Reviewerly/exHarmony huggingface dataset:
Open Medical Gesture: An Open-Source Experiment in Naturalistic Physical Interactions for Mixed and Virtual Reality Simulations

Mixed Reality (MR) and Virtual Reality (VR) simulations are hampered by requirements for hand controllers or attempts to perseverate in use of two-dimensional computer interface paradigms from the 1980s. From our efforts to produce more naturalistic interactions for combat medic training for the military, USC has developed an open-source toolkit that enables direct hand controlled responsive interactions that is sensor independent and can function with depth sensing cameras, webcams or sensory gloves. Natural approaches we have examined include the ability to manipulate virtual smart objects in a similar manner to how they are used in the real world. From this research and review of current literature, we have discerned several best approaches for hand-based human computer interactions which provide intuitive, responsive, useful, and low frustration experiences for VR users.
Sarcasm Detection in Hindi-English Code-Mixed Tweets Using Machine Learning Algorithms

Many micro-blogging social media platforms such as Twitter, Facebook, and Instagram. Users utilize such social media platforms to convey their opinions concerning different objects. Such significant interactions make Natural Language Processing tasks like opinion mining and sentiment analysis much more critical. Lots of textual content on social media also include Sarcasm. Often, if Sarcasm is involved, it reverses the polarity and meaning of the text. Detecting Sarcasm is a challenge for such Natural Language Processing tasks. The task of sarcasm detection has more and more significance for social media monitoring, market research, customer service, and brand monitoring. It is also essential for security and commercial services. First, we share the summary of methodologies proposed by a few researchers in this field for identifying Sarcasm. By analyzing and studying the previous work, we have proposed a system that will improve the results of sarcasm detection for Hindi-English code-mixed tweets. In this paper, we compare the performance of the machine learning and deep learning models and observe that Random Forest and Logistic Regression classifiers give a maximum F-score of 96%.
Best UI Experience: Material Design in Action

For a long time, Android UI was widely seen as not beautiful or at least not as good as iOS UI. This makes some companies enforce unified UI for Android and iOS which does not make sense to me. Because for the same user, it is unlikely that they will use the same app on different OS but surely that they use different apps in the same OS. Thus having same style across different apps in the same OS is more important than unifying the style of the same app in different OS.
SportsStore: Orders and Checkout

In this chapter, I continue adding features to the SportsStore application that I created in Chapter 5 . I add support for a shopping cart and a checkout process and replace the dummy data with the data from the RESTful web service.
Feature Transformations for Outlier Detection in Classification of Text Documents

In this paper, we investigate the influence of feature transformation on the results of outlier detection of text documents. We tested four outlier detection methods: Local Outlier Factor, Extreme Value Machine, Weibull-calibrated SVM, and the Mahalanobis distance. The analyzed text documents are represented by different feature vectors ranging from TF-IDF, through averaged word embedding (two types), to document embedding generated by the BERT network. Experimenting on two different text corpora, we show how a transformation of the feature space (vector representation of documents) influences the outlier detection results.
Effectiveness of Automatic Retargeting in Programmatic Advertising

Advertising platforms such as Facebook and Google provide the advertisers with tools allowing them to benefit from advanced targeting, automated ad delivery mechanisms, and behavioral algorithms. These algorithms are based on machine learning and can predict whether a user will click on a particular ad, allowing for optimization of their placement. Online marketing is transitioning to automatized lead nurturing strategies that follow the customer along his journey with a series of personalized and automated messages. A significant part of this process is represented by dynamic retargeting. The method of dynamic retargeting is used in advertising to address users that already interacted with the brand online and offer them personalized content to motivate them to proceed in the next stage of the customer purchase journey. While automation is generally associated with the increased effectiveness of the processes it is applied to, the human aspect of marketing creates a different context with unexpected responses. A growing corpus of studies questions the effectiveness of targeted online advertisements in relation to the increasing number of adblocker users and decreasing click-through rates. However, these studies usually do not distinguish the type of advertisements or the purchase stage. Dynamic retargeting represents an advanced targeting strategy that uses a combination of cookie- and pixel-based user tracking and personalized offers using e-shop generated product feeds. This article tests the effectiveness of 216 sets of dynamic retargeting ads by comparing them with 216 non-dynamic, manually created product ads both in Facebook Ads Manager and in Google Ads.
Intelligent Recognition Model for Machine Translation Based on Machine Learning Algorithm

