Reviewerly/exHarmony huggingface dataset:
Psychological and situational profiles of social distance compliance during COVID-19

Background Health advice in the wake of the COVID-19 pandemic has called upon the public to re-evaluate risk associated with recently routine behavior. However, differences in demographics, situational circumstances, and psychological dispositions create inequities in how people are able to respond to risks presented by the virus.Method Within a sample of 482 Americans, we examined the frequency of behavior reconceptualized as ‘risky’ by CDC public health guidelines released on 30 March 2020. We applied a cluster analysis using a data-driven persona framework from the field of user-design research, using only situational and dispositional (i.e. psychological) variables to identify profiles of individuals.Results This profile approach unpacked important variability in the evaluation of risk for COVID-19 contagion, as well as adherence to public health guidance. Profiles engaged in high-risk behaviors were more likely required to work on-site and report higher financial impact related to the pandemic.Conclusion Applying the profile approach facilitates personalized communications tailored to the psychological and situational circumstances of each profile that can promote compliance with public health guidelines and guide policy decisions. These results also suggest that risk-taking behaviors within the context of COVID-19 may also be driven by factors related to economic inequity since those who are more likely to be essential workers do not have the ability to remain as compliant to social distancing compared to those with higher economic status. Recommendations for policies promoting federally mandated paid leave policy in the US and employing qualitative methods for future research is discussed.
Multi-object tracking for road surveillance without using features of image data

Visual surveillance of dynamic objects on roads has been developed to ensure road safety for people. Particularly, vehicle tracking is considered as a key technology for the road safety; studies on multi-object tracking (MOT) are being actively pursued. However, when MOT is performed, raw vision data are not always available because of the technical limitation or the privacy concern of the system; MOT needs to be performed only using the coordinates obtained from the object detector without using features extracted from raw image data such as color of vehicles, which degrades the accuracy of MOT to the unsatisfactory level for road safety. This paper proposes an MOT scheme for moving vehicles that is inspired by cell tracking using the Viterbi algorithm. The proposed scheme extends the Brownian motion model, which was used in the base scheme of cell tracking, by weighting probability transitions in accordance with the direction of travel of vehicles on the road. We evaluate the proposed scheme using simulated vehicle-traffic data and verify that the proposed scheme performs better than benchmark schemes in terms of the accuracy of MOT. We also demonstrate an example of how the proposed scheme works well for real vehicle-traffic data.
Soft-ACK Feedback Based Link Adaptation for Latency Critical Applications in 5G/B5G

Link adaptation plays an essential role in optimizing the performance of wireless communications systems. With the emerging services, link adaptation needs to operate with stringent reliability and latency requirements. This paper introduces soft-ACKINACK based link adaptation, which is an adaptation of conventional OLLA to the high reliability and low latency requirements by a range of emerging 5G/6G NR applications, such as URLLC/IIoT, AV/AR, cloud gaming or V2X. We study the limitations of conventional OLLA for these scenarios, and introduce an enhanced OLLA algorithm using soft-ACK/NACK feedback from the UE. We analyse the possible methods to derive soft-ACK/NACK reports, and propose using flipped bit counts based on accuracy and feasibility. The performance gains of soft-OLLA are assessed in performance simulations.
Enabling personal consent in databases

Users have the right to consent to the use of their data, but current methods are limited to very coarse-grained expressions of consent, as "opt-in/opt-out" choices for certain uses. In this paper we identify the need for fine-grained consent management and formalize how to express and manage user consent and personal contracts of data usage in relational databases. Unlike privacy approaches, our focus is not on preserving confidentiality against an adversary, but rather cooperate with a trusted service provider to abide by user preferences in an algorithmic way. Our approach enables data owners to express the intended data usage in formal specifications, that we call consent constraints , and enables a service provider that wants to honor these constraints, to automatically do so by filtering query results that violate consent; rather than both sides relying on "terms of use" agreements written in natural language. We provide formal foundations (based on provenance), algorithms (based on unification and query rewriting), connections to data privacy, and complexity results for supporting consent in databases. We implement our framework in an open source RDBMS, and provide an evaluation against the most relevant privacy approach using the TPC-H benchmark, and on a real dataset of ICU data.
Towards Building A Robust Large-Scale Bangla Text Recognition Solution Using A Unique Multiple-Domain Character-Based Document Recognition Approach

