Reviewerly/exHarmony huggingface dataset:
Virtual Therapy for Phobias:A Human Computer Interaction

A specific phobia is a common anxiety problem that can be effectively treated with a variety of therapies such as exposure therapy and cognitive therapy. Exposure therapy is one of the most well-known approaches for treating a specific phobia. The purpose of exposure therapy is to allow the target patient to be exposed to the source of anxiety or its context without placing them in danger. Virtual reality exposure therapy is one potential study area. Interconnecting with AI, Virtual Reality (VR) is becoming more popular as a treatment option for many phobias. Virtual environments are used to simulate varying levels of anxiety in users. While being monitored by a therapist, users can navigate and interact with avatars and items in these environments. To do this, a virtual world is created with Unity and Blender 3D and then integrated into the Oculus quest virtual reality glasses. Patients will be taken to - house-like or building sets and progressively exposed to insects, heights, water, and darkness while progressively nearing them with no real risk or further exposure to trauma through the Oculus quest. This will be made as realistic as possible to make the patient feel more at ease while also assisting them in adjusting to their concerns in environments where these phobias are more common. Also, a hardware unit was introduced to monitor the heart rate sensor which is interfaced with ESP8266. Our solution gives the therapy to the patients who undergo Nyctophobia, Acrophobia, Entomophobia, and Aquaphobia.
Hardware for Quantized Mixed-Precision Deep Neural Networks

Due to network and privacy issues, there is a push to perform Deep Learning (DL) computations on the edge rather than on the cloud. However, state-of-the-art Deep Neural Networks (DNNs) require vast resources that are limited on edge devices. This limitation has brought the need to design custom hardware to implement DL-specific optimizations. Traditionally DNNs have run on high-precision number formats; however, DNNs are surprisingly robust and do not require this much precision. Instead, using quantization, networks can run on low-bit widths while achieving fair accuracy. This finding suggests that edge devices can run resource-efficient DNNs at the cost of accuracy. In addition, layers within a network do not require the same precision. Therefore, a further optimization suggests using mixed-precision over uniform quantization. A comparison is made between an architecture—designed to support mixed-precision DNNs in hardware—and its single-precision counterpart.
Novel Method to Manage Student Questions in the Anatomy Laboratory Using a Virtual Meeting Platform

Large class sizes and often few instructors in anatomy courses make it challenging for student laboratory groups to have their questions addressed in a timely manner. Instructors are often unaware of the number of requests for assistance, as well as the order in which assistance is requested, and students often spend a long time waiting for an instructor to become available. As a result of brainstorming with some of our students, a call button system of sorts was suggested. Instructors in consultation with the college’s IT department came up with the idea of using Zoom Meetings question and answer (Q&amp;A) feature to manage student questions. Zoom allows one to broadcast a Zoom Meeting to up to 50,000 participant attendees, and instructors, logged in as panelists (on a mobile device, e.g., iPad), can interact with the student attendees via the Q&amp;A feature. The students join the webinar using their dissection table number as their ID and request assistance in the Q&amp;A. These requests show up with a time stamp and are automatically queued on the panelist’s Q&amp;A window. Instructors employ the type answer feature to acknowledge the question by typing in their respective initials, which can be seen live by the other instructors (panelists). This allows student questions to be queued so that the instructors can address them in a timely, first-in/first-out order. Student feedback regarding the use of this system for the Small Animal Anatomy course was positive.
Pemanfaatan Kain Flanel sebagai Alat Peraga Pendidikan Bagi Anak Usia Dini

This service aims to provide training in utilizing flannel as a prop for early childhood. The method used in this service is First, Strengthening knowledge about the importance of learning media and their use in learning activities. Provide insight into learning media appropriate for early childhood. Second, the introduction of learning media that can be applied in the classroom, and finally training on making educational props made of flannel along with the determination of learning steps in the classroom carried out by the entire service team and service participants. The result of this service is that participants succeeded in making a busy book with the theme of knowing numbers, recognizing letters, and counting which is included in the excellent category. Keywords: Bussy book, flannel, props
A Graphical-Based Video Steganography for Information Security

In today’s digital media, data communication over the Internet increasing day by day. Therefore, the data security becomes the most important issue over the Internet. With the increase of data transmission, the number of intruders also increases. That is the reason it is needed to transmit the data over the Internet very securely. Steganography is a popular method in this field. This method hides the secret data with a cover medium in a way so that the intruders cannot predict the existence of the data. Here, a steganography method is proposed which uses a video file as a cover medium. This method has five main steps. First, convert the video file into video frames. Then, a particular frame is selected for embedded the secret data. Second, the least significant bit (LSB) coding technique is used with the double-key security technique. Third, an eight characters password verification process is applied to secure the data. Fourth, reverse the encrypted video. Fifth, the signature verification process to verify the encryption and decryption process. These five steps were followed for both the encrypting and decrypting processes.
An Autonomous Musical System via REST API Network

