Reviewerly/exHarmony huggingface dataset:
Introduction, generation and entanglement of entangled displaced even and odd squeezed states

In this paper, first, we introduce special types of entangled quantum states named “entangled displaced even and odd squeezed states” by using displaced even and odd squeezed states which are constructed via the action of displacement operator on the even and odd squeezed states, respectively. Next, we present a theoretical scheme to generate the introduced entangled states. This scheme is based on the interaction between a [Formula: see text]-type three-level atom and a two-mode quantized field in the presence of two strong classical fields. In the continuation, we consider the entanglement feature of the introduced entangled states by evaluating concurrence. Moreover, we study the influence of the displacement parameter on the entanglement degree of the introduced entangled states and compare the results. It will be observed that the concurrence of the “entangled displaced odd squeezed states” has less decrement with respect to the “entangled displaced even squeezed states” by increasing the displacement parameter.
Semantic Technologies as Enabler

Considered a core component of business intelligence, a data warehouse deals with the management and integration of structured databases. The web has become a global data warehouse, though containing unstructured text rather than the structured content of data warehouses. Managing and integrating this huge amount of content that is intrinsically unstructured and heterogeneous in nature requires the ability to represent and store it in a manner that can be read and understood by computers and is highly scalable. Sir Tim Berners-Lee, the founder of the World Wide Web Consortium, envisioned the semantic web as a web of interconnected data, and the supporting semantic technologies made it a success. Since the introduction of linked data standards, there has been much more activity in employing semantic technologies everywhere that information is modeled. Semantic technologies can significantly improve the way we use large amounts of data. Potential benefits of semantic technologies include data integration from disparate data sources, interoperability, and efficient query answering.
Анализ парадоксальных нейрофизиологических реакций на разных этапах восприятия отрицательных эмоциональных стимулов у больных шизофренией

The article is aimed at the study of brain activation to neutral and emotionally significant (threatening) stimuli with evoke potential (EP) method in patients with paranoid schizophrenia and healthy controls. It was shown that threatening stimuli, as more significant, cause greater activation (shortening of latency and increase of amplitude) in the occipital and posterior temporal regions to such stimuli in comparison with neutral ones both in patients and in norm in an intragroup assessment. After 200 ms this increase was observed in the right, after 300 ms – in the left, and after 400 ms – again in the right hemisphere. However, in patients, at the P200 wave, to significant stimuli, there began a physiologically paradoxical effect. In the left lower frontal area there was an increase in both amplitude and latency of the P200 wave and a decrease in both these parameters in the right frontal and central midline areas. 300 ms after the stimulus paradoxical effects (PE) in the form of increase of both parameters were observed in left prefrontal and right lower frontal areas, and a decrease – in the left lower frontal and central midline areas. After 400 ms, PE was observed in left prefrontal and right lower frontal areas as an increase in both parameters and in the right prefrontal – as their decrease. An intergroup comparison showed that patients had either a simultaneous increase or decrease of both parameters of all components of evoked potentials (ERP) starting with the P200 in comparison with norm, thus indicating the pathological state (PS) of the anterior brain regions in response to emotionally significant stimuli, which may be caused by several alternative factors.This can be explained by a disturbance of neural connections due to the pathological process of aberrant pruning in patients with schizophrenia.
A Method of Finding Shortest Paths for Bumblebees

Abstract In October 2010, a new British study said that bumblebees fluttering from flower to flower showed the ability to easily solve the “traveler salesman problem.” There is no good explanation for how the bumblebee solved the problem. In this paper, a simple and efficient algorithm is proposed to explain how bumblebee solves the traveling salesman problem.
Computational Light Field Photography : Depth Estimation, Demosaicing, and Super-Resolution

The transition of camera technology from film-based cameras to digital cameras has been witnessed in the past twenty years, along with impressive technological advances in processing massively digi ...
ANALISIS KESALAHAN BERBAHASA PADA ANAK USIA 2 TAHUN DITINJAU DARI TATARAN FONOLOGI

