Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review on Indian Sign Language for Deaf and Dumb People

Abstract: In sign language, hand gestures are one of the nonverbal modes used. It is most commonly used by deaf and dumb people who have hearing or speech problems to communicate with other deaf and dumb people or non-deaf people. It is also a piece of software that demonstrates a system prototype capable of automatically recognizing sign language, allowing deaf and dumb people to communicate more effectively with one another and with the general public. Dumb people are permitted to refuse normal communication with other members of society. Ordinary people find it difficult to understand and communicate with them. Deaf and Dumb people must communicate with an interpreter or some form of visual communication. Visual communication is notoriously difficult to learn, and a translator will not always be accessible. understand. The deaf and dumb community's principal mode of communication is sign language. Because the average person does not understand the syntax or meaning of many of the gestures used in sign language, it is mostly utilized by the families of the deaf and dumb. Keywords: Deaf and Dumb, Visual Communication, Disability, Machine learning, Indian Sign Language, Sign Language Recognition, Gestures.
A Survey on Sentiment Analysis and Topic Modeling

Sentiment Reason Mining is an emerging research area in this era of social media. Sentiment Reason Mining aims to resolve two problems: first is finding the reason of a sentiment, and second is interpreting sentiment variations. Time and Event where sentiment is being changed is also an important factor. Aspect-Based methods, Supervised Learning, Topic Modeling, and Data Visualization etc. can be used for finding the reason of a sentiment. VADER Sentiment Classifier can be used for sentiment of tweets. LDA is topic Modeling algorithm. In this research paper we have reviewed some the research work performed for this purpose. We have reviewed various research work which have used social media content as dataset. TF/IDF feature extraction is used in most of the work. Sentiment Detection tools VADER and Text Blob are also discussed in our work.
Perception advantages of foreign directed speech

Foreign directed speech (FDS) is a listener directed speech style used when native speakers interact with non-native listeners of a language. This study considers if native and non-native listeners benefit from the phonetic features of FDS in English. 43 native English speakers and non-native speakers were recruited on Amazon Mechanical Turk. Participants were presented with an audio clip and two pictures. They were asked to click on the correct image based on the audio given with reaction times recorded. Each participant was given a randomized order of speech tokens: 12 tokens from the two speech conditions, native speech (NS) and FDS. The data showed that speech condition had a significant effect on reaction time, where FDS yielded faster reaction times than native speech (est = −0.024, t = 2.035, p = 0.04). Listener language background did not have a significant effect, with both groups performing similarly in reaction times across both speech conditions. The results of this study show that the phonetic features of FDS are beneficial for comprehension when compared to native speech regardless of listener language background. This may be because the phonetic features of FDS are similar to the characteristics of other listener directed speech styles.
Perception and timing of acoustic distance

The notion of acoustic distance figures into many aspects of phonetics, including phonological neighborhoods. A measurement of word-level acoustic distance useful for cognitive modeling must account for two listener characteristics: sensitivity to acoustic differences and sensitivity to duration discrepancies between words. The present work used dynamic time warping to measure how acoustic distance accumulates between words over time. The results of a distance rating task with synthesized vowels were used as a basis for selecting a mathematical function that best matched listener sensitivities. Additionally, the results of a reminder task with synthesized vowels were used to determine a just noticeable difference threshold for vowel duration. The results suggested that a distance function based on the 4.5-norm using a 30 ms radius for dynamic time warping best matched human behavior. A third analysis used these dynamic time warping configurations to model reaction times in an auditory lexical decision task and found that Euclidean distance and no temporal constraints on dynamic time warping best matched human behavior. These results are discussed in relation to spoken word recognition models, including how to assess the acoustic match between the speech signal and a word in the lexicon.
Multi-wheel drive binocular exploration robot based on ROS system

Aiming at the problems of poor adaptability and insufficient functions of existing exploration robots in different caves and dangerous areas, this paper proposes a multi-wheel drive binocular exploration robot based on ROS system, which uses laser radar sensor and binocular depth vision camera to collect environmental data. The Gmapping algorithm is used to realize the real-time positioning and accurate map building of the robot system. The CCD vision camera collects the data of the exploration area in real time, and transmits the data to the host computer for analysis and processing. Through the ROS open source system, it is convenient to control the robot and receive the mapping data, navigation data and exploration data of the robot system on the upper computer. Through experiments,Compared with the existing exploration system, the system has the characteristics of low hardware cost, easy mastery, easy modification, strong function expandability and the like; This project has a very high application value in the exploration of caves and dangerous areas.
Efficient Algorithms for Monotone Non-Submodular Maximization with Partition Matroid Constraint

