Reviewerly/exHarmony huggingface dataset:
Gesture Recognition Glove For American Sign Language Using Accelerometers

Sign Language Recognition is a vast domain with a variety of solutions available on the market which achieve the same results, translating hand gestures to text and/or audio. There is a plethora of sign languages used globally, but the mostreputed and commonly used sign language is the American Sign Language (ASL). ASL uses hand gestures and finger spelling to convey the English alphabets and words. The two methods of implementing sign language translation are: Image Processing and Gesture Recognition. The gesture recognition gloves available in the current market utilize expensive flex sensors to map the bend of the users’ fingers. This makes the overall cost of the gloves expensive. Through ‘‘ASL Sign Language Recognition Gloves we aim to implement gesture recognition with MEMS accelerometers that are comparatively cheaper and make the glove more affordable.
Large scale performance analysis of distributed deep learning frameworks for convolutional neural networks

Abstract Continuously increasing data volumes from multiple sources, such as simulation and experimental measurements, demand efficient algorithms for an analysis within a realistic timeframe. Deep learning models have proven to be capable of understanding and analyzing large quantities of data with high accuracy. However, training them on massive datasets remains a challenge and requires distributed learning exploiting High-Performance Computing systems. This study presents a comprehensive analysis and comparison of three well-established distributed deep learning frameworks— Horovod , DeepSpeed , and Distributed Data Parallel by PyTorch —with a focus on their runtime performance and scalability. Additionally, the performance of two data loaders, the native PyTorch data loader and the DALI data loader by NVIDIA, is investigated. To evaluate these frameworks and data loaders, three standard ResNet architectures with 50, 101, and 152 layers are tested using the ImageNet dataset. The impact of different learning rate schedulers on validation accuracy is also assessed. The novel contribution lies in the detailed analysis and comparison of these frameworks and data loaders on the state-of-the-art Jülich Wizard for European Leadership Science (JUWELS) Booster system at the Jülich Supercomputing Centre, using up to 1024 A100 NVIDIA GPUs in parallel. Findings show that the DALI data loader significantly reduces the overall runtime of ResNet50 from more than 12 h on 4 GPUs to less than 200 s on 1024 GPUs. The outcomes of this work highlight the potential impact of distributed deep learning using efficient tools on accelerating scientific discoveries and data-driven applications. Graphical Abstract
Addressing Challenges of Opinion Mining in Businesses

Customer satisfaction has become the primary target for each successful running business currently. They have the true potential in uplifting a tiny business overnight to completely shutting down a brand. Real time analysis of customer opinion (sentiment) is the backbone of this success. Thus this piece of research tries to focus light on the challenges encountered by businesses in four categories of opinion mining. Natural Language Processing with machine learning algorithms works wonders for opinion mining. What is important is the correct choice of opinion mining for a specific type of business as each one of them demands an idiosyncratic approach. The research indeed shares a quick infusion for businesses to safeguard. Countless advice is beared by businesses to have real time analysis, but this research endeavor by showcasing its ideal infusion.
Era of Social Media Analytic Tools: A New Approach

Data is a word plays a vital role in every kind of aspects. In earlier days data can be expressed manually through communication channels. Day by day technology is enhanced in various etiquette. As per trends, technology is directly proportional to the data. Data will be transmitted from source to destination by using communication channel at wide range massively. The massive data is produced at the moment by using social media, as per today’s technology. Data communication will be passing through internet by adopting various kinds of several media platforms (Face book, twitter, snap chat). Raw data is in the form of unstructured where it moves to social media platforms. Unstructured data is unreadable in nature. To overcome this factor Social Media Analytics (SMA) is a weapon to organize the unstructured data into the manner of structured. To perform the analysis on the unstructured data, need to adopt different analytics tools. Among the different analytical tools, Tanagra is the best optimized tool to organized and analyzed the unstructured data and produces the users concerned data.&#x0D; Keywords: Structured Data, Unstructured Data, Social Media, Social Media Analytics
A Deep Learning Approach for Recognizing Malayalam Sign Language Gestures

