Reviewerly/exHarmony huggingface dataset:
Insertables: Beyond Cyborgs and Augmentation to Convenience and Amenity

Individuals are voluntarily inserting devices inside their bodies for non-medical purposes. As research into these insertable devices continues, and the practice gains traction, it is clear that earlier accounts of hobbyist makers and tinkerers reveal edge cases of use. While transhumanists still aim to augment their bodies past “human norms”, they are no longer the only group interested in insertables. Beyond these innovators, early adopters of these devices are simply concerned with practical and convenience-based uses. The motivation of this chapter is threefold. The first is to present a background of insertable devices individuals are using for augmentation. The second is a more in-depth account of insertables use than previous research ( $$n=115$$ ). This allows us to answer, more accurately: 1. What devices are individuals putting in their bodies; 2. What do they use these devices for; 3. What are the motivations for modifying their bodies in this way; and 4. How do users of these devices identify themselves? The third, and final, contribution is a discussion of the motivations and self-identifications of individuals with these devices, and the implications of these. We show the progression from individuals using insertable devices to augment themselves to become “cyborgs”. Now, “everyday” people are choosing to use insertables for the convenience and amenity-based purposes afforded by these devices. We contribute to the literature knowledge of these more commonplace uses, beyond augmentation. These are for access and authentication, and for storing and sharing information. Only a minority use them for augmentation purposes, which are now known to be edge cases. These are: extending senses; supporting human connections; acting as an alternative digital interface; and capturing biometric data. Motivations are evolving as device usage is moving from hobbyists and innovators to early adopters. Given they are less concerned with augmenting themselves, it logically follows that the majority also do not, or do not strongly, identify with any particular social movement. Users are no longer confined to the ‘fringes’. Understanding this not only contributes updated findings, but also helps normalize the use of such devices.
Search for the real McCoy: Authorship Attribution

A person's writing style is an example of a behavioral biometric identity. The words used by certain individuals, distinctive structuring of the sentences, personification of vocabulary, writing discipline and articulation of theory can often be used to link a piece of written work. Authorship attribution is one of the oldest problems in linguistics which also became one of the most important ones to be solved with the rise of modern statistics, machine learning and natural language processing. Authorship attribution is defined as an attempt to identify if the testing corpus has been written by the aforementioned author or not using their stylometric fingerprint. Here the proposed research discusses in detail about solving this challenging problem of identifying patterns in text using multiclass classification techniques coupled with Radial Basis Function. The proposed model yields state-of-the-art performance on several data sets, containing either formal texts written by a closed set of authors or informal texts generated by thousands of online users. Further discuss on the applicability of such novel algorithm in cases like email authorship verification and others are also carried out. Finally the findings have been formulated as a set of recommendation for best practices.
Using Deep Learning for Fusion of Eye and Mouse Movement based User Authentication

This paper presents a deep learning based user authentication system which aims to identify an individual using data gathered from a mouse and the user's eyes during computer use in a controlled environment. A stacked bidirectional and unidirectional Long Short-Term Memory Recurrent Neural Network (SBV-LSTM-RNN) is introduced to distinguish a legitimate user from impostors. As one of the few attempts of using fusion of mouse and eye movement for user authentication, the proposed system, when adopted on a small dataset, has shown promising improvement compared to a similar system where fusion of eye and mouse modalities and a traditional machine learning method are used.
Solution of a Solid Traveling Purchaser Problem using Modified Genetic Algorithm and Comparison Crossover Technique

In this paper we develops a comparison crossover for Modified genetic algorithm (MGA) to solve a NP hard optimization problem. Here we consider a set of markets, a depot and some products for each of which a positive demand is specified. Each product is made available in a subset of markets in each of which only a given quantity, less than or equal to the required one, can be purchased at a given unit price. Here, this Traveling purchaser problem (TPP) searches for a cycle starting at and ending to the depot and visiting a subset of markets at a minimum traveling cost, also we introduce different vehicle to visit different markets say solid TPP (STPP). The effectiveness of our model are illustrated by numerical examples.
OPTILYZER: Hawk-Eye Technology for Data Integration &amp; Performance Optimization