Language is a communication tool used by speakers in everyday life. Language skills are needed for good interaction and the information exchange process. Therefore, language has been taught to children since birth and will develop according to their age and growth. Pshycologically, new children can say a word at the age of 2-3 years old. The tendency of language error that the children, who are 2-3 years old did, is an error in the field of phonology which is the children still difficult to pronounce one word perfectly. In addition, the children were often changed the phonemes of word, thus produce a different utterance from as well as it should have but still have the same meaning. The aim of study is to determine the level of children’s ability who are 2 years old in pronouncing a word perfectly. The research method used a descriptive qualitative method with the purpose is to describe with clearly and objectly the object of study. There are two children who became the object of this study, Arfa and Aimar, who are still two years old. The data collection was conduct by the researchers  as the main instrument that show the picture of animals to the children, then the children said it. The results of analysis shows that the much phonological error are the replacement an ellipsis/omission of phonemes. Therefore, there is a change in the sound of words that should be pronounced. Thus, in order to the acquistion of the children’s language more maximal, by training it in order to be better in spoken and a good guidance and stimulus by parent are needed.
Knowledge Representation and Storage

Emergency information sharing between government entities and policy and decision makers is possible by the presence of centralized disaster knowledge stores. The knowledge stored in this store should be represented in a format that allows easy exchange of information between different parties involved. If the knowledge is exchangeable in real time, the speed and quality of the coordinated response increases. In order to free up data from storage in isolated silos and to enable machines to understand and interpret the meaning of information, we must set up a network of links between structured data. Semantic data models and semantic repositories are a solution for this. This chapter explains the process of creating knowledge stores for two disasters under study, namely earthquake and terrorism. Each domain-specific centralized knowledge store comprises a domain ontology, a case base of history cases, a rule base of expert knowledge, and required common procedures according to the use case scenario.
Design and Analysis of Laboratory Supervision Robot Based on Robot Operating System

As the laboratory becomes more open, the flow of laboratory personnel has increased, which has brought challenges to the daily management of the laboratory. At present, the difficulty of daily laboratory management lies in the cumbersome personnel statistics and the heavy workload of safety monitoring. In response to this problem, this paper proposes a laboratory supervision robot with autonomous movement, autonomous monitoring, face recognition, and management information. First, a face recognition algorithm is designed based on convolutional neural network. Then the robot control system is designed using the robot operating system (ROS). Based on the simultaneous localization and mapping (SLAM) technology, the Gmapping algorithm is used to realize the autonomous movement of the robot, which provides a reference for the design of the laboratory supervision robot.
Unsupervised classification of the solar wind using Self-Organizing Maps

&amp;lt;p&amp;gt;During the past decades different methods of classification of the solar wind have been proposed. These include simple models separating the &amp;amp;#8220;fast&amp;amp;#8221; from &amp;amp;#8220;slow&amp;amp;#8221; flows (Arya and Freeman, 1991, Yordanova et al., 2009, among others), complex empirical methods grouping its properties in multiple categories associated to its origin in the solar atmosphere (Xu et Borovsky, 2015), and more recently probabilistic classifications based on Gaussian processes (Camporeale et al., 2017).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Solar wind classification serves four main purposes: 1) statistical analysis of different wind types, 2) interpretation of observations in the magnetosphere, 3) diagnostics of physical processes in the Sun, and 4) identification of solar cycle effects on the Earth&amp;amp;#8217;s plasma environment. In this work, instead of using empirical methods, we use the machine learning technique known as Self-Organizing Maps (SOM) to automatically classify the solar wind at 1AU, without human intervention, using observations gathered by the ACE mission.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The ACE spacecraft has been continuously recording solar wind data for the past 22 years. We use hourly averaged solar wind parameters from the ACE Science Center in CalTech for this study. Each entry in this database can be considered as a single point in a multi-dimensional (ND) space. SOM techniques transform all the points in this space into a single 2D space with a small number of L x L nodes. The nodes are the 2D representation of the cloud of points in the ND space, grouping together around each node, points with similar properties. The nodes in this 2D map are interrelated, maintaining a structural topology that is useful for their interpretation. Each one of the nodes in the SOM map can viewed as one of the possible L x L classes. We go one step further, automatically grouping together nodes in the map that are close in the ND space, reducing the total number of classes to only a few. We compare the results obtained using SOM with the methods introduced above, showing the similarities and differences. We show that the SOM technique, which does not rely on human intervention, can be used to properly describe the different types of solar wind conditions observed in a full solar cycle.&amp;lt;/p&amp;gt;
Multimodal Attention for Layout Synthesis in Diverse Domains

We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents and 3D objects. Most complex scenes, natural or human-designed, can be expressed as a meaningful arrangement of simpler compositional graphical primitives. Generating a new layout or extending an existing layout requires understanding the relationships between these primitives. To do this, we propose a multimodal attention framework, MMA, that leverages self-attention to learn contextual relationships between layout elements and generate novel layouts in a given domain. Our framework allows us to generate a new layout either from an empty set or from an initial seed set of primitives, and can easily scale to support an arbitrary of primitives per layout. Further, our analyses show that the model is able to automatically capture the semantic properties of the primitives. We propose simple improvements in both representation of layout primitives, as well as training methods to demonstrate competitive performance in very diverse data domains such as object bounding boxes in natural images (COCO bounding boxes), documents (PubLayNet), mobile applications (RICO dataset) as well as 3D shapes (PartNet).
Semantics-based Decision Support - An Introduction