Bangla is one of the world’s top ten popular languages in terms of the number of speakers. It also happens to have a complex script primarily because of complex characters, e.g. graphemes, composed of multiple single characters, and the characteristic short-hands, e.g. vowel diacritics, and consonant diacritics making the number of classes of this script recognition quite large, varied, and challenging. In this paper, we present a unique large-scale Bangla document OCR solution based on character-level recognition modules. We have tested our approach on two independent domains: printed and handwritten documents. We also applied our solution to three subdomains within the printed domain: computer-composed documents, letterpress documents, and typewritten documents. Our extensive experiments show that our approach achieves state-of-the-art performance on handwritten and printed documents.
Knowledge Graph Embedding Based on Quaternion Transformation and Convolutional Neural Network

In the cognitive intelligence of machines, more and more researchers use knowledge graphs that represent the structure of entities. The basic idea of knowledge representation learning is to project knowledge (entities and relations) into a vector space. The entities and relationships embedded in the 2-D space use convolutional neural networks to obtain the mutual information between them. However, the feature extraction capabilities of neural networks are limited by the input feature information. Therefore, our model named knowledge graph embedding based on quaternion transformation and convolutional neural network (QCKGE), aims to solve the limitation of input features by riching entities and relations mutual features. Firstly, the entity and relation are expressed as a two-dimensional real number space. Then, these features are mapped to the quaternion space to increase the ability to express entities and relations. Finally, the convolutional neural networks to mine richer interactive information in entities and relationships for link prediction. Besides, this paper also uses multi-layer convolutional neural network to mine deeper information hidden in the features. On the datasets of WN18, FB15k, WN18RR, and FB15k-237, compared with the existing model, our design can show a better result.
Ontology Model for Public Services in Morocco Based on 5W1H Approach: PSOM-eGovMa

Morocco has launched several e-government programs to develop efficient public e-services which meet the needs of users. However, while digitizing public services processes for Moroccan administrations, the designers of information systems record the entered data in databases, without being too interested in the meaning of the entries made by citizens nor on the benefit of exchanged information between public administrations. This causes inconsistency and lack of interoperability between public administration systems. Moreover, the systems require information collection and shared knowledge based on a common vocabulary of public services in Morocco. For this purpose, this paper presents an ontology model based on the 5w1h Method for defining the public services domain. This PSOM ontology is based on the referential defined by the Moroccan Ministry of Administration Reform and Public service. Indeed, we have collected the different concepts related to this field and all possible relationships between them in order to ensure interoperability between all government entities.
Dynamic modeling of hospitalized COVID-19 patients reveals disease state–dependent risk factors

The study sought to investigate the disease state-dependent risk profiles of patient demographics and medical comorbidities associated with adverse outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections.A covariate-dependent, continuous-time hidden Markov model with 4 states (moderate, severe, discharged, and deceased) was used to model the dynamic progression of COVID-19 during the course of hospitalization. All model parameters were estimated using the electronic health records of 1362 patients from ProMedica Health System admitted between March 20, 2020 and December 29, 2020 with a positive nasopharyngeal PCR test for SARS-CoV-2. Demographic characteristics, comorbidities, vital signs, and laboratory test results were retrospectively evaluated to infer a patient's clinical progression.The association between patient-level covariates and risk of progression was found to be disease state dependent. Specifically, while being male, being Black or having a medical comorbidity were all associated with an increased risk of progressing from the moderate disease state to the severe disease state, these same factors were associated with a decreased risk of progressing from the severe disease state to the deceased state.Recent studies have not included analyses of the temporal progression of COVID-19, making the current study a unique modeling-based approach to understand the dynamics of COVID-19 in hospitalized patients.Dynamic risk stratification models have the potential to improve clinical outcomes not only in COVID-19, but also in a myriad of other acute and chronic diseases that, to date, have largely been assessed only by static modeling techniques.
Wireless Standard Classification Using Convolutional Neural Networks

The growing prominence of spectrum sharing technologies has spurred interest in spectrum monitoring technologies with the ability to identify unknown wireless signals. This paper presents a convolutional neural network (CNN) deep learning model to classify 4G LTE downlink, 4G LTE uplink, 5G NR downlink, 5G NR uplink, IEEE 802.11ax (WiFi 6), and Bluetooth Low Energy (BLE) 5.0 signals. The classifier operates on In-phase and Quadrature (I/Q) samples and does not require synchronization with the unknown signals. To improve the generalizability of the classifier, comprehensive signal datasets are generated to include a wide range of signal configurations found in the standards. These signals are impaired with additive white Gaussian noise (AWGN), Rayleigh or Ricean multipath fading channels, frequency offsets, and I/Q imbalances to make the signals more realistic. The exploration of time domain, frequency domain, and time-frequency domain features reveals high frequency resolution time-frequency domain features perform best. The proposed CNN model achieves a high classification accuracy in the presence of all of the aforementioned impairments, achieving over 94&#x0025; accuracy for signal to noise ratios (SNR) greater than 0 dB.
PRIVACY PRESERVATION OF SENSITIVE DATA IN BIG DATA ANALYTICS - A SURVEY