Communication involves the sharing or exchanging of information, ideas, and emotions, which requires a common language between individuals. For those with speech impairments, sign language serves as a means of communication. The lack of understanding of sign language can create difficulties in communication between individuals with speech impairments and those without, posing a significant challenge. One solution to this challenge is to create a machine learning-based model that recognizes various sign language motions and converts them into a shared language. This model can be trained to benefit those seeking to interact and communicate with individuals with speech impairments. Although Sign Language Recognition systems exist, they are limited in the number of languages that can be recognized. This article introduces a real-time Sign Language Recognition system that employs transfer learning with TensorFlow and utilizes a webcam for the purpose of detecting and interpreting sign language gestures specific to the Malayalam language.
Schema Design with Intelligent Multi-Modelling Computing Techniques for Health Care Applications

Numerous kinds of research conducted in the area of material science, especially nanomaterial science, and the results are documented, with a special focus on the healthcare sector. As a result, this opens the door for nano informatics which collects the data from current research articles because databases contain physical, mechanical, biological, etc., properties from bulk materials as well as the information already available about nanomaterials available in a variety of structures and formats. The data is extracted from the existing research articles using Named Entity Recognition (NER) and finding the relationship between the entities is extracted using BERT (Bidirectional Encoder Representations from Transformers). To analyze the properties and behaviors from the available research study: Insilco, a database with the data from existing research papers needs of the hour work. The data derived is stored in the database. To create an index, the engine preprocesses material publication repositories to extract entities like material compositions, physical properties, chemical property, and mechanical property and their values. Around 600 research articles were used from PubMed nanohydroxyapatite material articles and then extracted the entities and relationship of properties and values. To create complex queries, it offers a domain-specific query language with a database. Researcher's look forward that these findings will quicken the rate of upcoming material science discovery and its analysis and application.
Basics

This chapter introduces the basic concepts of linear algebra, probability theory, and Bayesian networks. Readers familiar with these concepts may skip this chapter and proceed to Chap. 2 .
Exploratory Data Analysis of Titanic Survival Prediction using Machine Learning Techniques

It is very important to find out the root causes of human tragedies in the past so that future crises can be avoided. The incident of 15 April 1912 is an example of a human tragedy in which around 1500 passengers and working staff lost their lives. Continuous research in today's time shows that if the proper statistical assessment was done then it would probably be possible that human devastation could be reduced. In today's era, many new and powerful technologies are available, with the help of which accurate statistical calculations can be done. In this research study, Titanic survivals have been studied based on machine learning techniques. In the study, out of the total entities, 891 entities have been used for training and 418 entities have been used for the test set and the comparative study of different machine learning algorithms gives importance to this research study.
Prediction of Gender and Age Period from Periorbital Region with VGG16

Using deep learning methods, age and gender estimation from people’s facial area has become popular. Recently, with the increase in the use of masks due to Covid-19, only the eye area of people is seen. The periorbital region can give an idea about the person’s characteristics, such as age and gender. This study it is aimed to predict gender and age from images obtained by cutting the eye area from facial photographs of people using Visual Geometry Group-16 (VGG16). With the transfer learning method for age group (male, female) and gender group (child, youth, adults, and old) classification, 5714 images in the data set were used for the age group, and 3280 images were used for the gender group. As a result of this study, 99.41% success in age estimation and 95.73% in gender estimation was achieved.
Implementation of Augmented Reality Resources in the Teaching-Learning Process. Qualitative Analysis

In the present investigation, the qualitative results of an educational intervention are presented, which consisted in the implementation of augmented reality resources A.R in the teaching process, the objective of the investigation was to identify the different categories that arose in the speeches of the participants in relation to their experience in the application of augmented reality elements in the learning process, for which the open coding technique was used. The study, which lasted 8 weeks, was conducted with a population of 81 students, 45 women and 36 men, whose ages ranged between 13 and 16 years. Once the intervention was over, an in-depth interview was applied to identify the experiences of the participants, subsequently the information obtained was analyzed, identifying the following categories of description: a) learning experience with elements of augmented reality, b) adaptation to a new methodology of learning, c) motivation, d) benefits and disadvantages of the application of elements of A.R. The results show that many students interviewed gave answers in favor of the use of augmented reality technology, the participants pointed out that the learning experience with elements of augmented reality was innovative and interesting, in relation to the adaptation to a new methodology. of learning indicate that they did not have major difficulties once they became familiar with the operation of the different applications, the vast majority indicated that they felt motivated during the intervention, this being the greatest benefit as well as being able to access study topics in an effortless way. Interactive, which significantly improves their willingness to conduct learning activities, finally as disadvantages of the application of AR elements, they pointed out that the applications used require a lot of memory on mobile devices.
Business Email Compromise Challenges to Medium and Large-Scale Firms in USA: An Analysis

