Reviewerly/exHarmony huggingface dataset:
ЭННОЕ ИЗМЕРЕНИЕ БУМАЖНОЙ ВОЛШЕБНОЙ ШКАТУЛКИ, ИЛИ КАК СТАВИТЬ ПРИГОВСКИЙ ТЕАТР

The article traces the role of dramatic texts in the literary and artistic works of Dmitry Alexandrovich Prigov, and also analyzes the reasons for the non-embodiment of these works by specific theatre productions. It is established that there is a close connection between Prigov’s vision of the theater space and his ‘phantom’ installations, between the general performative project “DAP – a living classic” and the presence in the plays of a number of authorial hypostases.As a result, a set of techniques is described (chaotic polyphony, mingling with side note remarks, involving the public in action), defining a separate casket semiotic space in which the plot, logic, identity are strained to the limit and decompose in their very essence, which leads to the collapse of the space through which, by the power of the theatrical word and the performative beginning, a new not only aesthetic, but also physical dimension is being revived: transcendence is saturated with an amazing “scenic” palpability
SMI Attributes: Key role in business as a service in Cloud Computing

The development of cloud computing leads to a situation that, unique services are being offered by an ever increasing number of organisations. It is very difficult for the customer to choose the right cloud provider based on his service requirements. There is no framework to index cloud provider that depends on the user requirement. Undertakings from new businesses to huge organisations see cloud as a decent choice, in light of asset pooling, on interest provisioning and pay as you go model. The most essential objective for a cloud specialist organisation is making a feeling of certainty and trust about that cloud provider among service requesters. To accomplish this, associations ought to build up a technique that is complete, efficient and it ought to be finished with legitimate checks understanding the future necessities, and by conveying a cloud framework. This review paper centres around different characteristics of cloud benefit estimation record created by Cloud Services Measurement Initiative Consortium (CSMIC) that gives a standard technique to estimating and looking at a business benefit.
A novel data sharing model for cloud environment using dual key authentication

In the present world scenario, cloud computing plays a significant role in sharing of data between computing devices in a secure manner. Specifically, data sharing amongst dynamic or static groups has grown as a major factor. For this purpose, the study proposed a dual key authentication-based dynamic group data sharing model in a cloud environment. First, data are encrypted before sharing using AES algorithm. Here, data is stored in the form of string instead of bytes which can reduce the memory size without affecting the original data. Second, the cryptographic approach is applied to overcome the issues of computational load and key length maintenance. A subkey is generated for the private key, wherein each user will receive a different subkey. Third, enabled the prevention of secret data from collusion attack and improved the malicious activity prediction during data transfer. So, that the revoked user unable to violate the data confidentiality. Also, the original data cannot be decrypted by anyone even if they receive it. Finally, this study evaluated the performance of proposed cloud security model with the existing methods, especially PRE, CL-PRE and SaDaSc.
What Is the President's Successful Crisis Management Leadership? Based on the Assessment of the Sewol Ferry Disaster Case in Korea

What is the President s Successful Crisis Management Leadership?: Based on the Assessment of the Sewol Ferry Disaster Case in Korea 의 이용 수, 등재여부, 발행기관, 저자, 초록, 목차, 참고문헌 등 논문에 관한 다양한 정보 및 관련논문 목록과 논문의 분야별 BEST, NEW 논문 목록을 확인 하실 수 있습니다.
A framework for disease diagnosis based on fuzzy semantic ontology approach

In this paper, we propose an OWL2 ontology based on SNOMED CT standard medical terminology. It can provide significant help to support physicians in diabetes risk level diagnosis problem. It explicitly defines the semantics of diabetes knowledge by using ontology and deal with the imprecise and vague nature of its data by using fuzzy set theory. It involves building a complete linguistic fuzzy rule-base that can integrate knowledge from expert and CPG with knowledge extracted from training data and knowledge extracted from the semantic model. This step enhances the level of automation and interoperability of CDSS. The importance of our work comes from the current lack of studies related to the integration of the formal integration between the ontology semantics and FES reasoning, especially in the medical domain. The ontology acts as an integral and complementary component of the FES.
Deep features-based dialect and mood recognition using Assamese telephonic speech