In recent years, as the political, economic, and cultural exchanges between countries have become more frequent and close, the demand for automatic translation between languages around the world has become increasingly urgent, and the research on machine translation has also entered an unprecedented period of high. The purpose of this article is to study the design of machine translation intelligent recognition model based on machine learning algorithms. The key technologies in machine translation recognition based on machine learning algorithms have been studied, and they have been improved and implemented. First, it summarizes the core theories and methods of machine translation, summarizes the methods of machine translation into four methods, builds a translation intelligent recognition model, and then proposes translation recognition technology and decoding strategies, which run on PDA to improve the translation effect; then analyze The results of intelligent recognition and the comparison of machine translation results of different models are compared. The experimental results show that the PER value of the intelligent recognition model based on the combination of rules and parts of speech is reduced by 3.11%.
Node Information Awareness Pooling for Graph Representation Learning

Graph neural networks (GNNs), which generalize deep neural network models to graph structure data, have attracted increasing attention and achieved state-of-the-art performance in graph-related tasks such as graph classification, link prediction, and node classification. To adapt GNNs to graph classification, existing works aim to define the graph pooling method to learn graph-level representation by downsampling and summarizing the information present in the nodes. However, most existing pooling methods lack a way of obtaining information about the entire graph from both the local and global aspects of the graph. Moreover, in these pooling methods, the difference features between nodes and their neighbors are usually ignored, which is crucial in obtaining graph information in our opinions. In this paper, we propose a novel graph pooling method called Node Information Awareness Pooling (NIAPool), which addresses the limitations of previous graph pooling methods. NIAPool utilizes a novel self-attention framework and a new convolution operation that can better capture the difference features between nodes to obtain node information in the graph from both local and global aspects. Experiments on five public benchmark datasets demonstrate the superior performance of NIAPool for graph classification compared to the state-of-the-art baseline methods.
Approaches to Conceptualizing the Cost of Academic Library Cataloging: Discourses on Metadata Creation Cost, Value, and Worth

Cost analyses are ways in which individual libraries can generate information about how their budgets are used, and can possibly help demonstrate the worth and value of work in catalog and metadata creation in academic libraries. However, despite their acceptance in the professional literature, cost analyses may be deeply flawed. This article begins with a broad survey of the professional and scholarly literature focusing on cost analysis and the elements composing it. The nature of cost analysis and the goals of costing practices, including the potential to calculate a return on investment (ROI), are presented. Next, through an analysis of the recent (2000–2019) scholarly literature on costing cataloging and metadata creation in academic libraries, we present peer-reviewed literature relating to costs; we demonstrate that the scholarly literature diverges from a focus on cost analysis to focus instead on improvements to individual aspects of cataloging and metadata creation work. In the final section, we propose approaching the catalog as a service that can be assessed in terms of opportunity costs for failed interactions. Overall findings suggest that focusing on user needs can be a productive alternative approach to considering the value and worth of academic library catalog and metadata creation.
Innovative Approach for Tertiary Education System: The Crowdsourcing Model

The paper deals with innovative approaches for tertiary education system based on simulation of crowdsourcing model in integration with such innovative learning strategies as 1) adaptive learning; 2) learning by doing; 3) project-based learning; 4) contextual learning. The publication reveals the crowdsourcing model for effective performance in universities of the Russian Federation and Kazakhstan. The results of research, analysis and approbation of implemented components of smart pedagogy reflect different levels for the achieved “smartness” for tertiary education systems. The key indicators for smart classes including (1) adaptability, (2) self-learning and (3) self-organization have been considered. The conduced review, experiment and the feedback represent the crowdsourcing model based on Ed Tech as an effective way to transfer knowledge from the older generation to the younger one.
Remote Programmability Model for SmartNICs in HPC Workloads

HPC workloads experience significant overhead due to handling network-related tasks on the CPU. Some tasks could be offloaded to a SmartNIC, thus reducing the run-time of the workload, but this typically requires explicit support in the application. Moreover, several SmartNIC models available today expose different APIs for offloading tasks. In this paper we present a model for SmartNIC programmability, including a proposed system design for allocating and utilizing network-wide SmartNIC resources. Unlike existing APIs for programming accelerators, our proposal focuses on access to resources over the network: large-scale deployments imply a network-based mechanism for allocating, and offloading to, SmartNIC devices. While a SmartNIC is often characterized by its embedded processor, the proposed system is also applicable to NICs based on ASIC or FPGA, aided by the host CPU.
Mapping Digital Competency Studies in Preservice Teaching Programs in Uruguay