Today game engines deal with not only game contents or assets, but also gain the ability to play music. We will soon see that the DAW (digital audio workstation) and the game engine will be merged into a more general and sophisticated musical environment. The authors are developing a prototype ensemble system in which several client PCs are managed with a REST http server. Each client plays its cello, piano, or some synthesizer part conducted by the server. The clients also send control signals to others to change the tempo, rhythm or pitch improvisationally. Experimental results show that the Modular Synth component in the Unreal Engine 4 can play pieces of chamber music composed by the authors almost synchronously via LAN.
The analysis of local correlation characteristics of human bioelectric signals while performing cognitive tasks

In this work, within the Memory Functions Formalism, we carry out an autocorrelation analysis of local segments of electroencephalogram signals for two groups of people. The first group consisted of 8 musicians professionally engaged in music for at least 5 years, the second group was represented by 11 people without any musical education. The subjects performed the tasks of perceiving a fragment of a musical work and perceiving a text. After determining the values of autocorrelation parameters for all time intervals, quantile regression analysis is additionally applied. As a result, areas of the cerebral cortex were identified, in the activity of which the greatest differences are manifested for the two groups of people. Finally, it is shown that the use of statistical analysis methods makes it possible to detect differences in the cognitive abilities of people when performing various cognitive tasks.
Modelling and Structuring Narrative Historical Sources with Temporal Context

Creating semantically well-structured data from various historical sources is a challenging task as its contents are quite complicated and less structured. Factoid Prosopography Ontology (FPO), a source-oriented data model for prosopography, tackles this problem and enables the knowledge representation of historical information. However, we highlight two issues that FPO does not sufficiently address. The first issue relates to the temporal expression. While FPO provides a way of attaching date or the date range to historical phenomena, it is based on absolute time information, which is not always given in historical sources. To overcome this, we introduce a way of expressing relative time information by describing a temporal sequence of events mentioned in sources. Second, the more important issue is how to describe the context for each entity mentioned in sources. This context information is not explicitly described in sources but plays an essential role in reading historical sources. For this, we introduced a new concept EntityInContext, which represents ‘an actor in certain context’ as a single Uniform Resource Indicator (URI), and an instance of EntityInContext is defined based on the events’ sequence described earlier. The validity of the model was clarified by creating actual data for Caesar's Bellum Gallicum and attempting a SPARQL search.
STARS Ignite: A Program for Supporting Professors in Organizing Student Cohorts for Conferences

Academic computing departments are seeking ways to broaden participation in computing (BPC), and many are encouraging individual faculty, staff, and students to attend diversity-oriented conferences, like the Tapia and STARS Celebrations of Diversity in Computing and Grace Hopper Celebration of Women in Computing. The purpose of the STARS Ignite Workshop is to provide faculty and staff with a framework including the tools and knowledge needed to make such conference attendance beneficial both to the students attending the conference and to the sponsoring faculty and their department. In this workshop, participants will learn to recruit and lead a BPC purpose-driven student conference cohort and the implementation of a BPC event or program. Participants will be provided with opportunities to adapt sample materials (recruitment emails, applications, etc.) to their own needs, assess the BPC needs at their institutions, and learn how to determine if their BPC efforts are successful. A laptop and Google account are required for this event. Interested attendees should be willing to commit to leading a student conference cohort to a diversity-oriented computing conference and then implementing a BPC event or program with these students.
Blockchain for Securing Internet of Things – A Layered Approach

The latest development in the Internet of Things (IoT) has brought about a revolution in modern-day communication. Today, the world becomes much smaller with the power of the internet. Moreover, it has opened the window of some of the most prominent concepts such as smart health, smart transportation, smart supply chain management, and many more. For its vast implementation and ubiquitous usage, it also needs to maintain concrete security, privacy, and validation procedure. There are many security flaws in the existing IoT system of different architectures. Many solutions are proposed too to mitigate these issues. In recent times, blockchain has shown great potential to this ecosystem in terms of authentication and data transmission processes. Some recent works have shown the implementation of various IoT prototypes. However, more drawbacks are yet to be solved before implementing it on a large scale. This chapter shows some of the most recent cyberattacks and security issues discovered in the IoT ecosystem upon discussing the IoT principles. Later, we present an investigation on the recent approaches to security issues and blockchain-based solutions. Finally, we have pointed some future research works for combining the two latest technologies – blockchain and IoT.
Produkte: Vakuum in Forschung und Praxis 4/2022

