Reviewerly/exHarmony huggingface dataset:
Multi-Layer Federated Learning Architecture

Machine Learning is one of the most prominent technologies in the world. It has various use cases in artificial intelligence. Availability of data is the most significant requirement for the development of machine learning algorithms. Generally, all the data is not stored at a single location, it is usually distributed across various endpoints. Using this data for a machine learning algorithm causes privacy issues for the data owner. To tackle this issue, Federated Learning was introduced by Google in 2016.The Federated Learning algorithm ensures the privacy of the data owner by running machine learning algorithms for updating weights on local machines and then it aggregates the local weights for our global model. This provides an approach to training machine learning models in a decentralized manner. This process provides good results, but it faces various issues during this process. If we try to improve the privacy of federated learning algorithms, there is a loss in accuracy and efficiency of it. To tackle these issues we propose multi-layer federated learning. We introduce a novel algorithm by adding extra laver between the client node and server node. Our goal is to improve the efficiency of the federated learning model along with the privacy of data. We use the MNIST dataset to test our model.
Accessible Hands-Free Input Methods for VR Games

New ways to interact with VR environments come with both opportunities and challenges for accessibility. In this work, we summarize different possible hands-free input methods to play a VR game and investigate their applicability. Using a serious game as a Proof of Concept we implemented five focus methods (head-tracking, scanning, head-movement, eye-tracking and voice control) and three activation methods (button, dwelling and blink) exemplary using the Oculus Go standalone VR headset and the Unity game engine. We analyzed the suitability of these methods in a pre-study that excluded head movements for controlling the game based on user feedback. The remaining input methods were evaluated in an explorative user study in terms of operability and ease of use.
Extracting Relations from NER-Tagged Sentences for Ontology Learning

This paper is a continuation of the research on training and using a neural-network language model for ontology learning. The paper deals with the problem of analyzing domain natural language texts and extracting possible relations between concepts from them. A brief overview of existing approaches to solving this problem is presented. A procedure for extracting relations between concepts based on the analysis of the sentence syntax dependency tree is proposed. The results of evaluating the efficiency of extracting relations using this procedure are considered.
Evaluating Intensity Concentrations During the Spatial Normalization of Functional Images for Parkinson’s Disease

The aim of this study is to assess the changes in imaging of dopamine transporters using [123]I-FP-CIT SPECT when applying a deformation of the striatum using a linear and/or a non-linear registration to a reference template. For that, the deformation has been indirectly measured studying the changes in the intensity values in two different scenarios when, during the interpolation stage, the amount or the concentrations of intensity values are preserved. As showed by our results, the degree of deformation is greater in images from patients with Parkinson’s Disease than in healthy control subjects.
ExpressMailer: Fast, Customizable, and Secure Mail Service

Almost everyone uses a mail service at some point in their life. We all are completely dependent on Gmail-like applications. When such services are down, we face a lot of trouble. Also, privacy is an issue of concern nowadays. The idea behind this application is to create a mail service that has highly customizable components and gives an organization complete control over privacy. We have used techniques that require less computational power making it affordable for all. The main highlights of the application are everyday mail, chat, video call, and spam detection using machine learning, email search optimization, and data privacy using cryptography. A product is created where a user can use the electronic mail functionality to send, receive emails, with added features like search mail, star, and mark as important. The spam detection algorithm will automatically send emails to the spam folder based on various parameters defined, using machine learning algorithms. Encryption and decryption of the mail contents are done using asymmetric cryptographic algorithms. Also, for searching mails in application, proper study and indexing for documents have been done to give emphasis on faster retrievals.
A Deep Learning-Based System for Product Recognition in Intelligent Retail Environment

This work proposes a pipeline that aims to recognize the products in a shelf, at the level of the single SKU (Stock Keeping Unit), starting from a photo of that shelf. It is composed of a first neural network that detects the individual products on the shelf and has been trained with the SKU110K dataset and a second network, designed and built within this work that associates to the single image created by the first network, an embedding vector, which describes its distinctive features. By obtaining this vector of the input image, it is possible to measure the similarity, by means of the cosine similarity, between this vector and all the embedding vectors in the comparison dataset. The vector with the highest cosine similarity is associated to an image labeled with the EAN (European Article Number) code and, therefore, this EAN will be that of the input image. Given the particular task, there are not currently any dataset able to meet our requirements as they have not such a granular level of detail (EAN labeled), so a new properly designed dataset is created to solve this task.
Human Action Recognition with Transformers

