Reviewerly/exHarmony huggingface dataset:
A Study on the Effects of Amy E. Herman’s Objective Observation Education on the Case Study ofInterior Architecture

The purpose of this study is to present the characteristics of subjective observation and objective observation by applying the objective observation method of Amy E. Herman in the process of analyzing the case of interior architecture, and to study the learning effect of students’ observation, analysis and narrative activities. First, to analyze the concepts and types of observational learning and to emphasis necessity of linguistic observational learning as a way to solve problems that may occur when applied to interior architecture case analysis. Second, as a linguistic observational study, this study reinterprets Amy E. Herman’s objective observation methods to observe art cases and interior architectural cases, and analyzes the effect of objective observation method education on case analysis. The results of the study are as follows. 1) Students gave positive evaluations to self-directed observation learning using objective observation. This is the result of understanding that the difference between unconscious and conscious observation methods can affect case analysis. 2) Objective observation improved students’ ability to analyze invisible functions and program information. This result shows that observational analysis elements based on linguistic observation method can perceive much more than visual observation of interior architectural information in the case study. 3) Students have improved their ability to describe the physical conditions of interior architectural cases based on mathematical facts. In the observational experiments, the students objectively recorded physical conditions such as the size, height, furniture, and materials of the interior architectural cases. This study is valuable to make an experiential learning model by applying objective observation method to interior architecture learning. Objective observation is to provide a learning process that allows students to self-directly analyze and feel and realize interior architectural information.
A New Network for Particle Filtering of Multivariable Nonlinear Objects †

In this paper, a new object in the form of a theoretical network is presented, which is useful as a benchmark for particle filtering algorithms designed for multivariable nonlinear systems (potentially linear, nonlinear, and even semi-Markovian jump system). The main goal of the paper is to propose an object that potentially can have similar to the power system grid properties, but with the number of state variables reduced twice (only one state variable for each node, while there are two in the case of power systems). Transition and measurement functions are proposed in the paper, and two types of transition functions are considered: dependent on one or many state variables. In addition, 10 types of measurements are proposed both for branch and nodal cases. The experiments are performed for 14 different, four-dimensional systems. Plants are both linear and highly nonlinear. The results include information about the state estimation quality (based on the mean squared error indicator) and the values of the effective sample size. It is observed how the higher effective sample size resulted in the better estimation quality in subsequent cases. It is also concluded that the very low number of significant particles is the main problem in particle filtering of multivariable systems, and this should be countered. A few potential solutions for the latter are also presented.
MST Parser for Telugu Language

Maximum spanning tree (MST) parser is a graph built dependency parser and also finds the k-best spanning trees based on the scores. These spanning trees are constructed based on the global optimization approaches. However, it selects the tree which has the highest score and the method uses Eisner algorithm to find the best spanning tree. Hence, the time complexity has been significantly reduced from O(n5) to O(n3). In this paper, we discuss some of the projective and non-projective approaches on how to apply MST parser to Telugu language. In addition to this, we also present a methodology to apply Telugu treebank to MST parsing. Finally, the results are obtained and found to be suggestively improved.
Cache Based Side-Channel Attacks

In the current era of computation, multiple processor cores are deployed in devices that range from smart-phones, laptops, desktops, servers, and cloud-based systems. Though, innovations in the world of hardware and computer architecture have resulted in faster computation in terms of better performance, a lot of sensitive data that is stored and processed by these devices can get leaked through various hardware components, such as branch predictors, caches, Translation lookaside buffers (TLBs), hardware prefetchers, Dynamic Random Access Memory (DRAM) controllers, and the DRAM. Basically, these hardware components become side-channels and leak information through attacks known as side-channel attacks. A recent and famous attack that was of similar taste was the famous Spectre and Meltdown attacks. This chapter discusses some of the famous side-channel attacks on on-chip SRAM cache memories and off-chip DRAM memory.
RARE BOOKS COLLECTION OF THE CENTRAL SCIENTIFIC LIBRARY OF THE IRKUTSK SCIENTIFIC CENTER OF SB RAS,AS AN INFORMATION SOURCE OF SCIENCE

