Reviewerly/exHarmony huggingface dataset:
Rediscovering the Maine Woods: Thoreau's Legacy in an Unsettled Land. Edited by John J. Kucich. (Amherst and Boston: University of Massachusetts Press, 2019. Pp. 248. $27.95 paper.)

March 01 2020 Rediscovering the Maine Woods: Thoreau's Legacy in an Unsettled Land Rediscovering the Maine Woods: Thoreau's Legacy in an Unsettled Land. Edited by John J.Kucich. (Amherst and Boston: University of Massachusetts Press, 2019. Pp. 248. $27.95 paper.) Daniel S. Malachuk Daniel S. Malachuk Daniel S. Malachuk is professor of English at Western Illinois University–Quad Cities and the author of Two Cities: The Political Thought of American Transcendentalism (2016). Search for other works by this author on: This Site Google Scholar Author and Article Information Daniel S. Malachuk Daniel S. Malachuk is professor of English at Western Illinois University–Quad Cities and the author of Two Cities: The Political Thought of American Transcendentalism (2016). Online Issn: 1937-2213 Print Issn: 0028-4866 © 2020 by The New England Quarterly2020The New England Quarterly The New England Quarterly (2020) 93 (1): 136–138. https://doi.org/10.1162/tneq_r_00804 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Search Site Citation Daniel S. Malachuk; Rediscovering the Maine Woods: Thoreau's Legacy in an Unsettled Land. The New England Quarterly 2020; 93 (1): 136–138. doi: https://doi.org/10.1162/tneq_r_00804 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentAll JournalsThe New England Quarterly Search Advanced Search This content is only available as a PDF. © 2020 by The New England Quarterly2020The New England Quarterly Article PDF first page preview Close Modal You do not currently have access to this content.
Natural image segmentation based on mutual information

Natural image segmentation plays an important role in the fields of image processing and computer vision. Image segmentation based on clustering is an important method in unsupervised image segmentation algorithms. But there are two problems with this type of approach. First, feature extraction is generally pixel-based, which results in poor segmentation results and boundary fitting. In order to solve this problem, it is proposed to introduce super pixel to be segmented image preprocessing. Second, the number of partitions is difficult to determine. Aiming at this problem, an energy difference based on mutual information is proposed, which can automatically determine the number of partitions. The experimental results on the standard database show that the proposed algorithm overcomes the above problems and achieves better experimental results.
A Sublinear-Regret Reinforcement Learning Algorithm on Constrained Markov Decision Processes with reset action

In this paper, we study model-based reinforcement learning in an unknown constrained Markov Decision Processes (CMDPs) with reset action. We propose an algorithm, Constrained-UCRL, which uses confidence interval like UCRL2, and solves linear programming problem to compute policy at the start of each episode. We show that Constrained-UCRL achieves sublinear regret bounds Õ(SA1/2T3/4) up to logarithmic factors with high probability for both the gain and the constraint violations.
Fully Convolutional Networks for Automatically Generating Image Masks to Train Mask R-CNN

This paper proposes a novel automatically generating image masks method for the state-of-the-art Mask R-CNN deep learning method. The Mask R-CNN method achieves the best results in object detection until now, however, it is very time consuming and laborious to get the object Masks for training, the proposed method is composed by a two-stage design, to automatically generating image masks, the first stage implements a fully convolutional networks (FCN) based segmentation network, the second stage network, a Mask R-CNN based object detection network, which is trained on the object image masks from FCN output, the original input image, and additional label information. Through experimentation, our proposed method can obtain the image masks automatically to train Mask R-CNN, and it can achieve very high classification accuracy with an over 90% mean of average precision (mAP) for segmentation.
A robust and flexible operating system compatibility architecture

Running applications for one operating system (OS) on another OS is useful in many cases. However, porting applications requires high development costs and running applications in a virtual machine poses resource sharing problems. A promising approach is to use an OS compatibility layer that converts the guest application's interface into the host one. Unfortunately, existing OS compatibility layers sacrifice either robustness or flexibility due to in-kernel subsystems or user-space-only implementations. This paper proposes a new architecture of OS compatibility layers that achieves robustness with almost user-level implementations while improving flexibility by exploiting standardized virtualization interfaces supported by most modern OSs. Our implementation of a Linux compatibility layer for macOS called Noah can run many of Ubuntu Linux binary applications, and a prototype implementation of a Linux compatibility layer for Windows confirmed the generality of our approach. Our experimental results demonstrated that the overhead of Linux kernel build time on Noah was 16%.
Efficient Semantic Segmentation through Dense Upscaling Convolutions

