechanism based on credit to allow CSPs to audit each other mutually with anticollusion (any CSP is not willing to help other CSPs conceal data problems). Then, we design an audit protocol to achieve public audit efficiently with low cost of audit verification. Rigorous analysis explains the security of PMAB using game theory, and performance analysis shows the efficiency of PMAB using the real-world dataset.
Introduction to VMware Workstation

VMware Workstation Pro is one of the most popular end-user desktop virtualization solutions used by enterprise IT personnel. It allows IT engineers to build and test real-world-like virtual machines and network devices from their own PC/laptop. Learning to build a fully functioning and integrated virtual lab on a single computer and master Python network automation basics on a single PC is this book’s underlying goal. This chapter is designed to teach you the basics of the most popular and versatile VMware product, VMware Workstation 15 Pro, and basic virtualization concepts. After reading this chapter, you will gain the following knowledge: you’ll understand the difference between Type-1 and Type-2 hypervisors, understand various IT vendors offering different desktop virtualization solutions, learn how to install VMware Workstation, learn how to perform general administration on VMware Workstation, and understand how VMware Workstation’s network adapters operate.
Predicting Numerals in Natural Language Text Using a Language Model Considering the Quantitative Aspects of Numerals

Numerical common sense (NCS) is necessary to fully understand natural language text that includes numerals.NCS is knowledge about the numerical features of objects in text, such as size, weight, or color.Existing neural language models treat numerals in a text as string tokens in the same way as other words.Therefore, they cannot reflect the quantitative aspects of numerals in the training process, making it difficult to learn NCS.In this paper, we measure the NCS acquired by existing neural language models using a masked numeral prediction task as an evaluation task.In this task, we use two evaluation metrics to evaluate the language models in terms of the symbolic and quantitative aspects of the numerals, respectively.We also propose methods to reflect not only the symbolic aspect but also the quantitative aspect of numerals in the training of language models, using a loss function that depends on the magnitudes of the numerals and a regression model for the masked numeral prediction task.Finally, we quantitatively evaluate our proposed approaches on four datasets with different properties using the two metrics.Compared with methods that use existing language models, the proposed methods reduce numerical absolute errors, although exact match accuracy was reduced.This result confirms that the proposed methods, which use the magnitudes of the numerals for model training, are an effective way for models to capture NCS.
The cooccurrence of linguistic structures

The study of cooccurrences, i. e. the analysis of linguistic units that occur together, has had a profound impact on our view of language. Not only has it contributed greatly to the insight that semi-preconstructed phrases and item-specific knowledge are central to how language works, but it has also led to improved dictionaries and teaching materials. Cooccurrences of various linguistic items have been studied under a variety of names, e. g. collocation, colligation or collostruction. While there are well-understood and fully worked out statistical models for the analysis of cooccurrences of pairs of words, no such model exists for cooccurrences of larger linguistic structures. This situation is remedied by the current work.Building on the well-understood 2 × 2 contingency tables and a graph-based representation of linguistic structures, we develop the generalized cooccurrence model, an explicit formal model for the statistical analysis of cooccurrences of arbitrary linguistic structures. Existing methods for the analysis of two-word cooccurrences and for collostructional analysis are shown to be simply special cases of the generalized cooccurrence model.
An Overview of Healthcare Information Technologies Used to Combat the COVID-19 Pandemic in China

This article describes the rapid development and deployment of healthcare information technologies (HIT) in response to COVID-19 from late January through March 2020 in China. It outlines the broad scope and in-depth penetration of various products and services, as well as the impacts that these products/services had in reaching the mass population for effective information dissemination, contact tracing, early diagnosis, virtual care, teleconsultation, social distancing and community outreach and support. The article also describes the rapid development of new infrastructures and the exponential growth of usage in the various technologies. Combined with other measures, HIT have played a crucial role in China through galvanizing public response in solidarity and achieving a massive scale of contact-tracing and interventions in a personalized manner.
Towards training time attacks for federated machine learning systems