In this work, we study the problem of monotone non-submodular maximization with partition matroid constraint. Although a generalization of this problem has been studied in literature, our work focuses on leveraging properties of partition matroid constraint to (1) propose algorithms with theoretical bound and efficient query complexity; and (2) provide better analysis on theoretical performance guarantee of some existing techniques. We further investigate those algorithms' performance in two applications: Boosting Influence Spread and Video Summarization. Experiments show our algorithms return comparative results to the state-of-the-art algorithms while taking much fewer queries.
Client-side ciphertext deduplication scheme with flexible access control

Data deduplication techniques with fine-grained access control have been applied to realise data sharing while saving the amount of storage space. However, many existing schemes can only achieve server-side deduplication, which greatly wastes the network bandwidth especially when the transmitted data is particularly large. Moreover, few existing schemes consider attribute revocation, in which the forward and backward secrecy cannot be guaranteed. To address the above problems, in this paper, we present a client-side ciphertext deduplication scheme with more flexible access control. Specifically, the data owners are divided into different domains and distributed corresponding domain keys through the secure channel, achieving proof of ownership in client-side deduplication. Besides, attribute revocation is realised through the proxy re-encryption, which need not preset the maximum number of clients in system initialisation phase. Security and performance analysis demonstrate that the proposed scheme can achieve desired security requirements while realising the efficient client-side deduplication and attribute revocation.
Pose Based Pedestrian Street Cross Action Recognition in Infrared Images

In the context of a traffic scenario captured during night with infrared cameras we focus on pedestrian street cross action and we study the influence of the pedestrian pose with respect to the road environment on the accuracy of the action recognition model. This paper presents a complete framework that performs pedestrian cross action recognition for infrared sequences captured mainly during night but also during day time. The main contribution of the paper resides in the study of the variation in pedestrian action recognition accuracy provided by the combination of pedestrian pose-based features with several road context features given by semantic segmentation networks. The main modules of the proposed framework consist in a YOLO based infrared pedestrian detector combined with a tracking algorithm that enhances the detections. A CNN based pose estimator is applied on detected pedestrians in order to extract the relevant keypoints of the pedestrian skeleton. Several semantic segmentation networks like U-Net, FCN and PSPNet have been adapted in order to perform the semantic segmentation of the road in infrared images. Pose features are combined with road context features provided by the semantic segmentation and input to a LSTM based cross action recognition network. The obtained results provide a 90% accuracy on CROSSIR dataset.
The Meaning of Early Childhood Music Education Class Based on Artistic Imagination for Pre-Service Teachers

본 연구는 유아음악교육 교과 특성상 상상력과 매우 깊은 관련이 있으므로 유아교사로서 필요한 능력으로 상상력을 훈련하는 기회가 필요하다는 인식 아래, 예술적 상상력 기반의 유아음악교육 수업을 통하여 예비유아교사가 경험하는 것은 무엇인지 그리고 그 수업이 예비유아교사에게 어떤 의미가 있는지를 알아보고자 하였다. 이를 위하여 예술적 상상력 개념을 정의하고 이 개념에 기초한 유아음악교육 수업을 구성하여 예비유아교사 49명을 대상으로 수업을 진행하였다. 본 연구에서 수집된 반성적 저널, 면담, 연구자 일지 등의 자료를 분석한 결과는 다음과 같다. 첫째, 이 수업에서 예비유아교사의 경험은 ‘상상과 표현: 느낌에 집중하기’, ‘즉흥과 창작: 흐름에 따라 가보기’, ‘음악: 함께여서 더 좋은 것’으로 나타났다. 둘째, 예비유아교사에게 이 수업의 의미는 ‘진실한 나: 실수해도, 못해도 괜찮아’, ‘음악의 일상성: 쉽고 즐겁게 할 수 있잖아’, ‘음악교육에 감(感)잡기: 아, 이렇게 하는 거구나’로 나타났다. 이 연구결과는 예술적 상상력 기반 유아음악교육 수업이 감각에 집중하고 자신의 심상을 다양한 이미지로 표현하는 기회를 제공하며 자기 자신, 음악, 그리고 교육의 본질에 대해 깊이 되돌아볼 수 있는 기초교육으로서의 가치가 있음을 보여주고 있다. 또한 교사의 상상력을 갖춘 유아교사를 배출하기 위해 유아교사 양성기관에서 상상력 교육의 기회를 확대하는 노력이 필요함을 시사하고 있다.This study tried to find out what pre-service teachers experience through an early childhood music education class based on artistic imagination and what it means to pre-service teachers, under the recognition that the nature of the early childhood music education curriculum is very deeply related to imagination. In this study, 49 pre-service teachers participated in the early childhood music education class based on artistic imagination. The results of the analysis of data such as reflective journals, interviews, and researcher s journal collected in this study are as follows: First, the experience of the pre-service teachers in the class turned out to be Imagination and expression: focusing on my feeling, Immediate and creative: going with the flow, and Music: Better Together. Second, for pre-service teachers, the meaning of the class turned out to be True me: I can make mistakes, I can t do them , The everyday nature of music : I can do them easily and amused , and Having an insight into music education: I can do this . The findings show that early childhood music education class based on artistic imagination offer opportunities to focus on the senses, express one s feelings in various images, and value it as a basic education to reflect deeply on oneself, music, and the nature of education. It also suggests that efforts are needed to expand the opportunities for imaginative education in the institutions for the training of early childhood education teachers to develop a teacher s imagination.
Shared Augmented Reality Experience Between a Microsoft Flight Simulator User and a User in the Real World