Learning aided methods are popular for designing automatic speech recognition (ASR) systems. Majority of works have used shallow models in combination with mel frequency cepstral coefficients (MFCC) and other features for speech recognition applications. Although these shallow models are effective but incorporating deep features in the mechanism for speech processing applications is necessary to increase the efficiency. Despite of considerable amount of works on the design of deep learning topologies and training paradigms in supervised domain, very few works have concentrated on deep features which are essential to capture detailed information of speech. This work focuses on the generation of deep features using stacked auto-encoder for normal and time shifted telephonic speech samples in Assamese language with mood and dialect variations. Experimental results show that the deep features learned by the stacked auto-encoder performs better while it is configured for Assamese speech recognition with mood and dialect variations.
A comparison of two blending-based ensemble techniques for network anomaly detection in Spark distributed environment

In this paper, two blending-based ensemble models, namely, logistic regression-based blending ensemble and SVM-based blending ensemble have been compared in terms of their total training time in a distributed environment and their detection accuracy rates. To handle process of concept drift two clustering algorithms have been compared for their training times in a distributed environment. Tests have been conducted on different machines by varying the number of executor cores to study time latency in a distributed Spark environment. Logistic regression-based blending ensemble with an accuracy of 93% and an accuracy of 98% using SVM-based blending ensemble was achieved. The proposed models have been evaluated using CIDDS dataset.
An improved Sudoku-based data hiding scheme using greedy method

Inspired by Chang et al.'s scheme, an improved Sudoku-based data hiding scheme is proposed here. The major idea of our improved scheme is to find the approximate optimal solution of Sudoku using the greedy method instead of via a brute-force search for an optimal solution. Later, the found approximate optimal solution of Sudoku is used to offer satisfactory visual stego-image quality with a lower execution time during the embedding procedure. Simulation results confirmed that the average stego-image quality is enhanced by around 90.51% compared to Hong et al.'s scheme, with relatively less execution time compared to a brute-force search method.
GigaBERT: A Bilingual BERT for English and Arabic

Arabic is a morphological rich language, posing many challenges forinformation extraction (IE) tasks, including Named Entity Recognition (NER),Part-of-Speech tagging (POS), Argument Role Labeling (ARL), and RelationExtraction (RE). A few multilingual pre-trained models have been proposed andshow good performance for Arabic, however, most experiment results are reportedon language understanding tasks, such as natural language inference, questionanswering and sentiment analysis. Their performance on the IE tasks is lessknown, in particular, the cross-lingual transfer capability from English toArabic. In this work, we pre-train a Gigaword-based bilingual language model(GigaBERT) to study these two distant languages as well as zero-short transferlearning on various IE tasks. Our GigaBERT outperforms multilingual BERT andand monolingual AraBERT on these tasks, in both supervised and zero-shotlearning settings.\footnote{We have made our pre-trained models publiclyavailable at https://github.com/lanwuwei/GigaBERT
Exponential reaching law and sensorless DTC IM control with neural network online parameters estimation based on MRAS

The most important problem in the control of induction machine (IM) is the change of its parameters, especially the stator resistance and rotor-time constant. The objective of this paper is to impl...
Pengaruh Model Picture and Picture terhadap Evaluasi Kosakata Bahasa Inggris Kelas III di SD Negeri Periuk 3

This study studies to learn whether there are learning models and pictures for English. This research was conducted on students in grade III of SD Negeri Periuk 3. The method used in the study was. The method was chosen because the main purpose of this research is to study the facts arising from aid from a treatment. The hypothesis proposed about the effect of drawing and drawing models is very impactful for students in doing vocabulary learning. However, based on data obtained from the results of research models, images and drawings very much refer to the English vocabulary.
Large-scale patch recommendation at Alibaba

We present Precfix, a pragmatic approach targeting large-scale industrial codebase and making recommendations based on previously observed debugging activities. Precfix collects defect-patch pairs from development histories, performs clustering, and extracts generic reusable patching patterns as recommendations. Our approach is able to make recommendations within milliseconds and achieves a false positive rate of 22%. Precfix has been rolled out to Alibaba to support various critical businesses.
Many-objective Optimization-based Optimal drone deployment for agricultural zone

Monitoring using drones is not just a civilian and military task, but it also concerns the agricultural sector, where it can play an important role in the context of smart agriculture. It seems to be a very valuable tool in the future. However, the optimal deployment of a set of monitoring drones is a very challenging problem; it is a NP-Hard problem. In this paper, the deployment problem has been modelled as a constrained many-objective optimisation problem. Powerful heuristics, namely multi-objective artificial bee colony (MOABC), multi-objective particle swarm optimisation (MOPSO), non-dominated sorting genetic algorithm II (NSGA II), strength Pareto evolutionary algorithm II (SPEA II) and non-dominated sorting genetic algorithm III (NSGA III) are used to find the optimal deployment strategy with four goals: minimising energy consumption, maximising total coverage, maintaining connectivity and minimising overlaps. A comparative study was carried out and the results showed that the SPEA II, NSGA III and NSGA II algorithms have better convergence and maintain good diversity than the other algorithms.
Engineering Spin Domains in a Binary BEC