This article shares the results of a systematic mapping of digital competency (DC) studies among preservice teachers in Uruguay from 2000 to mid-2021. The aim of the study has been to have a general view of preservice teachers’ DC building as far as it is approached and studied by different national and international academics and researchers. The following specific objectives were established: (1) identifying research teams dealing with DCs in preservice teacher education in Uruguay and their institutional affiliation, (2) identifying methodological approaches, and (3) mapping pre-service teachers’ DC-related sub-topics, addressed by researchers. Among other findings, 38% used mixed, qualitative, and quantitative methods, 32% applied qualitative techniques, and 20% reported experiences, either related to classroom practices or to the organization and management of educational institutions. 7% were the result of quantitative studies and 3% were action research projects. Topics addressed are DC and the place it should have in the curriculum, teaching practices to train preservice teachers, preservice teachers’ perceptions about their own digital competencies, the level of preservice teachers’ digital competencies and public policies regarding DC building. Research results reveal that preservice teachers are currently experiencing an ongoing renewal process, which still shows fragmentation and a variety of approaches to DC training, although some important achievements are also evident. Mostly, these achievements are in reference to the presence of an initial group of academics who have decided to focus on the topic from the point of view of a situated practice.
Avionic Software and Agile Development: A Systematic Mapping Study

Background: The rapid increase in the use of agile development has raised the question whether of the aerospace industry can benefit from the application of agile approaches in the production of software for airborne systems and equipment. These systems are regulated by the standard DO-178 since there are hard constraints imposed on them. Additionally, the evolution of the avionics software increases the complexity of the software. Therefore, the approach to the feasibility of agile development for avionics software needs to be further understood. Objective: This study aims to examine the research publications on the agile development implemented in the lifecycle of the avionic software in order to found the trend of this mode of development. Method: We conducted systematic mapping study adopting the guideline offered by [1, 2] and classified the contributions research between 2001 and 2021 in the map. Conclusion: We selected 46 articles from 956 found in four electronic databases and based on the results, we conclude that over the past decade there has been a general increase in the number of studies concerning the agile development in different phases of the avionics software lifecycle.
Optimization of Re-ranking Based on k-Reciprocal for Vehicle Re-identification

This paper proposes a re-ranking method for vehicle re-identification based on k-reciprocal Encoding. In recent years, with the development and popularization of deep learning, vehicle re-identification has made great progress and excellent performance. The re-ranking me-thod applied to re-identification has been widely adopted and recognized. However, the existing re-ranking methods still have room to be optimized. The existing k-reciprocal encoding based re-ranking method considers that if the k-nearest neighbor (k-nn) of a gallery image includes the probe image, it is more likely to be a positive matching result, and we called these gallery images are the k-reciprocal nearest neighbors (k-rnn) of the probe image. By encoding its k-rnn into a single vector to calculate the k-reciprocal features, and use this vector to re-ranking by the Jaccard distance. The final distance is calculated as a combination of the original distance and the Jaccard distance. Our idea is that when k-rnn are encoded as a single vector, the k-rnn with a higher-ranking should be given a higher weight, and the k-rnn with a lower-ranking should be given a lower weight. This method will make matching results that are more likely to be a positive result get a higher ranking. In this paper, we use the above idea to assign weights to k-rnn to re-ranking the results of re-ID.
Design and Test of a Controller for the Pluto 1.2 Quadcopter

Quadcopters are being utilized in varying applications like product delivery and surveillance. The controllers designed must be tested extensively in both simulations and flight tests before being deployed on the actual system. This paper presents a Proportional plus Derivative (PD) based Cascade control algorithm to allow the quadcopter to go to its goal location. The whole control is split into four subsystems- one to control each output. Simulation results of the same are presented to validate the proposed method. Further, Pluto 1.2, an open-source drone, is used as a testbed for the project. Since there was no prebuilt navigation support, a Vision-based Navigation system using an overhead camera and ArUco tags is developed. This estimated position is sent to Pluto 1.2 as a Multiwii Serial Protocol (MSP) packet via WiFi communication. For this, a new MSP command is written by modifying the drone’s firmware. Finally, the complete navigation and communication system are tested in flight. The system developed for Pluto 1.2 is a novel one and, therefore, a contribution to the Open source aerial robotics community.
Analysis of insider threat detection methods tested on CERT dataset

Applying artificial intelligence for insider threat detection faced a number of challenges: lack of data, user behavior modeling, etc. Meanwhile, there are open datasets for research and there are effective approaches of insider threat detection. This work considers studies that utilizes one of the popular datasets — CERT. A comparative analysis contains features used for user behavior modeling and insider threat detection methods.
Human Behavior Estimation Using Micro-vibration Sensor Based on Deep Boltzmann Machine