Data Comparison and Performance Optimization are key challenges we face when we talk about humongous volume of data. OPTILYZER, a user-friendly interface is proposed to overcome these challenges. The 4V's which are ruling in market due to which Big Data has come into existence that are Volume, Velocity, Variety and Veracity. Data Analysts use data for activities like forecasting or deep learning and in order to process these data various tools are available which helps to achieve this task with minimum efforts, but problem arises with legacy data. Legacy data piles up the space and hinders the performance of the current process. This system provides an efficient way to archive these data so as to reduce the space and time complexity of the current process also helps to resolve the challenges faced in data comparison between various heterogeneous platforms like Hive, MySQL and Redshift which have different structure and syntax for processing the data.
DNN-Based Mutual Satisfaction Prediction Model for Matching Between Users

Recently, there have been many attempts to provide customized services using big data. In this paper, we try to predict the degree of mutual satisfaction for matching between users. First, the proposed system calculates the supplier’s expected satisfaction and then calculates the higher-result supplier’s expected satisfaction. The participants’ personal information, preference information, and mutual evaluation are utilized as input values for learning. By using mutual evaluation for learning, recommendations can be made that consider both directions rather than unilateral recommendations. The mutual satisfaction level is obtained by suggesting both the consumer’s and supplier’s expected satisfaction. This results in better satisfaction scores for users and their matching systems.
Automatic video surveillance system: recognition of critical behaviors in the process of machine learning

This article describes means for interpreting and parametrization of critical behaviours. This is the basis for developing the database necessary for the implementation of an automated video surveillance system. The database will be used in the machine learning process as a decision block for the entire system. To this end, it is necessary to conduct an in-depth analysis of events that indicate a potential threat to human health or life. The project requires defining a hardware and software platform and how to integrate them into an optimal system. For the detection and tracking of human skeletons, it was decided to use the popular Microsoft Kinect 2.0 depth sensor, used in a wide spectrum of recreational games and applications. Integration with the platform, based on an artificial neural network, was decided to be developed in the well-known MATLAB programming environment. The author reveals details on the process of analysing potential emergency situations, parameterization, performing empirical measurements and developing a database.
RETRACTED: Hospital embedded system and acupuncture treatment of vascular cognitive impairment after stroke

Customary electronic needle therapy, the main pot can animate, and while deciding the patient, have their solidarity. Brilliant electronic needle therapy utilizes the biosensor and fluffy procedures to tackle these issues because of a remote electronic needle therapy framework utilizing the sensor module. Signal for treatment will be explored to the rules for the electronic needle treatment identifying pad plan. To change the needle therapy and power and time of a few patients' focus, build up a versatile remote needle therapy framework utilizing fluffy innovation. A productive remote electronic needle therapy treatment framework to acquire needle therapy rapidly utilizes the canny analytic framework. Stroke patients with intellectual debilitation after a stroke happens as often as possible. The danger of intellectual hindrance after stroke, age, and instructive foundation are identified with these two segment factors as occupation and veins. The component's fundamental psychological impedance after stroke isn't known in detail. A combination of white issue sores and miniature cerebral discharge, cerebellar vascular sickness or stroke of, alone or in the blend, add to the pathogenesis of psychological disability after stroke. After a stroke and benefit from the anti-dementia drug, cognitive impairment treatment can only not take measures to manage the cerebrovascular disease. Mechanism of the characteristics and cognitive impairment after a stroke and discussions promise these patients' management strategies. Internet of things, in the field of medical care. The continuous increase of IoT solutions for health care cannot be out of this model. Contribution introduces a direction for realizing the Internet of Things (IOT) and global connectivity between healthcare environments. All that is required to integrate into the worldwide environment are all significant challenges. In this revolution, health care collected from a minimum of sensors has been re-designed to see the big data.
More families press for in-person learning at schools in SE WI

Tired of distance learning. From demonstrations to parent surveys, families are voicing their desires for kids to get back into the classroom.
Development of a Motion Improvement Support System for Baton Twirling Players