This book is an attempt to exploit the full potential of existing tools, techniques, and methodologies to provide situation awareness and advisory support to end-users in a seamless manner. The nature of information input to a Decision Support System (DSS) is majorly unstructured or semi-structured, making the decision-making process complex. The heterogeneity of information sources is easily dealt with by incorporating semantic technologies and hence facilitating intelligent situation awareness and decision support. This book presents to the reader the prototypical development of a knowledge-driven situation awareness and advisory support (KDSAAS) in the emergency domain. KDSAAS applies semantic technologies to provide real-time information in order to provide intelligent decision support. This introductory chapter advocates the importance and utilization of semantic data models in achieving semantic intelligence, which is a prerequisite for successful decision support systems. A few use cases demonstrating the importance of semantic technologies for decision support systems are also presented.
Bipartite Quantensysteme und Quantencomputing

No AccessPhysik mit 2x2-MatrizenJan 2021Bipartite Quantensysteme und QuantencomputingHans Jürgen KorschHans Jürgen KorschSearch for more papers by this authorhttps://doi.org/10.3139/9783446468061.012SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2021Pages: 291-306Print ISBN: 978-3-446-46694-4eISBN: 978-3-446-46806-1 Copyright & Permissions© 2021 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
Technology, Autonomy, and Manipulation

Since 2016, when the Facebook/Cambridge Analytica scandal began to emerge, public concern has grown around the threat of “online manipulation”. While these worries are familiar to privacy researchers, this paper aims to make them more salient to policymakers — first, by defining “online manipulation”, thus enabling identification of manipulative practices; and second, by drawing attention to the specific harms online manipulation threatens. We argue that online manipulation is the use of information technology to covertly influence another person’s decision-making, by targeting and exploiting their decision-making vulnerabilities. Engaging in such practices can harm individuals by diminishing their economic interests, but its deeper, more insidious harm is its challenge to individual autonomy. We explore this autonomy harm, emphasising its implications for both individuals and society, and we briefly outline some strategies for combating online manipulation and strengthening autonomy in an increasingly digital world.
Research and Development of Rail Transit Supervision Management Information System Based on Mobile Office

The development of mobile office and Internet provides the technical conditions for the information management of rail transit supervision. The paper presented the overall technical scheme of the system, designed the system function module, described the application of key technologies and summarize the development prospect of mobile supervision system. Which has a very good application value to improve lean management and core competence for supervision enterprises.
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines

Machine-learned safety-critical systems need to be self-aware and reliably know their unknowns in the open-world. This is often explored through the lens of anomaly/outlier detection or out-of-distribution modeling. One popular formulation is that of open-set classification, where an image classifier trained for 1-of-K classes should also recognize images belonging to a (K+1)th other class, not present in the training set. Recent work has shown that, somewhat surprisingly, most if not all existing open-world methods do not work well on high-dimensional open-world images (Shafaei et al. 2019). In this paper, we carry out an empirical exploration of open-set classification, and find that combining classic statistical methods with carefully computed features can dramatically outperform prior work. We extract features from off-the-shelf (OTS) state-of-the-art networks for the underlying K-way closed-world task. We leverage insights from the retrieval community for computing feature descriptors that are low-dimensional (via pooling and PCA) and normalized (via L2-normalization), enabling the modeling of training data densities via classic statistical tools such as kmeans and Gaussian Mixture Models (GMMs).
Seismological system fault detection technology based on waveform analysis

This paper mainly discusses that it is possible to directly determine whether the seismic instrument has a fault during operation based on characteristics of the waveform and that the specific fault of the instrument in such cases can be deduced in reverse according to different waveforms shown by different faults. Faults can be identified early through application of the reverse deduction to instrument maintenance, and maintenance personnel can be prompted to deal with them, thereby ensuring and improving data quality.
Investigating Haptic Feedback for Human Interactions in Virtual Environment