Federated machine learning systems have gained more and more attention and popularity in both academia and industry because they can obtain a shared model among multiple parties without explicitly sharing the training data. Such a system is believed to have a good potential of protecting data privacy compared with the traditional machine learning frameworks. On the other hand, training time attacks are a procedure of purposefully modifying training data, hoping to manipulate the behavior of the corresponding trained system during test time. DeepConfuse, for instance, is one recent advance in generating adversarial training data with high efficiency. In this work, we extend the DeepConfuse framework so that it can be used in federated machine learning. This is the first training time attack for a federated learning system. The empirical results showed that the federated learning system is even more vulnerable under the DeepConfuse attack in terms of $\delta$-accuracy loss.
Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression

This paper develops a new approach to learning high-dimensional linear structural equation models (SEMs) without the commonly assumed faithfulness, Gaussian error distribution, and equal error distribution conditions. A key component of the algorithm is componentwise ordering and parent estimations, where both problems can be efficiently addressed using '1-regularized regression. This paper proves that sample sizes n = (d2 log p) and n = (d2p2=m) are sufficient for the proposed algorithm to recover linear SEMs with sub- Gaussian and (4m)-th bounded-moment error distributions, respectively, where p is the number of nodes and d is the maximum degree of the moralized graph. Further shown is the worst-case computational complexity O(n(p3 + p2d2)), and hence, the proposed algorithm is statistically consistent and computationally feasible for learning a high-dimensional linear SEM when its moralized graph is sparse. Through simulations, we verify that the proposed algorithm is statistically consistent and computationally feasible, and it performs well compared to the state-of-the-art US, GDS, LISTEN and TD algorithms with our settings. We also demonstrate through real COVID-19 data that the proposed algorithm is well-suited to estimating a virus-spread map in China. © 2021 Microtome Publishing. All rights reserved.
Scalable Normalizing Flows for Permutation Invariant Densities

Modeling sets is an important problem in machine learning since this type of data can be found in many domains. A promising approach defines a family of permutation invariant densities with continuous normalizing flows. This allows us to maximize the likelihood directly and sample new realizations with ease. In this work, we demonstrate how calculating the trace, a crucial step in this method, raises issues that occur both during training and inference, limiting its practicality. We propose an alternative way of defining permutation equivariant transformations that give closed form trace. This leads not only to improvements while training, but also to better final performance. We demonstrate the benefits of our approach on point processes and general set modeling.
Contextual Information Helps Understand Messages Written with Textisms

The present study investigated the influence of the use of textisms, a form of written language used in phone-mediated conversations, on the cognitive cost of French participants in an online conversation. Basing our thinking on the relevance theory of Sperber and Wilson, we tried to assess whether knowing the context and topic of a conversation can produce a significant decrease in the cognitive cost required to read messages written in textism by giving additional clues to help infer the meaning of these messages. In order to do so, participants played the judges in a Turing test between a normal conversation (written with the traditional writing style) and a conversation in which the experimenter was conversing with textisms, in a random order. The results indicated that participants answered messages written in textism faster when they were in the second conversation. We concluded that prior knowledge about the conversation can help interpret the messages written in textisms by decreasing the cognitive cost required to infer their meaning.
Reactive navigation under a fuzzy rules-based scheme and reinforcement learning for mobile robots