The world of data analytics has changed forever because to the advent of big data. Data that was deemed useless just a few years ago is now seen as a valuable resource. In today's world, big data is being used widely for the discovery of new information in virtually every industry. And one of these sectors is the communication industry. Because of the development of modern technologies, data gathering has been easier, but this also brings with it a serious risk: the risk of compromising people's privacy. Individual private data must be protected from prying eyes thanks to privacy preservation. Comparative study has been done on many research methodologies such as privacy preservation mechanisms, k-anonimity, 1-diversity, t-closeness and various cryptographic algorithms has been analyzed and their performance measures on security issues has been surveyed.
Decentralized used cars Bidding application using Ethereum

Blockchain technology is used to decentralize ledger to keep transaction records. The record of transactions is persistent in a peer-to-peer network. This eliminates central authority needed to confirm the transactions. The participants verify the transactions in peer-to-peer networks. The need for any central authority for any kind of trade settlement, voting, or money transactions is nullified. Blockchain technology has been revolutionizing Information Technology (IT) for the past few years. It is being used in supply chain, health sector, Internet of Things, asset management, banking sector, payments and many more. Blockchain allows decentralization, transparency, security and increased efficiency in the applications. With the increasing awareness of cryptocurrency, the significance of blockchain is obtaining its popularity across the globe.Cars being the most popular mode of transport used by the majority of the population across the globe. Because of the increasing prices of new cars, the popularity of used cars (second hand cars) has increased exponentially over all these years. Identifying this opportunity in the used cars segment, we propose an application that uses blockchain to connect buyers and sellers. By implementing decentralization, we aim to solve security related issues in existing centralized applications. We have implemented an e-auction methodology on top of Ethereum Blockchain. The proposed application allows bidders to directly bid the products over the Internet. The blockchain technology with low transaction cost is used to develop the smart contract of public bid and sealed bid.
ZF-SSE: A Unified Sequential Semantic Encoder for Zero-Few-Shot Learning

Humans can inherently recognize various objects and gestures either from very few examples or from their descriptions. However, supervised gesture classification methods require hundreds of examples to learn to classify, and cannot rapidly generalize from either few examples or from their high-level descriptions. This gap can be bridged using few-shot (FSL) and zero-shot (ZSL) learning methods (jointly referred to as ZFSL). Previous approaches studied the problems of ZSL and FSL in isolation, and there are few works concerned with ZFSL for temporal problems such as unfamiliar gesture recognition. In this context, this paper represents categories as semantic descriptions in a high-level attribute space and proposes a unified framework to facilitate ZFSL. This work further introduces an approach referred to as Unified Sequential Semantic Encoder (ZF -SSE) to explore temporal patterns and to predict the semantic information through simultaneously optimizing for both classification and semantic tasks. The proposed framework is validated in the domain of hand gesture recognition and the results show that the ZF -SSE approach significantly outperforms existing approaches by at least 4-10% in zero-shot, few-shot, and open-set experimental conditions.
Machine-learning techniques for quantifying the protolith composition and mass transfer history of metabasalt

Abstract The mass transfer history of rocks provides direct evidence for fluid–rock interaction within the lithosphere and is recorded by compositional changes, especially in trace elements. The general method adopted for mass transfer analysis is to compare the composition of the protolith/precursor with that of metamorphosed/altered rocks; however, in many cases the protolith cannot be sampled. With the aim of reconstructing the mass transfer history of metabasalt, this study developed protolith reconstruction models (PRMs) for metabasalt using machine-learning algorithms. We designed models to estimate basalt trace-element concentrations from the concentrations of a few (1–9) trace elements, trained with a compositional dataset for fresh basalts, including mid-ocean ridge, ocean-island, and volcanic arc basalts. The developed PRMs were able to estimate basalt trace-element compositions (e.g., Rb, Ba, U, K, Pb, Sr, and rare-earth elements) from only four input elements with a reproducibility of ~ 0.1 log 10 units (i.e., ± 25%). As a representative example, we present PRMs where the input elements are Th, Nb, Zr, and Ti, which are typically immobile during metamorphism. Case studies demonstrate the applicability of PRMs to seafloor altered basalt and metabasalt. This method enables us to analyze quantitative mass transfer in regional metamorphic rocks or alteration zones where the protolith is heterogeneous or unknown.
Contributed Session II: Visual Search in Virtual Reality (VSVR): A visual search toolbox for virtual reality