Semantic segmentation is the classification of each pixel in an image to an object, the resultant pixel map has significant usage in many fields. Some fields where this technology is being actively researched is in medicine, agriculture and robotics. For uses where the resources or power requirements are restricted such as robotics or where large amounts of images are required to process, efficiency can be key to the feasibility of a technique. Other applications that require real-time processing have a need for fast and efficient methods, especially where collision avoidance or safety may be involved. We take a combination of existing semantic segmentation methods and improve upon the efficiency by the replacement of the decoder network in ERFNet with a method based upon Dense Upscaling Convolutions, we then add a novel layer that allows the fine tuning of the decoder channel depth and therefore the efficiency of the network. Our proposed modification achieves 20-30% improvement in efficiency on moderate hardware (Nvidia GTX 960) over the original ERFNET and an additional 10% efficiency over the original Dense Upscaling Convolution. We perform a series of experiments to determine viable hyperparameters for the modification and measure the efficiency and accuracy over a range of image sizes, proving the viability of our approach.
Analyzing the Effects of Reinforcement Learning to Develop Humanoid Robots

Reinforcement learning is a flourishing machine learning concept that has greatly influenced how robots are designed and taught to solve problems without human intervention. Robotics is not an alien discipline anymore, and we have several great innovations in this field that promise to impact lives for the better. However, humanoid robots are still a baffling concept for scientists, although we have managed to develop a few great inventions which look, talk, work, and behave very similarly to humans. But, can these machines actually exhibit the cognitive abilities of judgment, problem-solving, and perception as well as humans? In this article, the authors analyzed the probable impact and aspects of robots and their potential to behave like humans in every possible way through reinforcement learning techniques. The paper also discusses the gap between 'natural' and 'artificial' knowledge.
Bahrain Government Information Security Framework

Information technology is perceived as an important enabler for government entities to accomplish their goals. The proliferation of electronic government services that can provide value for citizens and residents have pushed governments all over the world to adopt and deploy these services. However, governments have realized that it is critical to build proper defense to protect the information. Implementing information security by using international or national information security frameworks helps organizations to ensure the safeguard of information assets. This chapter reviews useful information security frameworks. Also, this chapter provides a proposed information security framework implemented in the Government of Bahrain, which is called CyberTrust Program. This framework was developed based on best practices and local resources and culture.
Resource sharing linear logic

Abstract In this paper, we introduce a new logic that we call ‘resource sharing linear logic (RSLL)’. In linear logic (LL), formulas without modality express some resource-conscious situation (a formula can be used only once); formulas with modality express a situation with unlimited resources. We introduce the logic RSLL in which we have a strengthened modality (S5-modality) that can be understood as expressing not only unlimited resources but also resources shared by different agents. Observing that merely strengthening the modality allows weakening axiom to be derivable in a Hilbert-style formulation of this logic, we reformulate RSLL as a logic similar to affine logic by a hypersequent calculus that has weakening as a primitive rule. We prove the completeness of the hypersequent calculus with respect to phase semantics and the cut-elimination theorem for the system by a syntactical method. We also prove the decidability of RSLL via a computational interpretation of RSLL, which is a parallel version of Kopylov’s computational model for LL. We then introduce an explicit counterpart of RSLL in the style of Artemov’s justication logic (JRSLL). We prove a realization theorem for RSLL via its explicit counterpart.
To The Problem Of Forming Creative Information-Communication Competence Of The Principal Of General Education School

The research is devoted to the actual problem of formation of creative information and communication competence of the principals of general education schools brought to life by the requests of the information society. The creative information and communication competency of the principal of general education school is the integration of the principal of the school for the creative use of modern information and communication technologies (ICT) into the professional and managerial activities. The respondents to this study were 102 principals of general education schools of the Republic of Uzbekistan. Data collection was carried out on the basis of a three parts questionnaire. The first part of the questionnaire collects information from respondents about knowledge in the field of modern ICT and the principles of the formation of an information and educational environment. The second part of the questionnaire contains information about the activities of the school principal in the information and educational environment. The third part of the questionnaire is aimed at elucidating the motives and attitude of the school principal towards innovation in the field of ICT. These profiles were summarized and analyzed as a percentage. The results show that the principals of general education schools have a low and medium level of creative information and communication competence, which confirms the hypothesis about the need for the formation of creative information and communication competence of the school principal in advanced training courses for leading cadres of Public Education.
Comparing Sentiment Analysis from Social Media Platforms – Insights and Implications

