Reviewerly/exHarmony huggingface dataset:
Bystander interruption of VR users

Research has begun to investigate the interruption of VR users but lacks an understanding of how social factors (setting / relationship to the VR user) might impact the interruption and why bystanders interrupt as they do. We conducted a survey (N=100) into bystander comfort when interrupting a VR user (known / unknown) in 4 settings (private spaces, public spaces, private transport, public transport) and their willingness to use a range of interruption strategies. Our results suggest relationship to the VR user is more influential than setting when considering comfort and acceptability of interruption strategy. A follow-up lab study (N=16) investigated bystander interruption of a known VR user in a private setting. Most used a combination of speech and touch to interrupt though a subset used unconventional, improvised strategies we term playful interruptions. With the recent inclusion of open ear audio in VR headsets and preference towards verbal interruptions in our results we conducted a survey (N=76) into the consumer response to open ear audio in VR headsets. We report open ear audio was used regularly by 52.6% of our respondents 60% of who rate their experience with it positively.
GPkit: A Human-Centered Approach to Convex Optimization in Engineering Design

We present GPkit, a Python toolkit for Geometric and Signomial Programming that prioritizes explainability and incremental complexity. GPkit was designed through an ethnographic approach in the firms, classrooms, and research labs where it became part of the fabric of daily engineering work. Organizations have approached GPkit both in ways which centralize and in ways which distribute design work, usecases which emerged from and inspired new toolkit features. This two-way flow between mathematical structure and practitioner knowledge resulted in several novel contributions to the formulation and interpretation of convex programs and to our understanding of early-stage engineering design. For example, dual solutions (often considered incidental) can be more valuable to a design process than the "optimal design" itself, and we present novel algorithms and design methods based on this insight.
PARCIV: Recognizing physical activities having complex interclass variations using semantic data of smartphone

Summary Smartphones are equipped with precise hardware sensors including accelerometer, gyroscope, and magnetometer. These devices provide real‐time semantic data that can be used to recognize daily life physical activities for personalized smart health assessment. Existing studies focus on the recognition of simple physical activities but they lacked in providing accurate recognition of physical activities having complex interclass variations. Therefore, this research focuses on the accurate recognition of physical activities having complex interclass variations. We propose a two‐layered approach called PARCIV that first clusters similar activities based on semantic data and then recognize them using a machine learning classifier. Our two‐layered approach first bounds the highly indistinguishable activities in clusters to avoid misclassification with other distinguishable activities and thereafter recognize them on a fine‐grained level within each cluster. To evaluate our approach, we make an android application that collects labeled data by using smartphone sensors from 10 participants, while performing activities. PARCIV recognizes distinguishable as well as indistinguishable activities with high accuracy of 99% on the self‐collected dataset. Furthermore, PARCIV achieve 95% accuracy on the publicly available dataset used by state‐of‐the‐art studies. PARCIV outperforms various state‐of‐the‐art studies by 8%‐17% for simple activities as well as complex activities.
BioSEAL

Genome sequences contain hundreds of millions of DNA base pairs. Finding the degree of similarity between two genomes requires executing a compute-intensive dynamic programming algorithm, such as Smith-Waterman. Traditional von Neumann architectures have limited parallelism and cannot provide an efficient solution for large-scale genomic data. Approximate heuristic methods (e.g. BLAST) are commonly used. However, they are suboptimal and still compute-intensive.
Information literacy trends in higher education (2006–2019): visualizing the emerging field of mobile information literacy

The thematic evolution of research on Mobile Information Literacy between 2006 and 2019 in the field of Information Literacy, learning and mobile technologies is analysed in an international context. For this purpose, the relevant bibliographic references from five databases (ERIC, LISA, LISTA, Scopus and WOS) were retrieved. To systematize the keywords, high dimensionality is reduced by means of a term-based process. Fields, topics, sub-topics and top terms are defined. The main top-terms and their relationships are analysed applying the fractional counting methodology using VOSViewer software. Fifteen major themes were set, which were grouped into six clusters to identify the main thematic trends during the period under review: IL and e-learning, Mobile devices and competencies, Ethics, Library and e-resources, Educational technology and Technological environment. The convergence of IL and e-learning, the growth of e-literacy, the increasing relationship between mobile devices and information competencies, as well as that of libraries and e-resources, are thus detected. In conclusion, there is evidence of a growing interdisciplinarity in the scientific publications on Mobile Information Literacy, which interrelates the studies of information and digital literacy with e-learning and mobile technologies.
Anomaly Detection in Traffic Surveillance Videos with GAN-based Future Frame Prediction