Our video shows an application where a user with an augmented reality (AR) display watches another user, flying an airplane in the Microsoft Flight Simulator 2020 (MSFS), at their respective location in the real world. To do that, we take the location data of a virtual 3D airplane model in a virtual representation of the world of a user playing MSFS, and stream it via a server to a mobile AR device. The mobile device user can then see the same 3D airplane model at exactly that real world location, that corresponds to the location of the virtual 3D airplane model in the virtual representation of the world. The mobile device user can also see the plane movement updated according to the 3D airplane movement in the virtual world. We implemented the application on both a cellphone and the Hololens 2.
A Time-varying Mixture Memory Multiplicative Error Model

The dynamics of financial volatility shows a behavior characterized by alternating periods of turbulence and relative quiet.We suggest modelling it as a mixture memory model where time-varying mixing weights are a function of some forcing variable capable of sudden changes.In choosing a mixture approach we rely on previous evidence on the presence of a short-and a long-memory component in the observed series.We apply our model to the main Spanish stock index (IBEX) using the spread between the sovereign national and German bond rates as the forcing variable.The results show a good performance in sample, pointing to the fact that fixed weights may be a limitation to an accurate description of volatility behavior.
S-transform-based efficient copy-move forgery detection technique in digital images

Copy-move forgery (CMF), which copies a part of a picture and pastes it into another location, is one of the common strategies for digital image tampering. Due to the arrival of high-performance hardware and the compact use of image processing software, empowers creating image forgeries easy that are undetectable by the naked eye. For CMF detection, we suggest an efficient and vigorous method that could take care of numerous geometric ameliorations including rotation, scaling, and blurring. In the projected CMF detection system, we use Stockwell transform (S-transform) which hybrids the advantages of both scale invariant feature transform (SIFT) and wavelet transform (WT) to extract the key points and their descriptors from the overlapped image blocks. Furthermore, Euclidean distance (ED) between the overlapped blocks are measured to detect the similarities and to identify the tampered or forged region in the image. Besides, a novel fuzzy min-max neural network-based decision tree (FMMNN-DT) classifier is used to recognise the duplicated regions in the forgery image. The proposed system is tested and validated using MICC-F220 dataset and we present comparison among the proposed outcomes with some existing ones which ensure the significance of the proposed.
Prediction of Misclassification Data using Cognitive Bayes Computation Techniques (COBACO)

Missing data arise major issues in the large database regarding quantitative analysis. Due to this issues, the inference of the computational process produce bias results, more damage of data, the error rate can increase, and more difficult to accomplish the process of imputation. Prediction of disguised missing data occurs in the large data sets are another major problems in real time operation. Machine learning (ML) techniques to connect with the classification of measurement to enforce the accuracy rate of predictive values. These techniques overcome the various challenges to the problem of losing data. Recent work based on the prediction of misclassification using supervised ML approach; to predict an output for an unseen input with limited parameters in a data set. When increase the size of parameter, then it generates the outcome of less accuracy rate. This article presented a new approach COBACO, an effective supervised machine learning technique. Several strategies describe the classification of predictive techniques for missing data analysis in efficient supervised machine learning techniques. The proposed predictive techniques COBACO generated more precise, accurate results than the other predictive approaches. The Experimental results obtained using both real and synthetic data set show that the proposed approach offers a valuable and promising insight to the problem of prediction of missing information.
Spatial Focusing Effect for Networking

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Walking-Aid Cane Robot Using Light Touch Effect

We propose a new a lightweight and compact single-wheeled pendulum-type robot to assist rehabilitation using light-touch effect. By applying the light touch effect, walking is stabilized by reduction of swing of center of the gravity. In addition, we examined the use of multiple sensors to determine external situations, estimate the user's intention, and make operational announcements. Then, it was verified walking stabilization by two cane robots. As a result of the experiment, it was confirmed that the center of gravity swing is more stable than when walking with two cane robots using one cane robot.
Detection and Classification of Breast Micro-calcifications with Mammography: Effect of Temporal Subtraction of Consecutive Mammograms.