The ubiquitous presence of social media is exerting its influence on several institutions in society by both positive and negative ways. In this context it behooves researchers to understand the validity of the influence of social media and what it means to the functionality of the institutions in society which include healthcare, politics, education, marriage, etc. This paper presents results and insights obtained from comparing sentiment analysis applied to Twitter and YouTube data on a set of topics. The focus of this study was to observe differences among sentiments expressed on different social media platforms. In other words, was there any influence generated by the social media platform on the individual’s expression of sentiments. Additionally, we also developed an app to encourage citizen data scientists to search for a topic relevant to their area of interest and obtain sentiment analysis for that topic.
Generating Hand Contour Images with Information of the Fingers Curled using a Colored Glove

We have developed methods for generating hand contour images with information of the curled fingers using an original colored glove and a monocular RGB camera. In this paper, we report availability of our methods under LED lightning. In experiments, we have researched image generation ability considered with wrist rotation angles. From results, we conclude that the proposed methods provide the core technology for generating hand contour Images using the colored glove.
A View on the Blockchain as a Solution to the Healthcare Industry: Challenges and Opportunities

Due to the advancement of technology, the large volume of digital data increases rapidly in every field. One of the most sensitive digital data field is the healthcare system. Healthcare information contains high delicate private data, and it needs to be shared among the peoples such as health specialists, pharmacist, family members, insurance companies, hospitals etc. Healthcare organization have not only their patient’s medical details which include insurance and financial account information. The breach of healthcare information is one of the most important concern nowadays. Recently, the research studies are going on Blockchain technology to secure the health care system. Blockchain technology is an open and distributed online database system which comprises the list of blocks that are related to each other. This paper summarizes the impact of blockchain in the healthcare system and enfranchises the privacy of a patient’s health data. This paper also addresses the issues, challenges, and research onset of blockchain in the area of the healthcare system.
A Deep Learning Strategy to Sentiment Analysis on Low-volume Dataset

The potency of Deep Learning techniques builds on the amount of labeled data accessible for training Deep Neural model. The lack of training data becomes a common barrier for using Deep Learning technique to perform different NLP task. Transfer learning or Domain adaptation is a required technique to settle the deficiency of categorized reviews by studying a model's ability of transferring knowledge across domains to perform Sentiment Classification. In this paper, we have suggested a transfer learning approach stand on a combination of Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) model, designated as C_LSTM. This proposed model is used to resolve the issue of data insufficiency in a particular domain by introducing a large-scale supplementary dataset from more or less similar domains for knowledge transferring. These experimental results of intra-domain and inter-domain Sentiment Classification on multi-domain product review indicate that utilizing a high-volume corpus on related domain can remarkably boost the accuracy.
PREPARATORY GROUP-POSSIBILITY FOR IMPLEMENTING THE PRINCIPLE OF ADMINISTRATION IN THE EDUCATION PROCESS IN MATHEMATICS BETWEEN GG AND INITIAL STAGE

The new century is very different. On the basis of interdependence, humanity has a unique opportunity for industrial modernization, alongside the dynamics of technology and communication development, information entropy, the volcanic eruption of visual and sound culture. This necessitates modernization in the education system /digital competences, transversal skills and good continuity between different stages of education /, triggered by intense changes in technology and the way of life of modern children. In kindergarten and primary education, the foundations for developmental learning are laid, where knowledge and skills in mathematics education are to be acquired and communicated that are applicable in everyday life.
An Image Compression Framework Based on Multi-scale Convolutional Neural Network for Deformation Images

Along with the development of deep learning, convolutional neural networks (CNN) have become more and more widely used in the field of image compression, and have made image compression technology significantly improved on performance and cost. In order to make the image better preserve the details and texture of the original image while compressing, we propose a novel method to optimize image compression. We first focus on slight deformation image which is changed by original image, which means that we preserve the features of image detail information when the bits are not increased, and then the deformed image is transmitted to our network framework to achieve image compression and the reconstruction process. In our system, first, a multi-scale convolutional neural network is used to learn the best compact representation from the input image to achieve the purpose of extracting multiscale structural information of natural images. The result of compact representation is then encoded and decoded by a conventional image codec. Finally, a reconstructed convolutional neural network is used for reconstructing the decoded image with high quality and accurate quality at the decoding end. Experimental results demonstrate that our network is superior to most existing methods and can improve image quality with more visual detail.
PGSD STUDENTS’ CRITICAL THINKING SKILS DEVELOPMENT THROUGH MEDIA LITERACY IN THE GLOBALIZATION ERA