In recent years, the aging of the population has become a social problem. Under such circumstances, domestic accidents have become a problem, and these accidents have a severe impact on the health of the elderly. Therefore, a system to watch over the elderly is needed to prevent accidents and detect accidents. However, the current practical system using camera images causes privacy problems for the target of the watch. In this study, we propose a behavior estimation system that uses micro-vibration sensors and deep Boltzmann machines to overcome the issue of privacy and the noise that micro-vibration sensors are prone to. The plan was trained for five behavioral patterns: normal walking, limping, jumping with both feet, jogging, and no action, and then behavior estimation experiments were conducted on the time series measured data for the five behavioral patterns. The experimental results showed that the estimation accuracy was about 72%, confirming the effectiveness of the proposed system. This result suggests that the proposed method can be an essential element for constructing a privacy-conscious watchdog system.
Usage of Electronic Resources by Faculty of Ballari Institute of Technology &amp; Management During COVID-19 Pandemic

This paper aims to analyze the usage of VTU-consortium e-resources through library remote access during pandemic COVID-19. The disturbing effects of the lockdown on the world economy are felt nearly in all circles of life. One of the sectors which are worst hit by the virus is the higher education sector where the students and staff are confined in their homes and educational transaction among them is completely dependent on information communication technology. The idea of an academic library is to upgrade and reinforce the training; learning and research by introducing regular document/information transfer the structure around the nation. it is hard to provide services and access to collections to the users who have been displaced due to COVID-19 barriers are facing challenges and restrictions in the lockdown, to achieve the use of e-resources were to enhance the pedagogic interests of the users and to enhance research capacity and continuity of research in disaster ‘protecting academic Interest of Students during COVID-19 Pandemic by Sharing Best Practices’ is commendable. Online education may become the new way of imparting education. Teaching and learning process during the pandemic period are only possible through alternative means of education. To ease the stress level of users in getting the necessary information, libraries themselves are doing great work to inform their members and support them. This paper also sheds some light on the growth of Startups during the time of pandemic and natural disasters and includes suggestions for academic institutions on how to deal with challenges related to e-resources.
Anticipating the Impact of Virtual Reality for Learning the Design Fundamentals: Students’ Perceptions and Perspectives

An increasing adoption of Immersive Virtual Reality (iVR) based solutions can be observed in higher education and in design education particularly, namely in the teaching and learning the design fundamentals, usually referred to as Basic Design (BD). Research on this subject focuses mainly the overall potential of this technology for developing new pedagogical tools for teaching, being scarce regarding the understanding of the students' expectations about the adoption of such technology, which is essential in a Human-Centred Design (HCD) approach. The main objective of this study was to understand students' familiarity and expectations regarding the use of iVR for teaching-learning BD. An online questionnaire assessed the students’ perception concerning: i) the difficulties related to the learning of the design fundamentals; ii) the suitability of the used digital technology; iii) and the receptivity/expectations to use iVR-based tools in the teaching-learning process. The results suggest that the students perceive the iVR-based tools as important and motivating for the learning process, being expected to assist the learning of all the BD-related content topics. Moreover, it is noteworthy that the most difficult content to learn appears to be 3D form, which iVR-based technologies are widely believed to aid in mastering. It is expected that the findings can support the development of learning activities assisted by iVR-based tools, more likely to meet the expectations of future students, thus contributing to engaging teaching-learning experiences and improved learning results.
Confidence-Aware Anomaly Detection in Human Actions

Anomaly detection in human actions from video has been a challenging problem in computer vision and video analysis. The human poses estimated from videos have often been used to represent the features of human actions. However, extracting keypoints from the video frames are doomed to errors for crowded scenes and the falsely detected keypoints could mislead the anomaly detection task. In this paper, we propose a novel GCN autoencoder model to reconstruct, predict and group the poses trajectories, and a new anomaly score determined by the predicted pose error weighted by the corresponding confidence score associated with each keypoint. Experimental results demonstrate that the proposed method can achieve state-of-the-art performance for anomaly detection from human action videos.
Gender differences in digital education in IT technologies

The article analyzes the gender problems in obtaining digital education, which make it difficult for women to work in the field of IT technologies. It is demonstrated that the widespread proliferation of digital technologies in almost all spheres of human activity made people’s lives much easier and facilitated the solution of problems that previously caused great difficulties and required significant time costs. However, the author shows that in practice, the use of IT (Information Technologies) immediately led to the segregation of the population, to its stratification, primarily on the basis of gender, as well as critically analyzes gender stereotypes.