2-component BEC, confined in an all optical line trap, we use a spin-dependent optical barrier beam to localise one of the spin components. By time-averaging the beam we can furthermore create multiple spin sites.
A Theoretical Approach for Correlating Proteins to Malignant Diseases

Malignant Tumors are developed over several years due to unknown biological factors. These biological factors induce changes in the body and consequently, they lead to Malignant Tumors. Some habits and behaviors initiate these biological factors. In effect, the immune system cannot recognize a Malignant Tumor as foreign tissue. In order to discover a fascinating pattern of these habits, behaviors, and diseases and to make effective decisions, different machine learning techniques should be used. This research attempts to find the association between normal proteins (environmental factors) and diseases that are difficult to diagnose and propose justifications for these diseases. This paper proposes a technique for medical data mining using association rules. The proposed technique overcomes some of the limitations in other algorithms such as the Apriori algorithm and ECLAT algorithm. A modification to the Apriori algorithm has been proposed to mine Erythrocytes Dynamic Antigens Store (EDAS) data in a more efficient and tractable way. The experiments inferred that there is a relation between normal proteins as environment proteins, food proteins, commensal proteins, tissue proteins, and disease proteins. Also, the experiments show that habits and behaviors are associated with certain diseases. The presented tool can be used in clinical laboratories to discover the biological causes of malignant diseases.
A summary study on handwritten documents word spotting

The researchers have focused on the techniques used to create systems for data protection such as signature verification, investigation in crimes, handwriting mails, graphical documents analysing, human assisting, etc. Recently, several researchers have focused on creating an optimal system for data processing in handwriting documents. This paper aims to present an overview about the studies and techniques used to achieve this system. The paper presents at first the most used databases, then a general pipeline diagram for word spotting system, next in each step (pre-processing, segmentation, feature extraction and classification) the most used techniques/methods and a summary table about the published researches are presented and finally the latest studies in this field.
Exploring complex networks with the ICON R package.

We introduce ICON, an R package that contains 1075 complex network datasets in a standard edgelist format. All provided datasets have associated citations and have been indexed by the Colorado Index of Complex Networks - also referred to as ICON. In addition to supplying a large and diverse corpus of useful real-world networks, ICON also implements an S3 generic to work with the network and ggnetwork R packages for network analysis and visualization, respectively. Sample code in this report also demonstrates how ICON can be used in conjunction with the igraph package. Currently, the Comprehensive R Archive Network hosts ICON v0.4.0. We hope that ICON will serve as a standard corpus for complex network research and prevent redundant work that would be otherwise necessary by individual research groups. The open source code for ICON and for this reproducible report can be found at this https URL.
Automated real-time anomaly detection of temperature sensors through machine-learning

Fast identification of faulty sensors is necessary for guaranteeing their robust functions in diverse applications ranging from extreme weather prediction to energy saving to healthcare. We present an automated machine-learning based framework that can detect anomalies of temperature sensor data in real-time. We adopted a purely temporal approach that utilises a univariate time-series (UTS) generated by a single sensor. The framework divides the UTS into subsequences, models each subsequence stochastically as an autoregressive function, and finally mines the function parameters with a one-class support vector machines (OC-SVM) that classifies any outlier as an anomaly. Extensive experimentation showed that the framework identifies both normal and anomalous data correctly with high degrees of accuracy.
Development of pass-through augmented reality interface for human robot interaction

In this technology driven world, human machine interaction is indispensable. Conventional human machine interface techniques pose the challenges of extensive training and increased cognitive load. Usage of hand gestures, face and speech recognition, eye ball tracking, etc. are in the upward trend to overcome these limitations and prove to be more intuitive and user friendly. In this paper, a vision-based pass-through augmented reality system is developed that enables the user to command the mobile robot for various mobility and navigation tasks. A pass-through augmented reality setup is achieved by a combination of a typical virtual reality headset and a stereo camera with depth perception. The stereo camera and a Leap Motion sensor mounted on a wearable VR headset acts as the vision system for the user and the gesture recognition system respectively. Necessary graphical user interface with the list of functions and information will appear on the VR headset display from which the user can select the option/command to be given to the mobile robot. The gesture made by the user in the GUI is detected and communicated to the robot through wireless means to perform the corresponding task.
WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols

