Reviewerly/exHarmony huggingface dataset:
COVID Alert: Factors Influencing the Adoption of Exposure Notification Apps Among Canadian Residents

The continued emergence of new variants of COVID-19 such as the Delta and Omicron variants, which can cause breakthrough infections, indicates that contact tracing and exposure notification apps (ENAs) will continue to be useful for the long haul. However, there is limited work to uncover the strongest factors that influence their adoption. Using Canada's "COVID Alert" as a case study, we conducted an empirical, technology-acceptance study to investigate the key factors that account for users' intention to use ENAs and the moderating effect of important human and design factors. Our path model analysis shows that four factors significantly influence the adoption of COVID Alert among Canadian residents: perceived risk, perceived usefulness, perceived trust, and perceived compatibility. The overall model explains over 60% of intention to use, with type of design, use case (functional interface), and adoption status moderating the strength of the relationships between the four factors and intention to use. We discuss these findings and make recommendations for the design of future ENAs.
IoT based Smart Gardening for Smart Cities using Blockchain Technology

The automation of agriculture is growing and thrust areas in a technological world. Previously, the data&#x0027;s are monitored in simple LCD screen, but in our article, we are introducing a novel idea to continue monitoring of the water level in an area and pumping the water, and we can also send it by GSM, then the analyzed data can send to gardener or the person who monitors the area using GSM Technology. A smart gardening is an electronic device designed to assist in alerting somebody in the situations where there is no water or water were filled in the garden/smart cities which is implemented by blockchain technology. The alarm can be used to request emergency assistance from local services or emergency services.
Traffic-Informed Multi-Camera Sensing (TIMS) System Based on Vehicle Re-Identification

Surveillance cameras are widely deployed traffic sensors, due to their affordable prices and being able to capture rich information. However, current surveillance systems have not been fully exploited: these cameras are isolated and can only extract information from their own fixed views. To enable a collaborative sensing system, we propose a novel framework called Traffic-Informed Multi-camera Sensing (TIMS) system for network-level traffic information estimation. By pushing multi-camera Re-IDentification (ReID) workflow towards network-wide traffic information extraction, TIMS system integrates a customized metric-learning vision-based vehicle ReID method (TIM-ReID) and establishing a traffic-informed workflow. To integrate the traffic network connection information along with visual and vehicle attributes features, the road network is extracted as a weighted graph through the Spatial-temporal Camera Graph Inference Model (StCGIM) and serves for matching and re-ranking ReID candidates. Moreover, an Accuracy Model (AAM) is designed to provide accurate, reliable and comprehensive traffic information estimation, including both the values and distribution of parameters under a high penetration rate. In experiments based on real-world multi-camera datasets captured in the city of Seattle, the customized TIM-ReID outperforms existing state-of-the-art methods, and delivers accurate cross-camera information estimation, whose value error is less than 8% and the Kullback-Leibler (KL) distance between the estimated and real distribution is less than 3.42 among all the evaluated camera pairs. TIMS system empowers cameras to work collaboratively through an interactive brain, and provides users with valuable and comprehensive traffic information.
Blockchain-Based Privacy-Preserving Vaccine Passport System

In this study, we propose a blockchain-based privacy-preserving vaccine passport system for the global prevention and control of infectious diseases. The system operates a double-chain framework which consists of a public blockchain and a consortium blockchain. Among them, the combination of the immutability of the public blockchain and Internet of Things (IoT) technology in the supply chain ensures the openness and transparency of the cold chain logistics records of the vaccines covering the stages from auditing to the target vaccination hospitals. The system adopts the consortium blockchain to achieve the balance between the protection of users’ vaccination privacy and auditing by the government departments. Specifically, a distributed system-based threshold signature is adopted in the vaccine qualification phase to resist collusion between the vaccine manufacturing company and vaccine approval institutions. The cryptographic tools such as the anonymous credentials, zero-knowledge protocols, and range proofs ensure that users do not disclose any private information other than proving that they have a legally valid vaccine passport when users display the vaccine passports to customs. At the same time, customs can apply various vaccine prevention policies based on the conditions on the specific vaccine passports. Regarding the security properties of the system, a formal security model is given along with the corresponding security proofs.
Person re-identification: A taxonomic survey and the path ahead