The purpose of this study is to develop a system for baton twirling players to check and understand changes in their movements for slump breakout. In the previous study, according to the results of interviews for players, we developed a prototype using overlay video and accelerated time series graphs, aiming for a system that players could use on their own every day. Two national-level players tried the prototype and welcomed its features. Based on their comments, we improved the system that uses a pose estimation system called “OpenPose” to reveal the motion differences only from videos. By using this system, players can understand the difference in movement without burden compared to when using sensors. One national-level player used the system for about a week and was asked to evaluate the usefulness of the system. As a result, we obtained a good evaluation by trial; using this system, the player could easily understand the differences in motions between success and failure and he could increase the success probability by reducing the difference in the motion. This time we used a high-spec computer, but the system should be realized with small devices (such as smartphones), so that anyone can use easily in anytime and anywhere.
Loss Constrains Added Squeeze and Excitation Blocks for Pruning Deep Neural Networks

Deep neural networks are proved to be very effective to solve problems on image classification, object detection and segmentation. However, in cases where only limited hardware is acquired, it may be a problem to deploy big models with excellent performance as they are sometimes calculation consuming. To overcome the limits on power, memory and calculation, channel pruning is proposed to compress the model in channel wise and soon become a common approach to have big models compressed. Generally, pruning is a three-stage pipeline containing training, pruning and finetuning. In this work, we come up with a new pruning approach that needs no finetuning. The major idea is extracting channel saliences by squeeze and excitation block and pushing the salience to either 0 or 1 by a sin-based function. Then take the salience as criteria for pruning. As the criteria of our approach is activation rather than trainable parameter, finetuning is not necessary in our pruning strategy which make the pruning process more stable and time saving. Experiment on flowers demonstrates our new designed pruning method is effective on reducing the model scale while maintaining the overall accuracy.
Competencies for Effective Distance Learning: Design and Development

Digitalization processes, as well as the conditions of the pandemic, have led to the active development of distance learning. This is dictated by the need to develop new qualities in students, allowing them to effectively engage in learning in new conditions. The studies, described in this article, allowed not only to develop a competence model for a bachelor's degree for work in distance learning, but also to provide recommendations, as well as a plan for the step-by-step development of competencies necessary for effective work with the use of distance technologies.
Presence experienced in smartphone-based exposure: First and third person perspectives

Various formats to deliver exposure stimuli have been developed, including video-based stimuli. Virtual reality-based exposure often utilizes a first-person perspective, which is associated with greater presence (e.g., feeling integrated in a virtual world) than third-person perspective. Yet, few have compared exposure stimuli presented in first-person versus third-person perspective. Thus, this study examined presence and anxiety levels associated with exposure video perspective. Participants (N = 18) completed a two-week video-based exposure treatment (i.e., watched one week of first-person perspective film and one week of third-person perspective film, counterbalanced). Participants reported anxiety and presence felt during the exposures. Results indicated greater presence was experienced during first-person videos than third-person ones. Anxiety levels did not differ between video perspective. An interaction between perspective and video order was found; those who watched third-person videos during week one reported more anxiety when presented first-person videos than those who watched the reverse order. Results support previous findings that more presence is reported with first-person than third-person perspective. More importantly, this study suggests an innovative way to sequence exposure stimuli so as to maximally sustain therapeutic levels of arousal over the course of treatment.
Crowd Analysis in Public Transport Based on Face Detection

In a developing country like India with a population over one thousand million, it is a chaotic effort to commute using public transport. Buses are most widely used in India as means of transport. Crowd is an exceptional gathering of individual or something includes network or society. These days, it turns into the most dynamic situated research and in vogue point in computer vision. With the trending technology of face detection and image processing, this paper discusses a proposed system to analyze the congestion in the buses and informing the passenger about the congestion in the upcoming bus. To capture live images of the bus, cameras that are present in the buses are used. These images are further analyzed using image processing which would further determine the status of the congestion in the bus in three modes, i.e., first one is “Full” to check whether all the seats are occupied, secondly “Standing space available,” and last one is to check “Empty” which tells about number of non-occupied seats.
Saliency Prediction based on Object Recognition and Gaze Analysis