It is essential to develop efficient methods to detect abnormal events, such as car-crashes or stalled vehicles, from surveillance cameras to provide in-time help. This motivates us to propose a novel method to detect traffic accidents in traffic videos. To tackle the problem where anomalies only occupy a small amount of data, we propose a semi-supervised method using Generative Adversarial Network trained on regular sequences to predict future frames. Our key idea is to model the ordinary world with a generative model, then compare a predicted frame with the real next frame to determine if an abnormal event occurs. We also propose a new idea of encoding motion descriptors and scaled intensity loss function to optimize GAN for fast-moving objects. Experiments on the Traffic Anomaly Detection dataset of AI City Challenge 2019 show that our method achieves the top 3 results with F1 score 0.9412 and RMSE 4.8088, and S3 score 0.9261. Our method can be applied to different related applications of anomaly and outlier detection in videos.
Optimization Complete Area Coverage by Reconfigurable hTrihex Tiling Robot

Completed area coverage planning (CACP) plays an essential role in various fields of robotics, such as area exploration, search, rescue, security, cleaning, and maintenance. Tiling robots with the ability to change their shape is a feasible solution to enhance the ability to cover predefined map areas with flexible sizes and to access the narrow space constraints. By dividing the map into sub-areas with the same size as the changeable robot shapes, the robot can plan the optimal movement to predetermined locations, transform its morphologies to cover the specific area, and ensure that the map is completely covered. The optimal navigation planning problem, including the least changing shape, shortest travel distance, and the lowest travel time while ensuring complete coverage of the map area, are solved in this paper. To this end, we propose the CACP framework for a tiling robot called hTrihex with three honeycomb shape modules. The robot can shift its shape into three different morphologies ensuring coverage of the map with a predetermined size. However, the ability to change shape also raises the complexity issues of the moving mechanisms. Therefore, the process of optimizing trajectories of the complete coverage is modeled according to the Traveling Salesman Problem (TSP) problem and solved by evolutionary approaches Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Hence, the costweight to clear a pair of waypoints in the TSP is defined as the required energy shift the robot between the two locations. This energy corresponds to the three operating processes of the hTrihex robot: transformation, translation, and orientation correction. The CACP framework is verified both in the simulation environment and in the real environment. From the experimental results, proposed CACP capable of generating the Pareto-optimal outcome that navigates the robot from the goal to destination in various workspaces, and the algorithm could be adopted to other tiling robot platforms with multiple configurations.
Blockchain Technology: Challenges and Security issues in Consensus algorithm

Blockchain is picking up footing and one of the omnipresent themes these days can be named. Despite the fact that faultfinders are challenging its durability, safety and stability, it has officially changed the way of life of numerous people in a few territories due to its overwhelming impact on projects, moreover, organizations. Giving that the highlights of blockchain innovation ensure more solid and convenient administrations, it is imperative to think about the security furthermore, protection concerns and complications behind the inventive innovation. The range of blockchain applications extends to open and public services including health, financial, automotive, Internet of Things (IoT) and risk management. Many talk about using the blockchain data system for various applications in the spotlight. Nonetheless, a comprehensive review on the point of view of specialized applications and applications has not yet been an expert. In this paper, we seek to perform a thorough study on the technology of blockchain by analyzing its architecture of varying consensus algorithms over and above challenges and opportunities for security and data protection within blockchains.
F-test feature selection in Stacking ensemble model for breast cancer prediction

Cancer data sets contains many details of patient information, out of which only a few attributes contribute in predicting the accurate stage of cancer. Certain attributes of the entire data set play a major role in deciding the type of cancer i.e. whether benign or malignant hence feature selection techniques are useful in such scenarios for retaining the relevant feature set. Moreover, in order to achieve our goal of predicting the accurate stage of cancer, we need an appropriate model which generally results in higher accuracy and ensemble model proves to be the best model for such scenarios. In this study, we are using the existing ensemble techniques along with a combination of supervised machine learning algorithms to develop a new model for breast cancer prediction. We are also using feature selection techniques to enhance the performance of the ensemble model. For this purpose, machine learning algorithms like Support Vector Machines, Naive Bayes, K-Nearest Neighbors, Logistics Regression and feature selection techniques like Variance threshold and f-test have been taken into consideration. To achieve higher accuracy for the ensemble model, bagging, boosting and stacking techniques are used.
Reactive sampling-based path planning with temporal logic specifications