Person re-identification (PRId) is one of the most challenging tasks in automated video surveillance and has been an area of intense research spanning the past decade. PRId aims at finding a person who has previously been identified using some unique descriptor of the person. This survey comprises a wide spectrum of PRId methods spanning from traditional to deep learning-based being analyzed and compared. This survey also discusses different PRId frameworks on the basis of machine learning and deep learning. It offers a multi-dimensional taxonomy to classify the most pertinent researches according to different perspectives and tries to unify the categorization of PRId methods and fill the gap between the recently published surveys. This study highlights the challenges in building PRId systems. It presents a critical overview of recent progress and the state-of-the-art approaches to solving some major challenges of existing PRId systems. Furthermore, we discuss the performance comparisons of the various state-of-the-art in different datasets. Finally, we discuss several open issues and directions for future studies.
A Situation Awareness Approach for Smart Home Management

Nowadays, the digitization of reality is increasingly prominent due to the Internet of Things paradigm. Even the environments we live in, such as homes, can be equipped with smart devices that make their management more effective. This paper presents a case study related to the Smart Home field, which exploits graph approaches to provide suggestions to users in home management. The proposed approach allows extracting and elaborating data from the context and using probabilistic approaches to provide helpful information and autonomous actions. The learning phase is related to graph approaches able to represent and able to increase the comprehension of the environment. The experimental phase is conducted through a prototype to test the ability of the proposed approach in saving energy and ventilation management. The experiments consist of the accuracy measure and a questionnaire, and the obtained results are promising.
Toward practical privacy-preserving linear regression

Linear regression is an ordinary machine learning algorithm that models the relation between the input values and the output ones with underlying linear functions . Giacomelli et al. (ACNS 2018) proposed the first system training the linear regression model over the rational numbers using only linearly homomorphic encryption . However, we find their system model is not applicable. A third authority generates the public key and secret key, which are used to encrypt and decrypt all the data sets. Then the privacy of data sets is in the risk of leakage even if the third authority is assumed to have no access to encrypted data sets. In this paper, we improve the system model in order to design a more practical linear regression algorithm over the rational numbers from the view of security. Concretely, every data owner generates his own public key and secret key, independent on a third authority. An improved multi-key fully homomorphic encryption over complex numbers is utilized to construct our linear regression algorithm with a preprocessing phase , which can directly encrypt rational numbers, support computations over ciphertexts under multi keys and obviate the rational reconstruction technique as Giacomelli et al.. Furthermore, performance analyses demonstrate that our algorithm is more feasible and practical.
Hybrid memristor optoelectronic integrated circuits for optical computing

We present a novel technology wherein memristors are heterogeneously integrated with optoelectronic devices on a silicon photonic platform. We present results on memristor integrated microring modulators and lasers with non-volatile memory. Furthermore, multiple devices are combined with optical waveguides to create photonic integrated circuits with neuromorphic computing. By pairing non-volatile memory devices directly with silicon photonics, we can integrate memory, computing, and high-speed optical interconnects all together on the same chip.
A Suggestion on the LDA-Based Topic Modeling Technique Based on ElasticSearch for Indexing Academic Research Results

Most academic researchers use the academic information system when they want to write a reference, such as a related research for a paper. Specific classification rules are applied based on vast amounts of data and the latest references to classify and search keywords. Meta information is designed for specific classification rules and search results are restructured. The search results can be classified and rearranged to suit academic research paper keywords by applying the restructured classification system and the LDA-based topic modeling technique. To implement this, the ElasticSearch classification method and topic-based LDA model were applied to extract the characteristics of academic papers in this study. Stable topics that could detect topic estimation and keyword search results within the minimum time were extracted to classify the paper search results. In addition, by analyzing the distribution of document weight among topics, the system performance was proven to be excellent.
CASE-SSE: Context-Aware Semantically Extensible Searchable Symmetric Encryption for Encrypted Cloud Data

Traditional searchable symmetric encryption (SSE) schemes rarely support context-aware semantic extension, and then lead to the searched results being incomplete or deviating from the user’s query intention. To address this problem, a new context-aware semantically extensible searchable symmetric encryption based on Word2vec model (CASE-SSE) is proposed to achieve context-aware semantic extension in this article. The proposed scheme utilizes outsourced datasets as corpora to extract all keywords for training the Word2vec model, and the trained results is the ontology knowledge base that can be used to extend the semantics of query keywords directly. Further, to facilitate multi-keyword search using the extended query vector, we use the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means clustering algorithm to classify outsourced datasets. We then construct an AVL-tree index and an inverted index based on the classified results, thereby achieving efficient context-aware semantically extensible SSE. The security analysis indicates it is secure and effective. The experimental results show that our scheme is superior in both efficiency and accuracy.
Criminal markets and networks in Cyberspace