The Central Scientific Library of the Irkutsk Scientific Center SB RAS has an extensive collection of literature on various topics of scientific research of academic institutions of Irkutsk and the Irkutsk region. This library is the successor of the Library of the East Siberian Branch of the USSR Academy of Sciences. With the organization of the Branch its own scientific library was being created. Acquisitions to the library stocks were carried out by its first head A. S. Chumicheva. The first books of the library were books from the exchange stocks of the Library of the Academy of Sciences of the USSR (Leningrad), special libraries’ sector of the Academy of Sciences of the USSR (Moscow); books from other institutions’ libraries all over the country; trophy books from Germany; books from personal libraries of scientists, rare book stores; since 1958 books began to come to hand from the State Public Scientific and Technological Library of the Siberian Branch of the USSR Academy of Sciences (Novosibirsk). The first library accumulations were the source of material that Russian science had in the period of its making on the Irkutsk soil, and an especially stored part of the library stock. There is a currently increasing interest of specialists in rare books’ collections organization, storage and use. A particularly valuable publication is the book, dated 1676, the life-time publication of the French geographer Pierre Duval (1618–1683). In 2019 its ex-libris was studied. They managed to determine the owner of the book and the history of the library. The ex-libris is heraldic, coat of arms belongs to Franciszek Velepolsky (1732–1809), and the book is from the Pinchuv Majorata library. In the library collections there are publications by prominent domestic and foreign scientists of the XIX–XX centuries, including Nobel laureates; books from personal libraries with notes of owners; autographed books, books with dedicatory inscriptions, bookplates. Works of scientific organizations of the XIX–XX centuries, reports of expeditions, and works of scientists about Siberia are of interest. Most of academic journals are presented starting since the first years of their publication. The collection of rare books is being replenished thanks to readers’ donations.
Classification Method For Mitigating Phishy E-Mails

Phishing is one of the most popular forms of social engineering attack that involves use of emails or text message from known associate or friend that has links or attachments, which automatically downloads a spyware unnoticed when clicked.This epidemic has been in existence for ages and pose a big threat to IT security, individual and organizations reputation.Thus, this study aims at developing an email application embedded with algorithm to categorize incoming emails into high, medium and low threats.The development process followed object-oriented analysis and design methodology (OOADM), while the system was developed with PHP (Hypertext Preprocessor) and MySQL (Structured Query Language).The validation of the developed application suggests that when implemented, it could mitigate phishy emails.
Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning

Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation. Meanwhile, in order to locate essential information for dialogue summarization/extraction, the pretraining process enables external knowledge integration. The proposed fine-tuned pretraining mechanism is comprehensively evaluated via three different dialogue datasets along with a number of downstream dialogue-mining tasks. Result shows that the proposed pretraining mechanism significantly contributes to all the downstream tasks without discrimination to different encoders.
A Verification Framework of Neural Processing Unit for Super Resolution

Deep neural networks (DNNs) computing has been rapidly moved from cloud to edge devices in many domains such as object recognition, natural language processing and video/image quality enhancement, etc. Neural network processing unit (NPU) leads a pervasive trend to accelerating neural network computation on edge devices. A NPU is a processor that is specialized for DNN algorithm computation. [4][5] Recently, a DNN based real-time video super resolution method which provides higher resolution video experience such as UHD from lower resolution input video, SD/HD/FHD etc.[2] However, it is difficult to apply DNN based super resolution because the algorithm consume high external memory bandwidth caused by huge input, intermediate and output data. In this paper, we describe our NPU architecture, called S-PIE (Samsung Perceptual Inference Engine) for super resolution application, and report our experience so far, including various components used for a verification framework of S-PIE.
Additive Scoring Rules for Discrete Sample Spaces

In this paper, we develop strictly proper scoring rules that may be used to evaluate the accuracy of a sequence of probabilistic forecasts. In practice, when forecasts are submitted for multiple uncertainties, competing forecasts are ranked by their cumulative or average score. Alternatively, one could score the implied joint distributions. We demonstrate that these measures of forecast accuracy disagree under some commonly used rules. Furthermore, and most importantly, we show that forecast rankings can depend on the selected scoring procedure. In other words, under some scoring rules, the relative ranking of probabilistic forecasts does not depend solely on the information content of those forecasts and the observed outcome. Instead, the relative ranking of forecasts is a function of the process by which those forecasts are evaluated. As an alternative, we describe additive and strongly additive strictly proper scoring rules, which have the property that the score for the joint distribution is equal to a sum of scores for the associated marginal and conditional distributions. We give methods for constructing additive rules and demonstrate that the logarithmic score is the only strongly additive rule. Finally, we connect the additive properties of scoring rules with analogous properties for a general class of entropy measures.
Trajectory via-point generation for autonomous mobile manipulation using 3D LiDAR data