The author wrote this article because she was motivated by the case of the declining ability of critical thinking and literacy of students in the growth of technology and education in the era of globalization. Based on rank proposed by Webometrics website, universities in Indonesia are mostly under universities in Singapore and Thailand. This is the reason why the learners cannot develop his or her potential to compete in the era of globalization. Educators often forget that the purpose of education is to change the behavior of learners to be better. Teacher training students as social, individual beings and citizens must develop themselves to live in the midst of society. Since the situation always changes, particularly in the political, defence, economic, society, and cultural conditions, exploring knowledge, developing critical thinking, and being able to deal with any possibilities in life are needed to achieve. Teacher training students as the future educator should improve their critical thinking through media literacy to face globalization era. Technology can be utilized to access information and knowledge related to education field.
Mechanist philosopy, concept of "ownership" and legal status of human body parts

The article deals with the rise of philosophical concept of mechanism, especially in the period of Molecular Biology development and its impact on legal approach to human body. The new challenges such as cloning or human genome editing are key for the dominating concept of mechanism, however, there are some limits as well. The basic question seems to be how far we "own" our bodies. There is also an increasing role of parents by the "enhancing" of their children. All this trends seem to be influenced by mechanism, however it creates a threat for other rights of the "enhanced" children.
The Effective Use of the Histogram of Principal Oriented Gradients for Natural Arabic Image Character Recognition

Nowdays, recognizing characters from natural scene image is an important task in various applications of pattern recognition. In fact, the automatic recognition of characters from natural scenes allows providing many information for peoples such as the language translation from smartphone or the address identification from a camera transported in a vehicle. Hence, most of the systems are implemented for recognizing the English language by employing different robust descriptors and classifiers. However, few works are dedicated for the Arabic characters. Also, this paper aims to focus on the use of a recent descriptor namely the Histogram of Principal Oriented Gradients (HPOG) that is used in our best knowledge for the first time in character recognition. For classification, the One Class-Principal Component Analysis (OC-PCA) Classifier is used for recognizing Arabic characters in natural scene. For evaluating the performance of the HPOG associated to the OC-PCA, experimental results conducted on a standard Arabic dataset show the effectiveness of the proposed system against the state-of-art.
Software Agents Meet Heterogeneous Ecosystems of Services and Resources for Controlling the Internet of Things

Service- and resource-oriented technologies have achieved a widely level of popularity and acceptance for the development of distributed systems. Most of the current processes executed on web, mobile and pervasive applications are generally developed by invoking heterogeneous services and resources through different models. This paper presents an analysis of the main approaches originated from the integration of Multiagent Systems (MASs) with service and resource architectures. The behavior of agents that explore and consume distributed services and resources on heterogeneous ecosystems was analyzed empirically in order to define a taxonomy of agents. Furthermore, a software tool for composing processes oriented to control IoT scenarios based on invoking services and resources is described. The experiments carried out show that the use of the artifice agent&services/resources is technically feasible and contributes significantly to the abstraction of the complexity of actions carried out in IoT environments.
A Study on a Combination of Sparse Coding and Dictionary Learning Algorithms for Sparse Super-Resolution

This study proposed a method based on sparse coding for super-resolution, which acquires images from low resolution to high resolution. The method combines two parts: Dictionary learning and sparse coding. This combination typically affects the overall performance. The optimization of the algorithms for each part has been extensively studied, but these combinations were rarely studied. This study analyzes how these two parts of the algorithm interact with each other and finds the optimum combination to improve the accuracy of the dictionary learning algorithm.
Reversible Data Hiding Based on Improved Rhombus Prediction Method

Reversible data hiding(RDH) is a research hotspot in the field of information hiding. Among them, RDH based on histogram shift(HS) is a high performance algorithm. Accurate pixel prediction can reduce image distortion while maintaining high capacity. Therefore, this paper proposes an RDH algorithm based on the improved rhombus prediction method. Experiments show that the improved rhombus prediction method can predict pixels more accurately, and the generated prediction error histogram is more compact and clear. The proposed RDH algorithm has a higher embedding capacity and a lower distortion rate.