Abstract This is an introduction to the special issue of Trends in Organized Crime on ‘criminal markets and networks in cyberspace’. All the contributions to this special issue, even if from different standpoints and focuses, help us understand how cyberspace is (re)shaping offenses and offenders.
A Survey on GAN-Based Data Augmentation for Hand Pose Estimation Problem

Deep learning solutions for hand pose estimation are now very reliant on comprehensive datasets covering diverse camera perspectives, lighting conditions, shapes, and pose variations. While acquiring such datasets is a challenging task, several studies circumvent this problem by exploiting synthetic data, but this does not guarantee that they will work well in real situations mainly due to the gap between the distribution of synthetic and real data. One recent popular solution to the domain shift problem is learning the mapping function between different domains through generative adversarial networks. In this study, we present a comprehensive study on effective hand pose estimation approaches, which are comprised of the leveraged generative adversarial network (GAN), providing a comprehensive training dataset with different modalities. Benefiting from GAN, these algorithms can augment data to a variety of hand shapes and poses where data manipulation is intuitively controlled and greatly realistic. Next, we present related hand pose datasets and performance comparison of some of these methods for the hand pose estimation problem. The quantitative and qualitative results indicate that the state-of-the-art hand pose estimators can be greatly improved with the aid of the training data generated by these GAN-based data augmentation methods. These methods are able to beat the baseline approaches with better visual quality and higher values in most of the metrics (PCK and ME) on both the STB and NYU datasets. Finally, in conclusion, the limitation of the current methods and future directions are discussed.
Autonomous dynamic docking of UAV based on UWB-vision in GPS-denied environment

This paper studies the autonomous docking between an Unmanned Aerial Vehicle (UAV) and a Mobile Platform (MP) based on UWB and vision sensors. To solve this problem, an integrated estimation and control scheme is proposed, which is divided into three phases: hovering, approaching and landing. In the hovering phase, the velocity of the MP and relative position between the MP and UAV are estimated by using geometric tools and Cayley-Menger determinant based on ultra-wideband distance measurements; in the approaching phase, a recursive least squares optimization algorithm with a forgetting factor is proposed, which uses distance, displacement and MP’s velocity to estimate the relative position between the UAV and MP. With the estimated relative position, UAV can approach MP until reaching a distance such that MP is within the field of view of UAV; in the landing phase, the UWB measurement value and visual perception attitude are integrated with the UAV on-board navigation sensor of the UAV to perform the precision landing. Simulation and experiment results verify the effectiveness and feasibility of the proposed integrated navigation scheme.
Investment models on centralized and decentralized cryptocurrency markets

Purpose. Significant capital inflows in the cryptocurrency market and record-breaking prices on cryptocurrency assets have led to the creation of alternative investment options on cryptocurrency markets, including a new field of decentralized investing, known as decentralized finance, operating on smart contracts. The objective of this study is to review investment options in the industry sector available to investors on cryptocurrency markets and decentralized protocols. Methodology. The model of decentralized cryptocurrency exchanges was used in the article. It is based on providing liquidity into the liquidity pool. Findings. The results of this study demonstrate that new industrial cryptocurrency investors have a wide range of investment options that can outperform strategies like passive holding of cryptocurrency or investing in the stock. Given the liquidity mining model attracts early investors, they need to look at assets such as governance tokens of different platforms. The Sharpe ratio of COMP and UNI tokens is higher than S&amp;P500. In addition, these tokens are mined via a liquidity mining model. Originality. The crypto market has been growing rapidly since the beginning of the pandemic. The calculations for crypto assets might be influenced by the bull run on the crypto market because the last time such high Sharpe ratio for BTC and ETH was observed during the 20172018 cryptocurrency bubble. Investing in the crypto market is riskier than investing in the stock market due to high operational risks. Crypto market investors might prefer to mine or buy UNI or COMP tokens to diversify their portfolios. Practical value. According to the analysis results of the received information, a Sharpe ratio of investments in protocols for loanable funds is lower compared to investment options on the stock market or CeFi lending. It is also potentially riskier due to volatile interest rates and high operational risks.
Development of NCL equivalent serial and parallel python routines for meteorological data analysis

The NCAR Command Language (NCL) is a popular scripting language used in the geoscience community for weather data analysis and visualization. Hundreds of years of data are analyzed daily using NCL to make accurate weather predictions. However, due to its sequential nature of execution, it cannot properly utilize the parallel processing power provided by High-Performance Computing systems (HPCs). Until now very few techniques have been developed to make use of the multi-core functionality of modern HPC systems on these functions. In the recent trend, open-source languages are becoming highly popular because they support major functionalities required for data analysis and parallel computing. Hence, developers of NCL have decided to adopt Python as the future scripting language for analysis and visualization and to enable the geosciences community to play an active role in its development and support. This study focuses on developing some of the widely used NCL routines in Python. To deal with the analysis of large datasets, parallel versions of these routines are developed to work within a single node and make use of multi-core CPUs to achieve parallelism. Results show high accuracy between NCL and Python outputs and the parallel versions provided good scaling compared to their sequential counterparts.
Bridge Deterioration Knowledge Ontology for Supporting Bridge Document Analytics