In this work, an approach to generating a set of via points for use in manipulator trajectory path planning is presented. The approach was developed for use on a robotic underground mining system, particularly for the task of autonomous application of a sprayable concrete called shotcrete. A LiDAR (light detection and ranging) scanner on a nodding head produces point clouds that are used as the input for the via-point selection algorithm. The algorithm generates a set of position and orientation via points that the manipulator must follow to perform the shotcreting task. The developed algorithm has been successfully tested on an autonomous mobile-manipulator system in a scaled mock-up of an underground mine. The main advantage of this algorithm is the ability to generate via points for any section of an underground mine in any position relative to the robot.
Action for Better Prediction

Model-based curiosity combines active learning approaches to optimal sampling with the information gain based incentives for exploration presented in the curiosity literature. Existing model-based curiosity methods look to approximate prediction uncertainty with approaches which struggle to scale to many prediction-planning pipelines used in robotics tasks. We address these scalability issues with an adversarial curiosity method minimizing a score given by a discriminator network. This discriminator is optimized jointly with a prediction model and enables our active learning approach to sample sequences of observations and actions which result in predictions considered the least realistic by the discriminator. We demonstrate progressively increasing advantages as compute is restricted of our adversarial curiosity approach over leading model-based exploration strategies in simulated environments. We further demonstrate the ability of our adversarial curiosity method to scale to a robotic manipulation prediction-planning pipeline where we improve sample efficiency and prediction performance for a domain transfer problem.
Transfer learning approach for intraoperative pixel-based diagnosis of colon cancer metastasis in a liver from hematoxylin-eosin stained specimens

Development of computer-aided diagnosis (CAD) systems is motivated by reduction of the workload on the pathologist that is increasing steadily. Among approaches upon which CAD-based systems are built, deep learning (DL) methods seem to be well suited for image analysis in digital pathology. However, DL networks include a large number of parameters and that requires a large annotated training dataset. Unfortunately, probably the biggest problem in digital pathology using machine learning methods is a small number of annotated images. That is especially true in intraoperative tissue analysis which coincides with the topic of the present paper: intraoperative CAD-based diagnosis of metastasis of colon cancer in a liver from hematoxylin-eosin (H and E) stained frozen section. To cope with the insufficiency of training images we adopt a transfer learning approach using the Nested UNet architecture. For better diagnostic performance, the trained model predicted pixels multiple times for different striding levels using the sliding window strategy. Threshold optimization using balanced accuracy score showed the validity of such an approach as balanced accuracy has increased significantly. When compared to often used UNet with VGG16 backbone, Nested UNet model with DenseNet201 backbone performs better on our dataset for both balanced accuracy metric and F1 score.
A Message Length Verification of Modern Messaging Systems

Nowadays, there has been a rapid increase in the variety and popularity of messaging systems and social networks.It is imperative to consider the effect and impact of the number of words feature on the verification process for modern messaging systems such as Twitter, Facebook, SMS and Email.Given the volume of text is often a restricted factor (due to the nature of messaging systems), key to this investigation is a better understanding of what length of message is required to improve performance.A large historical dataset containing 50 participants, the four datasets containing a large number of messaging system samples (4539 samples for Facebook, 13,616 for Twitter, 6538 for Email and 106,359 for Text message), the best performance was for Text messages, with an EER of 7.6% if the number of words was more than nine; followed by Email with an EER of 14.9% if the number of words was between 25 to 60; then, Twitter tweets, with an EER of 22.5% if the number of words was less than ten.Finally, the Facebook platform with an EER of 31.9% if the number of words was over 11.
Cloud Computing Security

The applications and software using cloud computing as a base have significantly increased, and cloud computing is nowadays developed as a scalable and cost-effective technique. It facilitates many features in the computing environment like resource pooling, multi-tenancy, virtualization, etc. The cloud environment has led to the proliferation of various attacks that affect the security of applications and software developed by using cloud services and also cloud user's data. Data security is the main concern in cloud computing because it is stored in data centers provided by service providers. The chapter presents the background history of cloud computing and different challenges faced by the cloud environment, especially in terms of providing security for customer data stored in data centers. Authors also discuss the case studies on Microsoft Azure (i.e. a cloud platform) and Aneka (i.e. a cloud development and management platform).
Explanation-Based Tuning of Opaque Machine Learners with Application to Paper Recommendation