Business Email Compromise (BEC) attacks have emerged as a significant cybersecurity threat, leading to substantial financial losses for organizations.According to the FBI's Internet Crime Complaint Center (IC3), BEC attacks resulted in financial losses exceeding $1.8 billion in the USA in 2019 alone.Business Email Compromise (BEC) attacks have emerged as a significant cybersecurity threat, leading to substantial financial losses for organizations.According to the FBI's Internet Crime Complaint Center (IC3), BEC attacks resulted in financial losses exceeding $1.8 billion in the USA in 2019 alone.BEC attacks target a wide range of sectors.No industry is immune to these attacks, which emphasizes the need for increased vigilance across all sectors.Attackers often impersonate high-level executives or vendors to gain credibility and manipulate employees into complying with fraudulent requests.BEC attacks have a global reach, with threat actors operating from various countries, including Nigeria, Russia, China, and Eastern European nations.We will examine the unique difficulties SMEs encounter in relation to BEC attacks.This study provides a more excellent knowledge of the severity of the problem and offers ideas for efficient mitigation solutions through an investigation of attack characteristics, tactics, and impacts.
Clinical and Translational Science Personas: Expansion and use cases

Twelve evidence-based profiles of roles across the Clinical and Translational Science (CTS) workforce and two patient profiles were developed by CTS Personas collaborators in 2019 as part of the CTSA Program National Center for Data to Health (CD2H). Based on feedback received from the community, CTS Personas team members collaborated to produce five additional Personas to broaden representation of the CTS workforce and enhance the existing portfolio. This paper presents the rationale and methodology used in the latest CTS Personas initiative. This work also includes an implementation scenario incorporating multiple Personas. Using the new National Institutes of Health's (NIH) Data Management and Sharing Policy as an example, we demonstrate how administrators, researchers, support staff, and all CTS collaborators can use the Personas to respond to this new policy while considering the needs of service providers and users, CTS employees with short- and long-term needs, and interdisciplinary perspectives.
A System for Live Coding that Realizes a Unified Production of Real Space and Virtual Space

We are studying live coding and exploring its possibilities. As part of this, we have designed and developed a system that unified controls stage lighting in real space and objects in virtual space and made it possible to link with Sonic Pi, a musical live coding system. Furthermore, we tried to associate the representation of music by stage lighting in real space with the representation of objects in virtual space. This research is related to the fact that it not only introduces a new field of the control system to the conventional live coding, which mainly generates music and video in real time but also extends it to the virtual space and fuses it. It has a uniqueness that is not found in related works. As a result of this research, music-related live coders will be able to perform music by themselves while performing real space production with stage lighting and virtual space production with objects. This not only means that it is possible to produce stage and virtual spaces that directly reflect the performer's intentions but also has the potential to greatly affect the way live music and concerts are and how they are streaming.
Harnessing Deep Neural Networks for Accurate Offline Signature Forgery Detection

Signature is considered to be one of the broadly practiced and important biometric features for the verification of an individual. It is used in various documents like pan card, passport, bank cheques, etc which can lead to numerous frauds like identity theft, money laundering and so on. To help solve or reduce this issue, an attempt is being made to develop an offline signature forgery detection system using an artificial neural architecture known as the Siamese neural network which consists of two identical Convolutional Neural Network (CNN) which are trained to learn feature representations of genuine and forged signatures. The two individual Siamese Models implemented in our project provide an accuracy of 98.27% and 98.35% respectively. Additionally, on occasions one of the models performs better while the other gets confused. To get optimum results from the former, a logical system was developed for handling the corner cases which resulted in an accuracy of 99.75%
Mountain Segmentation Based on Global Optimization with the Cloth Simulation Constraint