Vakuum in Forschung und PraxisVolume 34, Issue 4 p. 10-11 Magazin Produkte: Vakuum in Forschung und Praxis 4/2022 First published: 25 August 2022 https://doi.org/10.1002/vipr.202270403AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume34, Issue4August/September 2022Pages 10-11 RelatedInformation
Bert-based Text-to-SQL Generation method with question-table content enhancement and template filling

Generating SQL queries through natural language is a way to both reduce the cost of learning SQL for users and improve the efficiency of data queries, and has attracted great interest in recent years. To address this problem, the text proposes a new approach: building feature vectors by linking question and table contents, then building a classification model on top of a pretrained BERT for fine-tuning, and breaking down the Text2SQL process into multiple sub-processes based on template populated multi-classification, using both a general classification model and a conditional value classification model to generate SQL. The conditional value model relies on the results of the general classification model and is optimized using an exhaustive combination. This approach was tested on the WikiSQL dataset and yielded at least 9.8% improvement in accuracy and very good results on the decomposed subtasks, with an accuracy of 85.3% on the test set.
Weapon System Effectiveness Evaluation Method Based on Normal Distribution FSVM

In view of the disadvantages of not making full use of the data generated in the experiment and actual combat and the strong dependence of experience in the effectiveness evaluation of weapon system, a combination of machine learning and statistical technology is proposed to evaluate the effectiveness of weapon system. Based on the establishment of the weapon system tree type index system, the method of calculating the weapon system effectiveness is defined. Under the circumstance of the establishment of the tree index architecture of the weapon system, the intrinsic attributes of the weapon system index are further analyzed, and the fuzzy support vector machine regression method based on normal distribution is used to evaluate the effectiveness of the weapon system, so as to improve the rationality and accuracy of the effectiveness evaluation of the system. It provides a reference for other methods to calculate weapon system effectiveness and the development and improvement of effectiveness evaluation software.
Resúmenes al Español

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Resúmenes al Español. Am J Intellect Dev Disabil 1 September 2022; 127 (5): 433–434. doi: https://doi.org/10.1352/1944-7558-127.5.433 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
Nearest neighbour-based feature selection and classification approach for analysing sentiments

Sentiment analysis is considered as one of the most important aspect in the field of research. The aim of the paper is to select features and perform classification of data using positive and negative. The objective of the proposed work is to analyse sentiment and perform classification. In traditional feature selection methods, the word order in the given documents is not considered and hence it will be a tedious process to compute the features. Existing sentiment analysis techniques do not predict the context and the similarity among the words. The proposed method performs the feature selection of data using the nearest neighbour-based approach where initially the distance metrics and the cosine similarity of the data are calculated based on the pre-processed data. The main aim is to perform feature selection and tune the hyper parameters to get the optimal value for improving performance. Experiments have conducted using several feature vectorisation methods to obtain better accuracy.
Nonparametric and Online Change Detection in Multivariate Datastreams Using QuantTree

We address the problem of online change detection in multivariate datastreams, and we introduce QuantTree Exponentially Weighted Moving Average (QT-EWMA), a nonparametric change-detection algorithm that can control the expected time before a false alarm, yielding a desired Average Run Length (ARL<inline-formula><tex-math notation="LaTeX">$_{0}$</tex-math></inline-formula>). Controlling false alarms is crucial in many applications and is rarely guaranteed by online change-detection algorithms that can monitor multivariate datastreams without knowing the data distribution. Like many change-detection algorithms, QT-EWMA builds a model of the data distribution, in our case a QuantTree histogram, from a stationary training set. To monitor datastreams even when the training set is extremely small, we propose QT-EWMA-update, which incrementally updates the QuantTree histogram during monitoring, always keeping the ARL<inline-formula><tex-math notation="LaTeX">$_{0}$</tex-math></inline-formula> under control. Our experiments, performed on synthetic and real-world datastreams, demonstrate that QT-EWMA and QT-EWMA-update control the ARL<inline-formula><tex-math notation="LaTeX">$_{0}$</tex-math></inline-formula> and the false alarm rate better than state-of-the-art methods operating in similar conditions, achieving lower or comparable detection delays.
Reacting in Style

We will discuss different ways of styling components and their pros and cons in this chapter. We'll go through CSS-in-JS, Styled Components, CSS, SCSS, and CSS modules. The chapter will not make any recommendations on which method you should use for your React app, but it will shed light on how a React app reacts to different styling methods. I will also introduce you to some tools that can help you choose styling for your React apps.
Interoperability and Standardization of Web Navigation Taxonomies: Clarifying some Operational Concepts and Developing a Tentative Basic Vocabulary