We develop a sampling-based motion planning algorithm that combines long-term temporal logic goals with short-term reactive requirements. The mission specification has two parts: (1) a global specification given as a linear temporal logic (LTL) formula over a set of static service requests that occur at the regions of a known environment, and (2) a local specification that requires servicing a set of dynamic requests that can be sensed locally during the execution. The proposed computational framework consists of two main ingredients: (a) an off-line sampling-based algorithm for the construction of a global transition system that contains a path satisfying the LTL formula; and (b) an on-line sampling-based algorithm to generate paths that service the local requests, while making sure that the satisfaction of the global specification is not affected. The off-line algorithm has four main features. First, it is incremental, in the sense that the procedure for finding a satisfying path at each iteration scales only with the number of new samples generated at that iteration. Second, the underlying graph is sparse, which implies low complexity for the overall method. Third, it is probabilistically complete. Fourth, under some mild assumptions, it has the best possible complexity bound. The on-line algorithm leverages ideas from LTL monitoring and potential functions to ensure progress towards the satisfaction of the global specification while servicing locally sensed requests. Examples and experimental trials illustrating the usefulness and the performance of the framework are included.
Blockchain-Based Data Sharing and Trading Model for the Connected Car

Currently, "connected cars" are being actively designed over smart cars and autonomous cars, to establish a two-way communication network between the vehicle and all infrastructure. Additionally, because vehicle black boxes are becoming more common, specific processes for secure and efficient data sharing and transaction via vehicle networks must be developed. In this paper, we propose a Blockchain-based vehicle data marketplace platform model, along with a data sharing scheme, using Blockchain-based data-owner-based attribute-based encryption (DO-ABE). The proposed model achieves the basic requirements such as data confidentiality, integrity, and privacy. The proposed system securely and effectively handles large-capacity and privacy-sensitive black box video data by storing the metadata on Blockchain (on-chain) and encrypted raw data on off-chain (external) storage, and adopting consortium Blockchain. Furthermore, the data owners of the proposed model can control their own data by applying the Blockchain-based DO-ABE and owner-defined access control lists.
Human-robot interaction: Conceptualising trust in frontline teams through LEGO® Serious Play®

Due to fast-paced developments in the technological sphere, we witness robots becoming commonplace in hospitality. While research has focused on technical and functional aspects of robots, the ways in which robots could become effective members of human service teams are less understood. By taking a human-robot interaction (HRI) lens, this study investigates the impact of integrating non-human service providers into human teams with a focus on trust. A qualitative focus group approach with the LEGO® Serious Play® method is employed to open novel avenues in tourism research, and to build, visualise and distil what human-robot interaction in hospitality frontline teams could look like. The study's contribution is a conceptual framework that reveals three dominant factors of trust and their sub-dimensions necessary for successful future human-robot interaction in frontline teams in tourism, hospitality and the wider service sector.
Mining Spatial-Temporal Similarity for Visual Tracking

Correlation filter (CF) is a critical technique to improve accuracy and speed in the field of visual object tracking. Despite being studied extensively, most existing CF methods suffer from failing to make the most of the inherent spatial-temporal prior of videos. To address this limitation, as consecutive frames are eminently resemble in most videos, we investigate a novel scheme to predict targets' future state by exploiting previous observations. Specifically, in this paper, we propose a prediction based CF tracking framework by learning the spatial-temporal similarity of consecutive frames for sample managing, template regularization, and training response pre-weighting. We model the learning problem theoretically as a novel objective and provide effective optimization algorithms to solve the learning task. In addition, we implement two CF trackers with different features. Extensive experiments are conducted on three popular benchmarks to validate our scheme. The encouraging results demonstrate that the proposed scheme can significantly boost the accuracy of CF tracking, and the two trackers achieve competitive performances against state-of-the-art trackers. We finally present a comprehensive analysis on the efficacy of our proposed method and the efficiency of our trackers to facilitate real-world visual tracking applications.
Development of an Online Health Care Assessment for Preventive Medicine: A Machine Learning Approach