Mountains are an important research object for surveying, mapping, cartography, space science, and ecological remote sensing. Automatic mountain segmentation is one of the most critical techniques in large-scale mountain analyses. However, several factors limit the segmentation accuracy, such as the complexity of mountains, the noise of geospatial data, and the confusion in distinguishing non-mountainous objects with similar features. In order to improve the accuracy of mountain segmentation against these limiting factors, we impose the cloth constraint over the digital elevation model (DEM) with the underlying assumption that the mountain has a sizeable relative elevation and slope. We propose a robust mountain segmentation method with the cloth simulation constraint. The core algorithm extracts the relative elevation of the region using a cloth simulation filtering algorithm by transforming the mountain segmentation problem into an optimization problem based on the global energy function consisting of the relative elevation and slope. Experiments on a wide range of Earth and lunar elevation datasets with some of the aforementioned limitations show that the proposed method can extract complex mountain baselines, avoid the misclassification of lunar craters, and significantly improve the robustness and accuracy of mountain segmentation. Compared to three state-of-the-art methods (the Lunar Mountain Detection Method, the Landform Mask Method in SNAP™ from European Space Agency (located in Paris, France), and the Multiscale Segmentation Method in eCognition™ from Definiens Imaging (located in Munich, Germany), the F1 and IoU improved by 14.70% and 20.46% on average and 29.07% and 38.94% at most, respectively, which validates that the proposed method has a better all-around performance.
Improving the Performance of CNN Accelerator Architecture under the Impact of Process Variations

Convolutional neural network (CNN) accelerators are popular specialized platforms for efficient CNN processing. As semiconductor manufacturing technology scales down to nano scale, process variation dramatically affects the chip’s quality. Process variation causes delay variation within the chip due to transistor parameter differences. CNN accelerators adopt a large number of processing elements (PEs) for parallel computing, which are highly susceptible to process variation effects. Fast CNN processing desires consistent performance among PEs; otherwise the processing speed is limited by the slowest PE within the chip. In this work, we first quantitatively model and analyze the impact of process variation on CNN accelerators’ operating frequency. We further analyze the utilization of CNN accelerators and the characteristics of CNN models. We then leverage the PE underutilization to propose a sub-matrix reformation mechanism and leverage the pixel similarity of images to propose a weight transfer technique. Both techniques are able to tolerate the low-frequency PEs and achieve performance improvement at chip level. Furthermore, a novel resilience-aware mapping technique that exploits the diversity in the importance of weights is also proposed to improve the performance. Evaluation results show that our techniques are able to achieve significant processing speed improvement with negligible accuracy loss.
Deep Learning Approaches for Classification of Road Types Using Wearable Sensors Embedded in Smart Glasses

The Internet of Vehicles (IoV) is an architecture of the intelligent transportation system that combines automotive, transportation, and information exchange to increase road safety. The categorization of roadways not only improves the passenger's comfort and safety but also provides autonomous cars with safe navigation paths. In this paper, we present an identification method based on movement data from smart glasses (electroocu-lography, acceleration, and angular velocity) to categorize four kinds of roads often experienced: highway, city road, highway, undeveloped region, and housing estate. We developed a deep pyramidal residual network that automatically recovers spatial-temporal data and efficiently identifies road kinds. We performed experiments to evaluate deep learning models utilizing a publicly available benchmark dataset, including sensor data acquired from intelligent eyewear. Experimentally, we discovered that the suggested 1D-PyramidNet model obtained the most incredible interpretation with the most increased accuracy (92.23%) and outperformed all other deep learning models.
Communication System for Sharing and Providing Information about Threats and Incidents on Transport Infrastructure Elements

Transport infrastructure currently faces many anthropogenic threats, resulting in an increasing number of security incidents. These are, for example, attacks with explosives or firearms in the premises of railway stations and connected transport infrastructures, e.g. in the premises of airports or bus terminals. For this reason, it is essential that the owners or operators of these transport infrastructure elements have an effective tool for the timely sharing and provision of information about threats and incidents on transport infrastructure elements. For this purpose, this article presents a list of the affected entities of the transport infrastructure and the content page of the information that should be shared and transmitted between these entities. The supporting part of the article is the design of the sharing process and providing information about threats/incidents and the design of a communication system and suitable technical means for sharing and providing information about threats.
The Borrowing Medical Records System : A Case Study of Medical Information System

This research is an application of information technology to efficiently manage the educational information of the School of Dentistry. Dental students must study medical history in teaching dentistry, although the systematic shelving of documents requires greater flexibility in retrieving and searching for patient cases to be studied. The researchers developed a workflow system to increase the efficiency of document borrowing in the form of a web application. to reduce the process of searching for borrowing documents, reduce the waiting time for documents to be borrowed, and shorten the time to summarize all borrowing information, including adding a user-generated hashtag feature to make it easier to find documents by category and context. By using the system to replace the original process, the total time in every process can be reduced from 180–350 minutes to only 8–40 minutes, equivalent to 241 minutes of reduced wasted time.