Predicting the human visual attention in an image is called saliency prediction, and is an active research area in the field of neuroscience and computer vision. Early works on saliency prediction was performed by using low-level features. In recent years, convolutional neural networks (CNN) have been adapted for saliency prediction and achieved the state-of-the-art performance. However, the eye-gaze depends on the personality of each viewer(1) and conventional methods did not take into account such individual properties of the viewer. Therefore, this paper proposes a novel saliency prediction method considering the influence of eye-gaze. Assuming that personality can be expressed as the degree of attention to an object, our proposed method considers the personality by learning which objects are likely to be perceived by each viewer, and weighting the universal saliency map with the generated mask based on the object detection results. The experimental results show that the proposed universal saliency map achieves higher accuracy than conventional methods on the public dataset, and the proposed weighted saliency map can reflect the variation of the eye-gaze influences among viewers.
A Hybrid Sliding Window Based Method for Stream Classification

The resources of time and memory space are limited in data stream classification process. Hence, one should read the data only once and it is not possible to store the history as a whole. Therefore, when dealing with data streams, classification approaches in traditional data mining fall short and several enhancements are needed. In the literature, there are stream classifications methods such as stream based versions of nearest neighbor, decision tree based or neural network based methods. In our previous work, we proposed m-kNN (Mean Extended k-Nearest Neighbors) and CSWB (Combined Sliding Window Based) classifiers and presented the accuracy performances in comparison to other data stream classification methods from the literature. In this work, we present two new versions of CSWB, CSWB-e and CSWB-e2, such that our m-kNN classifier is combined with K* (K-Star) and C4.5, and with K* (K-Star) and Naive Bayes, respectively. In the experiments, accuracy of m-kNN, CSWB-e and CSWB-e2 are analyzed with new data sets in order to observe the relationship between window size and the accuracy. Additionally, the classification performance results for m-kNN are further analyzed and reported in precision, recall and f-score metrics in addition to accuracy.
Educación en tiempos de pandemia

“What are the costs of keeping schools closed until 2021?” is the main question that drives this research.  Several public authorities have called to delay the return to in-person classroom until next year. However, this call seems to ignore the adverse effects in the young population, which comprises more than 3.5 million children and youth. This paper seeks to make visible the potential negative effects of extended school closure on students' learning and development opportunities, and the risks derived from these: wider education gap, and greater school dropout rates. To this end, we analyze three key factors for distance learning according to the literature: critical skills for distance learning, housing conditions, and parental support. Using the available data (CASEN 2017, SIMCE 2017 y 2018 and others), we find that one in five schoolchildren has significant deficiencies in the analyzed dimensions. Whereas nearly a third of the schoolchildren in the lowest income quintile are in the highest risk group, only six percent of schoolchildren in the highest income quintile are in this group. Extended school closure carries high costs for children and youth, particularly for the most vulnerable. These costs may be long-lasting, if drop-out rates and learning gaps increase.
Face Identification Using LBP-Based Improved Directional Wavelet Transform

Face identification is the most active area of research in computer vision and biometric authentication. Various face identification methods are developed over the time, still, numerous facial appearances are needed to cope with such as facial expression, pose, and illumination variation. Moreover, faces captured in unrestrained situations also impose immense concern in designing effective face identification methods. It is desirable to extract robust local descriptive features to effectively characterize such facial variations both in unrestrained and restrained situations. This chapter discusses such a face identification method that incorporate a popular local descriptor such as local binary patterns (LBP) based on the improved directional wavelet transform (IDW) method to extract facial features. This designed method is applied to complex face databases such as CASIA-WebFace and LFW which consists of a large number of face images collected under an unrestrained environment with extreme facial variations in expression, pose, and illumination. Experiments and comparison with various methods which include not only the local descriptive methods but also local descriptive-based multiresolution analysis (MRA) based methods demonstrate the efficacy of the LBP-based IDW method.
Differentiated Approach to Teaching and Its Implementation in Russian Language Lessons in Primary School

The article discusses the possibilities of using a differentiated approach to teaching the Russian language, which takes into account both the stage of mastering the subject content and the degree of individual preparation of each student, his abilities, interests, needs, motivation. The authors determined the specifics of modeling tasks of the basic and increased levels of complexity, which allow, based on the knowledge gained and the speech experience of junior schoolchildren as native speakers, to successfully form their universal educational actions (cognitive, communicative, regulatory), which meets the requirements of the Federal State Educational Standard of the Primary General Education to the planned subject and metasubject learning outcomes. The examples of differentiated tasks aimed at the development of speech-thinking abilities, logical thinking, cognitive skills, and skills of independent educational activity are presented.