This paper presents the results of the wet lab information extraction task at WNUT 2020. This task consisted of two sub tasks: (1) a Named Entity Recognition (NER) task with 13 participants and (2) a Relation Extraction (RE) task with 2 participants. We outline the task, data annotation process, corpus statistics, and provide a high-level overview of the participating systems for each sub task.
MAPS-X: Explainable Multi-Robot Motion Planning via Segmentation

Traditional multi-robot motion planning (MMP) focuses on computing trajectories for multiple robots acting in an environment, such that the robots do not collide when the trajectories are taken simultaneously. In safety-critical applications, a human supervisor may want to verify that the plan is indeed collision-free. In this work, we propose a notion of explanation for a plan of MMP, based on visualization of the plan as a short sequence of images representing time segments, where in each time segment the trajectories of the agents are disjoint, clearly illustrating the safety of the plan. We show that standard notions of optimality (e.g., makespan) may create conflict with short explanations. Thus, we propose meta-algorithms, namely multi-agent plan segmenting-X (MAPS-X) and its lazy variant, that can be plugged on existing centralized sampling-based tree planners X to produce plans with good explanations using a desirable number of images. We demonstrate the efficacy of this explanation-planning scheme and extensively evaluate the performance of MAPS-X.
FEATURES OF LEAD AND ZINC METALLOGENY WITHIN THE YENISEI RIDGE

Information collected on deposits, ore occurrences, and lead and zinc mineralization points known within the Yenisei Ridge is provided. A summary of the latest data on the geology and metallogeny of lead and zinc in the region is presented. A brief description of the deposits is given and their ore formation typification is carried out. The position of deposits in a stratigraphic section, the relationship with geological formations and tectonic structures, and subsequent conclusions about deposit location patterns and metallogenic zonality of the area were examined. The position analysis of polymetallic mineralization in regional geophysical and geochemical field revealed the presence of lead → zinc → barium trend from the Ishimbinsk fault towards the Siberian platform and the potential discovery of economically significant polymetallic mineralization in the eastern Yenisei Ridge.
Free speech, politics, and government

Politics and government are undergoing dramatic changes through the advent of new technology. The early developers of community networks (mentioned in Section 1.2) had hopeful visions of information technology (IT)-facilitating participatory democracy. Yet the most memorable visions have been literary dystopias, where surveillance is omnipresent and governments have absolute control. We shall begin by highlighting some of these important writings. We shall then consider a current and present topic—the cultural and legal frameworks governing free speech and other forms of expression on the internet. We review several kinds of ‘undesirable’ speech that test our commitment to free speech—messages that are viewed as obscene, hateful, seditious, or encouraging of terrorism. Next, we examine methods governments worldwide use to censor web content and prevent digital transmission of messages of which they disapprove, as well as a similar role for social media firms in what is now known as content moderation. We shall also mention one new form of rampant and very harmful internet speech— fake news. Fake news becomes especially troubling when it is released into and retransmitted widely into filter bubbles that select these messages and echo chambers that focus and sensationalize such points of view to the exclusion of other contradictory ideas. The prevalence and dangers of fake news became obvious during post facto analyses of the 2016 US presidential campaign. The internet and social media enable greater civic participation, which is usually called e-democracy or civic tech. Most such uses of social media are relatively benign, as in online deliberations about the desired size of a bond issue, or internet lobbying to get libraries to stay open longer during the summer. However, for more significant issues, such as violations of fundamental human rights, or unpopular political decisions that incite public unrest, social media communications may facilitate political protest that can lead to political change. IT also plays a role in elections—social media can be used to mobilize the electorate and build enthusiasm for a candidate. Correspondingly, surveys and big data are used to target potential voters during political campaigns and to tailor specific messages to key voters.
A comparative linguistic analysis of English news headlines in China, USA, UK, and ASEAN countries

This paper aims to conduct a comparative study on English news headlines in the USA, China, the UK, and ASEAN countries, mainly investigating how the composing of news headlines is interrelated with its linguistic factors such as the part of speech, length and the frequency of most common words depending on which country the news is published in. In this paper, the linguistic comparison is performed based on merely the headlines without going through whole articles. For this purpose, 13 sets of data are collected from major online news sites within above-mentioned countries. The comparison results reveal that headlines in different countries comply with news writing rules in slightly different ways as well as boast distinctive features. These differences are attributed to the consideration of the target audience's multi-faceted states such as knowledge states, beliefs, or interests. To better exemplify the results, the headlines in this article were read with care. The proposed method begins with data collection and pre-processing. News headlines are then fetched from different news sources using crawler and processed in natural language processing tool (NLTK).