Research in human-centered AI has shown the benefits of machine-learning systems that can explain their predictions. Methods that allow users to tune a model in response to the explanations are similarly useful. While both capabilities are well-developed for transparent learning models (e.g., linear models and GA2Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, no method currently exists for tuning of opaque models in response to explanations. This paper introduces LIMEADE, a general framework for tuning an arbitrary machine learning model based on an explanation of the model's prediction. We apply our framework to Semantic Sanity, a neural recommender system for scientific papers, and report on a detailed user study, showing that our framework leads to significantly higher perceived user control, trust, and satisfaction.
PrivApollo – Secret Ballot E2E-V Internet Voting

The Apollo voting protocol improves on the integrity properties of Helios by enabling voters to communicate to the public the failure of the cast-as-intended check, in the event that the voting terminal changes the vote on receiving the credential. It also enables the voter to detect a dishonest registrar and to prove misbehaviour. It provides an explicit description of the role of one or more computational voting assistants which help the voter perform the checks without obtaining information on the vote. Unfortunately, neither Helios nor Apollo provides ballot secrecy, because the voting terminal knows the vote. We present PrivApollo, a protocol that improves Apollo by providing ballot secrecy from the voting terminal.
Virtual Memory Management of Private Cloud using OpenStack API

There is a constantly increasing demand for computing resources in the modern age. The traditional information technology systems cost too high in investment, updating and maintenance to the organizations. Moreover, they fail to perform well with respect to high utilization of resources. The modern paradigm of Cloud Computing answers these issues well. The cloud infrastructures and platforms are not only cost effective but also efficient in handling huge resources. More and more companies are moving to cloud platforms to run their applications. Many software platforms are available for cloud computing under open source software and are free of Apache CloudStack and OpenStack. The current study uses OpenStack cloud and in particular, emphases on the memory management of the cloud for handling virtual machine instances and their distribution.
Blended learning in teaching higher mathematics to students with disabilities

A stable trend in modern education is the transition from traditional or face-to-face learning to blended learning by introducing new online tools into the educational process. The complexity of converting traditional academic disciplines into a blended learning format arises from the need to simultaneously develop two interconnected and at the same time qualitatively different components (traditional and online). A correct balancing of the traditional and online components of blended learning allows making the educational process more comfortable and adaptable to the needs of every individual student. The solution of this problem largely depends on specific features of different academic disciplines. In this paper we propose a solution for basic mathematical disciplines taught to students with disabilities at Bauman Moscow State Technical University. The purpose of the study is to find the optimal organization of the process of studying higher mathematics by students with disabilities at Bauman Moscow State Technical University in the framework of blended learning, combining elements of the traditional teaching in the classroom and elements of online learning. Materials and research methods include the analysis of scientific and methodological literature on blended learning, the preparation and implementation of electronic training materials and teaching aids, the analysis of student performance and feedback, as well as the subsequent educational process adjustment based on the data received. The result of the study is the creation of a balanced two-component structure of the educational process in the framework of blended learning. The traditional component includes lectures, seminars and classroom consultations, and the online component consists of electronic training materials and communication tools via the Internet (email and a means of online consultations). Special hardware and software make online consultations similar in effectiveness to traditional classroom consultations. Every student at any place with access to the Internet is able not only to see and hear other participants of the online consultations, but also to see their notes and, in turn, show them his/her own notes made by hand on a regular sheet paper. The introduction of the online component into the educational process allows reducing the high learning rate typical for traditional learning and making it more acceptable for students experiencing certain health problems. Conclusion: the results of the study are used during classes in four mathematical disciplines: mathematical analysis, analytical geometry, linear algebra and integrals with differential equations, which are taught to students with disabilities in the first and second years of study at Bauman Moscow State Technical University.
Asia's surging interest in binary analysis

research-article Share on Asia's surging interest in binary analysis Authors: Sang Kil Cha KAIST KAISTView Profile , Zhenkai Liang National University of Singapore National University of SingaporeView Profile Authors Info & Claims Communications of the ACMVolume 63Issue 4April 2020 pp 86–88https://doi.org/10.1145/3378424Published:20 March 2020Publication History 1citation2,856DownloadsMetricsTotal Citations1Total Downloads2,856Last 12 Months121Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access