Bridge owners possess important data sources, such as bridge construction records and inspection and maintenance reports, which hold great promise for improving understanding of bridge deterioration and informing maintenance decision making. However, the valuable data buried in these documents are not being fully exploited due to their unstructured nature. Domain-specific semantics are needed to facilitate analysis of the data based on content and domain-specific meaning and to bridge terminology gaps across different sources. To address this need, this paper proposes a bridge ontology (BridgeOnto) that captures deterioration knowledge and semantics related to bridge elements, deficiencies, deficiency causes, and maintenance actions. The proposed ontology was evaluated through answering competency questions, automated consistency and redundancy checking, expert interviews, and application-oriented validation. The BridgeOnto was implemented in supporting automated extraction of information describing bridge conditions and maintenance actions from bridge inspection reports. The experimental results show that the ontology was able to improve information extraction precision, recall, and F-1 measure by 11.7%, 12.4%, and 12.0%, on average. This research contributes to the body of knowledge by offering an ontology that can capture the key deterioration knowledge areas with sufficient coverage and in-depth classification for achieving adequate support for bridge document analytics. By allowing better access to and use of crucial textual data, this research has the potential to improve the understanding of bridge deterioration and enhance maintenance decisions.
An Attention-Based Multi-Representational Fusion Method for Social-Media-Based Text Classification

There exist various text-classification tasks using user-generated contents (UGC) on social media in the big data era. In view of advantages and disadvantages of feature-engineering-based machine-learning models and deep-learning models, we argue that fusing handcrafted-text representation via feature engineering and data-driven deep-text representations extracted by performing deep-learning methods is conducive to enhancing text-classification capability. Given the characteristics of different deep neural networks, their complementary effect needs to be investigated. Moreover, contributions of these representations need to be adaptively learned when it comes to addressing different tasks or predicting different samples. Therefore, in this paper, we propose a novel fused deep-neural-network architecture with a hierarchical attention mechanism for text classification with social media data. Specifically, in the context that handcraft features are available, we employ the attention mechanism to adaptively fuse totally data-driven-text representation and handcrafted representation. For the generation of the data-driven-text representation, we propose a data-driven encoder that fuses text representations derived from three deep-learning methods with the attention mechanism, to adaptively select discriminative representation and explore their complementary effect. To verify the effectiveness of our approach, we performed two text-classification tasks, i.e., identifying adverse drug reaction (ADR)-relevant tweets from social media and identifying comparative-relevant reviews from an E-commerce platform. Experimental results demonstrate that our approach outperforms other baselines.
Multi-Unit Serial Polynomial Multiplier to Accelerate NTRU-Based Cryptographic Schemes in IoT Embedded Systems

Concern for the security of embedded systems that implement IoT devices has become a crucial issue, as these devices today support an increasing number of applications and services that store and exchange information whose integrity, privacy, and authenticity must be adequately guaranteed. Modern lattice-based cryptographic schemes have proven to be a good alternative, both to face the security threats that arise as a consequence of the development of quantum computing and to allow efficient implementations of cryptographic primitives in resource-limited embedded systems, such as those used in consumer and industrial applications of the IoT. This article describes the hardware implementation of parameterized multi-unit serial polynomial multipliers to speed up time-consuming operations in NTRU-based cryptographic schemes. The flexibility in selecting the design parameters and the interconnection protocol with a general-purpose processor allow them to be applied both to the standardized variants of NTRU and to the new proposals that are being considered in the post-quantum contest currently held by the National Institute of Standards and Technology, as well as to obtain an adequate cost/performance/security-level trade-off for a target application. The designs are provided as AXI4 bus-compliant intellectual property modules that can be easily incorporated into embedded systems developed with the Vivado design tools. The work provides an extensive set of implementation and characterization results in devices of the Xilinx Zynq-7000 and Zynq UltraScale+ families for the different sets of parameters defined in the NTRUEncrypt standard. It also includes details of their plug and play inclusion as hardware accelerators in the C implementation of this public-key encryption scheme codified in the LibNTRU library, showing that acceleration factors of up to 3.1 are achieved when compared to pure software implementations running on the processing systems included in the programmable devices.