This book explores the latest advances in how knowledge organisation can both draw upon and inform diverse disciplines and technological developments. It explores how best to combine theory and practice, with its contents considering practical solutions as well as the theory behind the design, development and implementation of knowledge organising systems, ranging from controlled vocabularies, classification systems and metadata schemas to ontologies and taxonomies. With contributions by Carmen Agustín-Lacruz, Mayukh Bagchi, Mario Barité, Maria Teresa Biagetti, Briony Birdi, Lynne Bowker, Thiago Henrique Bragato Barros, Ana Inés Brozia, Gema Bueno-de-la-Fuente, Julia Bullard, Rafael Cacciolari Dalessandro, José Augusto Chaves Guimarães, Christian Cole, Stephanie Colombo, Luciana Corts Mendes, Luis Corujo, Niamh Nic Daeid, Patrícia de Almeida, Hadassa De Zen Itepan, Anthony Dunbar, Jonathan Furner, Francisco-Javier García-Marco, Fausto Giunchiglia, Claudio Gnoli, Míriam Gontijo de Moraes, Hazlem Granados-Amaya, Hugo Alberto Guadarrama-Sánchez, Carlos Guardado da Silva, David Haynes, Ania Hernández Quintana, Philip Hider, Chris Holstrom, Eva Hourihan Jansen, Fidelia Ibekwe, Mat Kelly, Michael Kleineberg, Nair Yumiko Kobashi, Deborah Lee, Maël Le Gall, Joaquín López del Ramo, Luís Miguel Oliveira Machado, Hugo Moquet, Catalina Naumis-Peña, Mirtha Rauch, Mariana Ramos Crivelente, Raissa Resende de Moraes, Jorge Revez, Riccardo Ridi, Somayeh Roshan, Clarissa Schmidt, Negin Shokrzadeh Hashtroudi, Richard P. Smiraglia, Mariângela Spotti Lopes Fujita, Adriana Suárez-Sánchez, Erik Stubkjwær, Rick Szostak, Seyed Mahdi Taheri, Marc Tanti, Ana Lúcia Terra, Amanda Terrero Trinquete, Natalia Bolfarini Tognoli, Jéssica Beatriz Tolare, Raquel Torrão Valentim, Hyung Wook Choi and Mohsen Haji Zeinolabedini.
Research Methods and Progress of Text Sentiment Analysis Based on Machine Learning

With continuous development of artificial intelligence, Natural Language Processing (NLP) has become an emerging research field. As an important branch of NLP, text sentiment analysis has drawn attention from many scholars. At present, the mainstream methods of text sentiment analysis include text sentiment analysis method based on sentiment dictionary, machine learning, deep learning, and mixed strategy. The method based on the sentiment dictionary is no longer commonly used because it requires a lot of manual annotation. Due to traditional machine learning methods cannot perform good classification and prediction of context semantics, but deep learning can solve this problem, more and more researches will adopt a combination of the two methods. By investigating the current research at home and abroad, this paper describes and compares the aforementioned four methods in detail, summarizing their advantages and disadvantages, and proposing possible challenges in future research.
TAGGING: Setswana Complex Qualificatives &amp; Adverb

In Natural Language Processing(NLP) we have processes like Part-of-speech (POS) tagging where words in a given text are labeled with their corresponding parts of speech whether a word is a noun, verb, adjective, or adverb. Setswana is a Bantu language spoken in Botswana, Namibia, Zimbabwe and South Africa and is written disjunctively. Disjunctive writing means that the group of words could be combined together to form one function or one part of speech. Setswana has different parts of speech including qualificatives and adverbs which could be formed by multiple words. Qualificatives and adverbs can be recursively extended to form complex parts of speech and this makes it challenging to tag them in a sentence. Some research has been done before to tag Setswana parts of speech but they did not cover complex qualificatives and adverbs. The aim of this study was to identify and tag qualificatives and adverbs. Additionally our focus was to combine qualificatives or and adverbs in a sentence using rule based methodology. A Morphological analyzer was used to tag individual words. Regular expressions as a pattern matcher adopted from NLTK library was used in our generated rules to create regular expression which were fed into the algorithm to detect and tag qualificatives and adverbs. Within the algorithm, a noun class module was used to determine which noun complies with which concord. Precision and recall metrics were used in seven hundred sentences during testing and the model gave an accuracy of 86&#x0025;. We have realized that most of the sentences in Setswana were correctly tagged. For sentences which gave incorrect results, their rules were not generated and some of the words were not included in the dictionary. Moreover there were ambiguities in some of the sentences, which was the other factor that contributed to the 14&#x0025; inaccuracy.