The sense of touch is essential for our daily life activities. It helps human beings to differentiate the characteristics of the objects in the environments. Same goes in Virtual Reality (VR), it not just replicating the real-world objects but the haptic sensation as well. Integrating electro-cutaneous device and haptic feedback system enables the simulation of touch sensation inside the virtual environment. In this project, the haptic glove prototype was built to produce touch feedback through vibration and implementing it to the virtual environment inside Unity3D. The haptic glove is controlled with Arduino and programmed in Unity3D to create a relationship between the haptic device and the Leap Motion Controller (LMC). The Leap Motion Controller detect and track the user’s hands and emulate the virtual hands displayed in Unity3D. Different sizes of virtual cubes were created inside the Unity3D to evaluate the functionalities and the capabilities of the haptic glove. The collision of the virtual hand and the cubes shall trigger the vibration sensation to the user’s fingertips. The experiments show that the addition of haptic device promotes better interactivity between the user and the virtual objects. In this work, the presence of virtual objects is perceived in the form of vibration. In future work, we shall design the efficient system to reach the three desired features of VR: immersion, interaction and presence.
Portraying of machine learning techniques to detect mockery in social media

Abstract Sentiment is the inclination or disposition towards something and conclusion investigation on examining or learning about different surveys and remarks given by individuals. The sentiment analysis technique is generally understood and classed as optimistic, negative and non-partisan. These emotions are In sentimental research, the basic measure is the proximity of mocking. Mockery is an advanced language that identifies a difference between the anticipated meaning and the exacting meaning of the words. This confusion is often a disturbing job for people, mockery exploration. Individuals typically speak using significant obvious tension and gestural signals, such as eye motions. These obvious and gestural data are simply not sufficient to express material ridicule, which renders it subject to a variety of factors. This paper provides an overview of different methodologies used to define mockery. The results of the analyses are discussed in various ways. The study shows that deep learning is a great tool for recognizing sarcasms.
Implementasi Data Mining Dalam Mengelompokkan Jumlah Produktivitas Ubi Kayu Menurut Provinsi Menggunakan Algoritma K-Means

Abstract − Cassava is one of the main foodstuffs, not only in Indonesia but also in the world. In Indonesia, cassava is the third staple food after rice and corn. The spread of cassava plants extends to all provinces in Indonesia. Using data mining is one of the ideas of information to classify the amount of cassava productivity by province, by using the k-means clustering method the amount of cassava productivity will be collected based on the year (2011-2018) of 30 provinces. K-means is a method with unsupervised classification type where the data is grouped into one or more clusters. k-means modeling the dataset into clusters where one cluster has the same characteristics and has different characteristics from other clusters. This study aims to classify the amount of cassava productivity by province. Where the highest cluster results are obtained with a total of  2 provinces, medium cluster with 4  provinces, and a low cluster with 24 provinces. Keywords : Data Mining, K-means, Clustering, Cassava, RapidMiner Studio
Effective Communication with Hand Gesture Recognition Using Voice Over and Crime Detection System for Disabled People

The Hand motions are the most well-known types of correspondence and have extraordinary significance in our reality. They can help in building sheltered and agreeable UIs for a large number of uses. In the current system, we have to speak with the Deaf and moronic individuals utilizing Deaf and idiotic language just, there is no programmed device to change over that into sound arrangement. In the Proposed system, Hand motions are the most widely recognized types of correspondence and have incredible significance reality. It is used to build protected and agreeable UIs for huge number of uses. Various types of calculations have utilized on camera for hand motion acknowledgment, yet hearty implementation on motions from different subjects is as yet testing. In the Modification, We convey Hand motion acknowledgment alongside Criminal stance additionally to distinguish and anticipate any criminal activities by any client. So this application is utilized for Deaf and idiotic correspondence and voice over and well criminal activity discovery utilizing matlab.
Peningkatan keterampilan membaca pemahaman melalui model pembelajaran cooperative integrated reading and composition pada peserta didik kelas IV sekolah dasar

This study aims to enhance reading comprehension skills through implementation of Cooperative Integrated Reading and Composition model. The subjects of the study are 26 students of the 4 th grade in Pajang II elementary school, Laweyan, Surakarta. This research is Classroom Action Research consist of two cycles. Data collection is done by interviewing, observing, reading comprehension skills test, and document analysis. The data’s validity is done by using content validity, source triangulation, and technical triangulation. The data is analyzed using an interactive analysis model of Miles-Huberman and comparative descriptive model. The result of reading comprehension skills in pretest showed an average of 46,08. Then, in the first cycle, the average increase to 64,98 and become 83,5 on the second cycle. Thus, the conclusion that can be drawn regarding reading comprehension skills in 4 th grade students of Pajang II elementary school academic year of 2019/2020 can be enhanced by applying Cooperative Integrated Reading and Composition model.