Thank you very much for joining EUSOBI 2021 ONLINE and celebrating the Breast Cancer Awareness Month with us! This year we had a terrific programme and we are indebted to Dr. Ritse Mann and his Programme Committee for all their hard work in putting this together. We had a wonderful selection of international speakers who gave the latest updates in all your favourite topics and ensured that you are kept up to date with the latest developments in breast imaging. A special focus was given to the interactivity of this year’s programme, by adding expert discussions and concluding with the first virtual edition of our famous Breast Interpretation Quiz. Thanks to all of you who submitted abstracts and therefore having been an important part of this scientific event. We are also very grateful for the support of our industry partners who are listed here: https://www.eusobi.org/congress-sponsors-2/ Most of them have contributed to the extension of our scientific programme by offering interesting sessions and workshops. Have a look by scrolling through all sessions. All sessions have been recorded and are available on-demand.
Web-Based Tracking Systems for Electronic Resources Management

Robert Alan reported on the development of ERLIC2, Penn State’s tracking system, which will include optical imaging of critical documentation such as licenses, access and authentication information, financial information, and statistics for library staff, as well as information on access problems and new products. Lai-Ying Hsiung reported on how UCSC is using the Web-based program Request Tracker to receive queries from their users, share responsibility for responding to these communications, and archive the resolved transactions. Both projects satisfy specific local needs in responding to the problem of managing electronic resources. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: &lt;docdelivery@haworthpress.com&gt; Website: &lt;http://www.HaworthPress.com&gt;]
Artificial Intelligence Techniques for Information Security in 5G IoT Environments

The development of the telecommunication networks observed in present and future time is impressive. Today we witness rapid implementation of 5G networks. We can say that this actually is the moment when (artificial intelligence) AI enters at small door but in the beyond 5G world it is expected to have the prime role in smart operation, management and maintenance of non-software defined networking (SDN), network function virtualization (NFV) and especially at SDN and NFV aware networks. Number of standardization body’s and work groups are focused in a way to create a framework that will define the future use of AI and security standards necessary to exist in order to create health environment for the next generation telecommunication infrastructure. In the wireless world AI/Machine learning (ML) has great potential to shake the way we operate and to become foundation of the transformation that leads to the next industrial revolution. Network virtualization gives flexibility and freedom of the telco operators to choose the hardware and network topology they need for AI/ML platforms and big data sets. 5G and IoT create positive environment for AI and ML development and usage. As the network requirements are developed and the number of the users raises, gains are expected to grow with the number of variables and the interactions among them so it becomes impossible to relay on humans to control the network for increased number of variables and this is why AI with ML and automation become beneficial and necessity to run the future networks. AI generally is defined as capacity of mind or ability to acquire and apply knowledge and skills while ML is defined as learning that does not require explicit programming. Combined usage of AI and ML can optimize almost any component of the wireless network, this does not mean that it should be used everywhere mainly because at the end of the day the cost benefit analysis of its usage must be positive. Smart operation, management and infrastructure maintenance (SOMM) networks are defined as: Intelligent, data driven, integrated and agile. Today AI is introduced but in future it will represent the network engine. It is interesting to mention that network security must be upgraded because the network will provide services for massive number of IoT devices that will have variety of functions and requests. AI/ML can improve the security services and to be used in order to elevate them at advanced level. In this text we focus our attention at AI/ML and security scenarios defined for IoT in 5G environment.
Supporting FAIR* data discoverability in clinical research: building an object metadata repository of clinical trial objects

The ECRIN certification programme 1 is designed to audit data centres to confirm their ability to provide compliant effective and efficient data management services for clinical trials as well as to provide a clear interpretation of regulatory and good practice requirements.
Low Latency Live Streaming System with Congestion Control

In recent years, the traffic for live streaming on the web has been increasing. The current live streaming methods that use MPEG-DASH or HLS are simple and scale easily to many clients using HTTP. However, they do not take into account the communication between the distributor and the viewer. As a result, latency between the distributor and the viewer is relatively high. Therefore, in this paper, we propose a low latency live streaming system on the web using WebRTC. Since WebRTC uses UDP, it does not have a congestion control mechanism. Depending on the network congestion, it is possible to stream video with quality that exceeds the available bandwidth. Therefore, we propose a system to change the video quality based on the congestion status. The proposed system increases or decreases the video transfer rate by changing the quality of the streamed video depending on the network conditions. We have evaluated the proposed system in a real network environment. As a result, we showed that the delay of the proposed system is smaller than that of the MPEG-DASH system. We also showed that the proposed system can change the quality of the video and switch the transmission rate appropriately according to the network conditions.