Having a reliable tool to predict the actions performed in a video can be very useful for intelligent security systems, for many applications related to robotics and for limiting human interactions with the system. In this work we present an architecture trained to predict the action present in digital video sequences. The proposed architecture consists of two main blocks: (i) a 3D backbone that extracts features from each frame of video sequence and (ii) a temporal pooling. In this case, we use Bidirectional Encoder Representations from Transformers (BERT) as technology for temporal pooling instead of a Temporal Global Average Pooling (TGAP). The output of the proposed architecture is the prediction of the action taking place in the video sequence. We use two different backbones, ip-CSN and ir-CSN, in order to evaluate the performance of the entire architecture on two publicly available datasets: HMDB-51, UCF-101. A comparison has been made with the most important architectures that constitute the state of the art for this task. We have obtained results that outperform the state of the art in terms of Top-1 and Top-3 accuracy.
Influence of a Non-Linear Interaction and Kerr Medium on the Emission Spectrum and Entropy Squeezing of Su(1,1) Algebra with Single Qubit

The interaction of a quantum algebraic system su(1,1) interacting with a qubit is studied. A nonlinear function has been added to interaction with the presence of Kerr medium (KM). The general solution is obtained using the constants of motion and Heisenberg equation for the time-evolution operator. The effects of the nonlinear function and KM on the emission spectrum and entropy squeezing are calculated. The results showed that the emission spectrum is affected by the nonlinear function. Symmetric peaks are generated around the vertical axis with increasing influence of the nonlinear function. By activating the role of the KM, the emission spectrum loses symmetry around the vertical axis and the maximum values decrease. Moreover, the KM reduces the squeezing intervals, while the squeezing intervals are improved by increasing the effect of the nonlinear function.
Transformer-Bls: An Efficient Learning Algorithm Based on Multi-Head Attention Mechanism and Incremental Learning Algorithms

Abstract: Using its efficient learning capabilities and unique incremental learning algorithms, broad learning system (BLS) ha s made impressive progress in image analytical tasks such as image classification and object detection. Inspired by this success, w e design a sub-BLS layer for the multi-head attention mechanism and use these layers to construct a transformer-BLS network that achieve a trade-off between model training speed and accuracy. Our transformer-BLS network provides four different incremental learning algorithms that enable the mode to increment of feature nodes, enhancement nodes , input data and sub-BLS layers without updating entire weights. Finally, we validate the performance of our transformer-BLS network and its incremental learning algorithms on a variety of image classification datasets. The results show that the proposed transformer-BLS saves 2/3 training time while improv ing classification performance on the MNIST and Fashion-MNIST dataset . T hese also imply that the proposed method could significantly reduce training time while improving the increment learning performance of the original BLS in the classification task of some datasets .
Intelligent Recognition English Translation System Based on Data Analysis Algorithm

The future development trend of machine translation systems is to develop machine translation systems that combine traditional methods with new methods. The translation quality and efficiency of today’s machine translation systems are far from being able to replace manual translation, but are widely used in many fields. This article aims to study an intelligent English translation recognition system based on data analysis algorithms. It is recommended to name English noun phrases based on short syntactic features as functional noun phrases. That is, on the basis of systemic functional grammar, the boundaries of noun phrases are defined according to the syntactic function of noun phrases in sentences. Automatic recognition of noun phrases in English learning, including noun phrases, research boundary recognition and syntactic function recognition, transforms the task of clarifying the structure of English noun phrases into the task of identifying functional English noun phrases. The S recognition result is the best, and the F value is 98.88%. The results of the analysis can simplify the structure of sentences, reduce the difficulty and complexity of analysis, provide a basis for further analysis, and enhance the ability to deal with structural ambiguities in machine translation.
Adaptive Generalized Multi-View Canonical Correlation Analysis for Incrementally Update Multiblock Data

One of the major problems in real-life multiblock dynamic data analysis is that all the available modalities may not be relevant. Some of them may provide noisy or even inconsistent information with respect to other modalities. So, it is necessary to evaluate the quality of a new modality before considering it for feature extraction. In this regard, the paper introduces a new multiset canonical correlation analysis (MCCA), termed as incremental MCCA (IMCCA). When a new modality is available for the analysis, the IMCCA generates the new canonical variables from that of the earlier modalities, without repeating the same procedure with the original data augmented by the new modality. The proposed IMCCA deals with the &#x201C;curse of dimensionality&#x201D; problem associated with multidimensional data sets, by using the ridge regression optimization technique. Using the proposed IMCCA model, a new feature extraction algorithm is introduced, which considers a new modality for the analysis if it has relevant and significant information with respect to existing modalities. The proposed algorithm starts with the two most relevant modalities, and the remaining modalities are added sequentially according to their relevance. The optimum regularization parameters for the proposed algorithm are estimated based on the supervised information of sample categories. The effectiveness of the proposed algorithm, along with a comparison with state-of-the-art multimodal data integration methods, is established on several real-life multiblock data sets.
Increasing Motivation of Walking Exercise Using 3D Personalized Avatar in Augmented Reality