Robot navigation allows mobile robots to navigate among obstacles without hitting them and reaching the specified goal point. In addition to preventing collisions, it is also essential for mobile robots to sense and maintain an appropriate battery power level at all times to avoid failures and non-fulfillment with their scheduled tasks. Therefore, selecting the proper time to recharge the batteries is crucial to address the navigation algorithm design for the robot’s prolonged autonomous operation. In this paper, a machine learning algorithm is used to ensure the extended robot autonomy based on a reinforcement learning method combined with a fuzzy inference system. The proposal enables a mobile robot to learn whether to continue through its path toward the destination or modify its course on the fly, if necessary, to proceed toward the battery charging station, based on its current state. The proposal performs a flexible behavior to choose an action that allows a robot to move from a starting to a destination point, guaranteeing battery charge availability. This paper shows the obtained results using an approach with thirty-six states and its reduction with twenty states. The conducted simulations show that the robot requires fewer training epochs to achieve ten consecutive successes in the fifteen proposed scenarios than traditional reinforcement learning methods exhibit. Moreover, in four scenarios, the robot ends up with a battery level above 80%, that value is higher than the obtained results with two deterministic methods.
Do Foreign Aid and Concessional Debts Attract FDI Flows into Recipient Countries?

Poverty in Africa has long been a big concern that tremendous worldwide anti-poverty efforts have been poured into African countries. Suggested by the Big Push theory, outside capitals, such as foreign direct investment (FDI), foreign aid and concessional debts, should be encouraged to flow into poor countries to stimulate capital accumulation. This paper aims to examine if foreign aid and concessional debts can significantly attract FDI inflows to underdeveloped African countries. The study sample covers fifty African countries during the period 1993-2011. This study highlights the heterogeneity problems existing in the recipient countries, which varies with each other in terms of income levels, financial creditworthiness, and institutional conditions. For this reason, the sample countries are divided into two groups, IBRD and IDA countries, according to the World Bank's lending categories. Based on the F-test, Breusch-Pagan test, and Hausman test results, the random effects model is more efficient than pooled OLS and fixed effect model in the panel data analysis. In addition, this study applies GMM methodology to address the endogeneity problem existing in panel data analysis. Based on the Hansen test results, lagged FDI inflows and exogenous variables are justified to be introduced into the SYS-GMM as instruments. As expected, the main results generated from the analysis for IBRD countries and IDA countries are fairly diverse. Results developed by random effects model show that foreign aid and concessional debts have significant positive effects on FDI inflows for IDA countries. Neither foreign aid nor concessional debts are significantly related with FDI inflows for IBRD countries. The GMM generates similar results for IDA countries. In addition, the results suggest that previous FDI inflows have significant impact on current decisions. Hence, as suggested by the results, foreign help and capital resources should be diversified and allocated according to development levels of different countries. Foreign aid and concessional loans should be encouraged to invest in IDA countries so that FDI can be attracted to flow into recipient countries. For IBRD countries with higher level of development, economic growth plays a more important role in attracting FDI inflows.
The Impact of Active Learning Algorithm on a Cross-lingual model in a Persian Sentiment Task

One of the most challenging problems that we may face in natural language processing tasks is the lack of annotated training datasets. In this paper our goal is to consider the impact of Active Learning algorithm on a cross-lingual model in sentiment analysis task on Persian language which is known as a low-resource language. Cross-lingual model trains a model by using a rich-resource language like English as a source language and apply it to a low-resource language, in this way the dependency to training datasets is decreased. Also using Active Learning strategy helps us to improve the functionality of our model by selecting most representative samples. Since labeling data is expensive and time consuming, by selecting the machine desirable data we can reduce the amount of labeled data required for our tasks. To do this we can select data which classifier is the least confident about them. When they are chosen, a user is asked to labeled them. There are lots of methods and factors to choose the appropriate data for Active Learning strategy. In the end these methods help our classifier to gain more knowledge about samples and work more properly.
Comparative Study of Data Clustering Algorithms and Analysis of The Keywords Extraction Efficiency: Learner Corpus Case

The paper focuses on the task of clustering essays produced by ESL (English as a Second Language) learners. The data was taken from a learner corpus REALEC. The division of texts by certain characteristics can be useful to speed up the analysis of a single corpus or access to the necessary sections of a large number of documents. The study discusses not only some existing approaches to clustering text data, as well as the possibility of clustering texts produced by ESL learners, but also ways to extract keywords in order to determine the topic of the essays in each group.