Background In the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows patients to be aware of their clinical conditions, which may contribute toward the prevention of several chronic diseases and disorders. Objective In this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs) of subjects who have undergone health examination. This system aims to provide online self-health evaluation to clinicians and patients worldwide, enabling personalized health and preventive health. Methods We built a medical database system based on the literature, and data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data to establish prediction models. The models with EMR databases were then applied to the internet platform. Results The validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 91%, and the area under the receiver operating characteristic (ROC) curve was 0.904 in this system. For chronic kidney disease, the prediction accuracy of the model reached 94.7%, and the area under the ROC curve (AUC) was 0.982. In addition, the system also provided disease diagnosis visualization via clustering, allowing users to check their outcome compared with those in the medical database, enabling increased awareness for a healthier lifestyle. Conclusions Our web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we aim to connect hospitals worldwide with our platform, so that health care practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.
A ciphertext-policy Attribute based encryption scheme for wireless body area networks based on ECC

The Internet of Things (IoT) based healthcare system is one of the prominent approaches to overcome the issue of the increasing burden of healthcare costs in India. Wireless Body Area Networks (WBANs) are the key enabler in this scenario. It provides continuous monitoring of the patient’s health condition remotely, by making the real-time health data of the patient available to healthcare professionals through cloud/public channel. On the other side, the practical implementation of this service is not possible if the issue of data security is not considered. Thus, in this paper, we have proposed a secure framework for WBAN using Elliptic Curve Cryptography based Ciphertext-Policy Attribute Based Encryption (CPABE) without bilinear pairing operations. The proposed CPABE is secured under Elliptic Curve Decisional Diffie-Hellman assumption and also has a feature of user/attribute revocation. We have evaluated the lightweight feature of the proposed CPABE by comparing it with other existing ABE schemes for WBAN. The result shows that our scheme outperforms the existing schemes for WBAN in terms of keys-ciphertext size and computation overhead.
A New Polarization Image Demosaicking Algorithm by Exploiting Inter-Channel Correlations With Guided Filtering

This paper presents a fast and effective polarization image demosaicking algorithm, which explores inter-channel dependency of Stokes parameters for the minimization of residual aliasing artifacts after cubic spline interpolation. A guided filtering approach is used for denoising. An optimization based on the confidence level of the aforementioned guided filtering, the correlations between the demosaicked image and input, as well as the total intensity, angle and degree of linear polarization, is constructed and solved with Newton's method. Experimental results demonstrate that the proposed algorithm can surpass the existing methods in terms of both objective root mean squared error and structural similarity index by at least 36.0% and 3.4%, respectively, and by close visual inspection of the clarity of objects in the angle and degree of linear polarization images. The proposed algorithm consists of only convolutions and element-wise operations, making it fast and parallelizable for efficient GPU acceleration. An image of size 512 × 612 × 4 can be processed within 10 s on i7-6700k CPU, and gains further 5 times speedup with M4000M GPU.
Triple-GAN: Progressive Face Aging with Triple Translation Loss

Face aging is a challenging task which aims at rendering face for input with aging effects and preserving identity information. However, existing methods have split the long term into several independent groups and ignore the correlations of age growth. To better learn the progressive translation of age patterns, we propose a novel Triple Generative Adversarial Networks (Triple-GAN) to simulate face aging. Instead of formulating ages as independent groups, Triple-GAN adopts triple translation loss to model the strong interrelationship of age patterns among different age groups. And to further learn the target aging effect, multiple training pairs are offered to learn the convincing mappings between labels and patterns. The quantitative and qualitative experimental results on CACD, MORPH and CALFW show the superiority of Triple-GAN in identity preservation and age classification.
Orderless Recurrent Models for Multi-Label Classification

Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. Current approaches sort labels according to their frequency, typically ordering them in either rare-first or frequent-first. These imposed orderings do not take into account that the natural order to generate the labels can change for each image, e.g. first the dominant object before summing up the smaller objects in the image. Therefore, in this paper, we propose ways to dynamically order the ground truth labels with the predicted label sequence. This allows for the faster training of more optimal LSTM models for multi-label classification. Analysis evidences that our method does not suffer from duplicate generation, something which is common for other models. Furthermore, it outperforms other CNN-RNN models, and we show that a standard architecture of an image encoder and language decoder trained with our proposed loss obtains the state-of-the-art results on the challenging MS-COCO, WIDER Attribute and PA-100K and competitive results on NUS-WIDE.