Walking is an easily accessible and effective exercise, hence it can be easily participated as a part of a person’s everyday. However, due to changes in our social environment such as the increase in single-person households, hectic lifestyles and an unprecedented pandemic, the number of people who walk alone is increasing. We found that people lack motivation when they walk alone; to address this, we designed an interactive full-body 3D personalized avatar in augmented reality (AR) as a virtual walking partner. Our research goal is to increase the motivation of walking exercise using an AR 3D avatar. This approach focuses on the social aspects of physical exercise, that is, cooperation and competition with a partner. The proposed system has two types of use cases: (1) walking with an avatar, and (2) walking with a remote user using an avatar. We investigated the effect of designed interactions with a virtual walking partner for both cases. In addition, we designed a method of movement synchronization between a user and an avatar using only a head-mounted display (HMD) without separate sensors. The preliminary evaluation of the system indicated positive response from participants. We believe that our findings support the idea that designed interactions with a virtual walking partner can increase a person’s motivation of walking exercise.
Speaking About Wine: Another Case Study in Bridging the Gap Between Formal and Informal Knowledge

This paper presents WineSpeak, a system that uses controlled natural language for translation and information retrieval about the topic of wine. WineSpeakcombines some recent work on natural language interfaces to databases with other recent work on information extraction from heterogeneous sources. It supports database queries about wine in natural language, as well as the translation of questions, comments, and short articles about wine in human-to-human communication. WineSpeakwill be available in English, German, French, Italian, and Spanish, but also readily portable to other languages via the use of Grammatical Framework and its Resource Grammar Library (The system will be released in connection to the publication of this volume at https://www.grammaticalframework.org/gf-winespeak/ . The final name of the system can be different, to avoid confusion with other similar names).
ANNPRO: A Desktop Module for Automatic Segmentation and Transcription

This paper describes an automatic segmentation and transcription module for Polish and its integration with the Annotation Pro software tool. The module is an extended desktop version of the CLARIN-PL online tool and has been named ANNPRO. Thanks to developing the module, it becomes possible to combine the functionality of Annotation Pro desktop program and the web-based automatic aligner. The results can be immediately used as the input for further acoustic-phonetic analyses with Annotation Pro native functions or annotation mining plugins. Annotation Pro enables using any number of external alignment modules, provided that certain basic format requirements are kept. We discuss these requirements and exemplify them with the ANNPRO module functionality and the integration steps. As an illustration, we present a brief report on experiences gained in the process of annotation of a multimodal corpus with the use of ANNPRO. Both Annotation Pro and the ANNPRO module are publicly available for download and can be freely used for research.
2D/3D Quasi-Intramodal Registration of Quantitative Magnetic Resonance Images

Quantitative Magnetic Resonance Imaging (qMRI) is backed by extensive validation in research literature but has seen limited use in clinical practice because of long acquisition times, lack of standardization and no statistical models for analysis. Our research focuses on developing a novel quasi-intermodal 2D slice to 3D volumetric pipeline for an emerging qMR technology that aims to bridge the gap between research and practice. The two-part method first initializes the registration using a 3D reconstruction technique then refines it using a 3D to 2D projection technique. Intermediate results promise feasibility and efficacy of our proposed method.
An Extendable Sentiment Monitoring Model for SNS Considering Environmental Factors

Rapid growth of social network service (SNS) has drawn significant attention from the publics. Existing research indicated that emotional state and behavioral tendency of SNS users can be identified and predicted through sentiment analysis. However, it is found that not only the posts can express user's emotions but the overall environmental conditions faced by the user may lead to the generation of different emotions based on the cognitive theory of emotion and observation. Therefore, it may lead to bias between the sentiment analysis results and the actual situation if only analyzing the post. This study targets to propose an extendable sentiment monitoring model which considers the actual environment of users in SNS. Through this model, the result of sentiment analysis is closer to reality. By analyzing the content of users' continuous posts, the sentiment analysis can take into account the pre- and post-textual relationships. The classification result of external affecting sentiment factors by K-means is used as criteria for weighting method to adjust the results of sentiment analysis based on BERT. Finally, the time series analysis is used to predict sentiment tendency monitor sentiment changes. The experiment results show that the training and validation accuracy are 89.24% and 84.00%, respectively. By our weighting method to revise the BERT results, the F1 score is improved from 0.839 to 0.850.
How Touch Works in Everyday Networked Social Relationships