Our understanding of human visual attention has greatly benefited from a wealth of visual search studies conducted over the past few decades. Task observers with searching for a specific target embedded among a set of distractors, and the time taken for them to find the target can reveal much about the sensory and cognitive processes involved. These experiments have typically been conducted on 2D displays under tightly controlled viewing conditions. Recently however, there have been calls within the visual attention community to explore more ecologically valid means of data collection. Virtual reality (VR) is a promising methodological tool for such research as it offers improved visual realism and the possibility of participant interaction, while retaining a significant amount of the control afforded by a computerized and monitor presented experiment. Here we present the Visual Search in Virtual Reality (VSVR) ToolBox. VSVR is a set of functions, scripts and assets that can be combined within a visual scripting environment in the Unity game engine to design, replicate and extend visual search experiments in VR. We further demonstrate the utility of such a toolbox with three experiments: a replication of feature search behavior, a demonstration of wide field-of-view visual search and eccentricity effects, and replication of depth plane as a feature for search.
Analysis of U-Net Based Image Segmentation Model on Underwater Images of Different Species of Fishes

Image segmentation is one of the key tasks for image processing in robotic vision, navigation, virtual reality, and augmented reality. Segmentation in underwater images can help to overcome many challenges in exploring vast marine biological resources and gene banks. This work compares the performance of image segmentation using the U-Net architecture based model for underwater semantic image segmentation on selected five different fish species from the Fish4Knowledge image dataset. Analysis on the effect of different threshold values on refining the predicted segmented mask was also performed.
VFH+ Based Local Path Planning for Unmanned Surface Vehicles

In the field of marine engineering, the path planning problem of unmanned surface vehicles (USVs) is particularly important, especially in complex marine environments. Path planning for USVs is the process of regulating them to find an optimal or sub-optimal path from the starting point to the target point. It should guarantee that USVs can avoid obstacles based on one or more performance indicators (such as the lowest working cost, the shortest navigation route, the shortest navigation time, etc.). Path planning methods can be divided into two types: global and local ones. Generally, USVs with only global path planning ability cannot adapt to real-time changing marine environments, while local path planning is more effective for real-time path planning. By introducing an improved VFH+method, this paper proposes a local path planning framework for a USV equipped with a laser radar. It improves the USV’s dynamic obstacle avoidance ability from two aspects: (i) Polar obstacle densities are adopted to identify dynamic obstacles quickly; (ii) By considering velocity vectors of the USV and dynamic obstacles, candidate directions are optimized. The effectiveness of the proposed method is verified through an experiment. The experimental results demonstrate that the proposed method can guarantee safe autonomous navigation of a USV in unknown static and dynamic marine environments.
PyLithics: A Python package for stone tool analysis

Archaeologists have long used stone tools (lithics) to reconstruct the behaviour of prehistoric hominins.While techniques have become more quantitative, there still remain barriers to optimizing data retrieval (Andrefsky,William, 2012).Machine learning and computer vision approaches can be developed to extract quantitative and trait data from lithics, photographs and drawings.PyLithics has been developed to capture data from 2D line drawings, focusing on the size, shape and technological attributes of flakes.The problems addressed in the software are: one, capturing data in a form that can be quantified, and information maximized; two, solving the challenges of data that is not a simple linear sequence of bases but complex 3D objects or 2D image representations; and three, transforming and exporting these into systematic data for analysis.The goal is to enhance the size and quality of lithic databases for analysing ancient technology and human behaviour.
A Script Independent Hybrid Feature Extraction Technique for Offline Handwritten Devanagari and Bangla Character Recognition

Recognizing handwritten characters plays a significant role in different applications of pattern recognition. That is why the digital representation of character images is much necessary to design an efficient offline Optical Handwritten Character Recognition System (Offline HCR). Here a hybrid feature representation method is suggested for two Indic scripts, such as Devanagari and Bangla. The method utilizes three different features to represent any character images. Those are angular motion of character shape-based feature, center to the thin text of character shape-based feature, and center to edge text of character shape-based feature. After collecting all these three features, these are applied to various machine learning algorithms, including two modified neural network models. One simple traditional convolutional neural network is also designed which takes immediate images and recognizes the character images. Although the two modified neural network models are unable to hit the peak in terms of accuracy like the traditional CNN, it is found that two of our modified NN models take quite less time to execute upon the character datasets.