Abstract This chapter presents a way to begin mapping Touch in everyday networked practices by examining how Touch is culturally significant. Extending from the previous chapter, networked Touch is first identified as embodied and multimodal. The five fundamentals of the cultural mode of Touch—identified as connection, engagement, contiguity, differentiation and positioning—are then located for how they are culturally significant in everyday relationships. Finally, acknowledging networked social relationships as embodied and multimodal, I argue that visual social relationships can be understood as practices of Touch and mapped through close examination of the fundamentals of Touch. Furthermore, the fundamentals (as a map) could potentially be used to investigate the technological self beyond visual social relationships.
Knowledge Transferred Fine-Tuning: Convolutional Neural Network Is Born Again With Anti-Aliasing Even in Data-Limited Situations

Anti-aliased convolutional neural networks (CNNs) are models that introduce blur filters to intermediate representations of CNNs to achieve high accuracy in image recognition tasks. A promising way to prepare a new anti-aliased CNN is to introduce blur filters to the intermediate representations of pre-trained (non anti-aliased) CNNs, since many researchers have released them online. Although this scheme can build the new anti-aliased CNN easily, the blur filters drastically degrade the pre-trained representations. Therefore, to take full advantage of the benefits of introducing blur filters, fine-tuning using massive amounts of training data is often required. This can be problematic because the training data is often limited. In such a &#x201C;data-limited&#x201D; situation, the fine-tuning does not bring about a high performance because it induces overfitting to the limited training data. To tackle this problem, we propose &#x201C;knowledge transferred fine-tuning.&#x201D; Knowledge transfer is a technique that utilizes the representations of a pre-trained model to help ensure generalization in data-limited situations. Inspired by this concept, we transfer knowledge from intermediate representations in a pre-trained CNN to an anti-aliased CNN while fine-tuning. The key idea of our method is to transfer only the essential knowledge for image recognition in the pre-trained CNN using two types of loss: pixel-level loss and global-level loss. The former loss transfers the detailed knowledge from the pre-trained CNN, but this knowledge may contain &#x201C;aliased&#x201D; non-essential knowledge. The latter loss, on the other hand, is designed to increase when the pixel-level loss transfers non-essential knowledge while ignoring the essential knowledge, i.e., it penalizes the pixel-level loss. Experimental results demonstrate that the proposed method using just 25 training images per class on ImageNet 2012 can achieve higher accuracy than a conventional pre-trained CNN.
Comparison of Real-Time Face Detection and Recognition Algorithms

With the phenomenal growth of video and image databases, there is a tremendous need for intelligent systems to automatically understand and examine information, as doing so manually is becoming increasingly difficult. The face is important in social interactions because it conveys information. Detecting a person's identity and feelings Humans do not have a great deal of ability to identify. Machines have different faces. As a result, an automatic face detection system is essential.in face recognition, facial expression recognition, head-pose estimation, and human–computer interaction, and so on Face detection is a computer technology that determines the location and size of a person's face. It also creates a digital image of a human face. Face detection has been a standout topic in the science field This paper provides an in-depth examination of the various techniques investigated for face detection in digital images. Various face challenges and applications. This paper also discusses detection. Detection features are also provided. In addition, we hold special discussions on the practical aspects of developing a robust face detection system, and finally. This paper concludes with several promising research directions for the future.
Handshape Recognition in an Educational Game for Finger Alphabet Practicing

A vision-based method of handshape recognition was developed and used in a simple educational game designed to practice the finger alphabet. It uses a deep neural network to determine the two-dimensional skeleton of the hand and the genetic algorithm to recognize its shape. The classification was carried out by defining and solving an optimization problem, in which the skeleton corresponding to the unknown shape is subjected to an affine transformation and then adjusted to the previously prepared set of templates. The method was tested using the leave-one-subject-out validation protocol on the author’s dataset of Polish Finger Alphabet letters and the publicly available Microsoft Kinect and Leap Motion Dataset. Based on the developed algorithm, a simple educational game was prepared, the purpose of which is to practice hand dexterity in showing complex shapes appearing in the finger alphabet.